Podcasts about series b

What if the job you almost didn't get became the foundation for building two public companies? That's exactly what happened to Babu Sivadasan, who was rejected by nine firms before landing his first job out of college, a job he still calls the best deal he ever made. Babu is a serial entrepreneur and engineer who co-founded stamps.com and later co-founded Envestnet, two category-defining platforms in commerce and wealth management technology. He's now the founder of Jiffy AI, applying artificial intelligence to how software gets created and deployed. In this episode, Babu traces a 1990s patent that became stamps.com, a demo computer damaged in his suitcase before a make-or-break Postal Service pitch, and an accidental pivot into wealth management that led to Envestnet. He also shares why he believes natural language will replace traditional programming, and how that belief now drives Jiffy AI. Whether you're weighing whether to raise capital or curious how AI is reshaping wealth management technology, this conversation offers a rare, three-decade view of how founders build through very different technology eras. FOR MORE ON BABU SIVADASAN: https://www.jiffy.ai FOR MORE ON COREY KUPFER: https://www.linkedin.com/in/coreykupfer/ https://www.coreykupfer.com/ Corey Kupfer is an expert strategist, negotiator, and dealmaker. He has more than 35 years of professional deal-making and negotiating experience. Corey is a successful entrepreneur, attorney, consultant, author, and professional speaker. He is deeply passionate about deal-driven growth. He is also the creator and host of the DealQuest Podcast. Get deal-ready with the DealQuest Podcast with Corey Kupfer, where like-minded entrepreneurs and business leaders converge, share insights and challenges, and success stories. Equip yourself with the tools, resources, and support necessary to navigate the complex yet rewarding world of dealmaking. Dive into the world of deal-driven growth today! Episode Highlights with Timestamps [02:20] - Rejected by nine firms before landing his first job [05:17] - The 1990s patent that became stamps.com [13:19] - A damaged demo computer and a make-or-break Postal Service pitch[25:15] - The accidental pivot into wealth management [32:41] - Unifying a fragmented advisor tech stack at Envestnet [54:06] - Jiffy AI today: Series B funded, $61 million raised Guest Bio Babu Sivadasan is a serial entrepreneur with deep experience building and scaling public companies backed by a strong engineering foundation. He co-founded stamps.com and later co-founded Envestnet, helping shape category-defining platforms in commerce and financial services. He is now the founder of Jiffy AI, applying natural language AI technology to how software is created and deployed for the wealth management industry. Babu is also an active angel investor and mentor, supporting startups across Silicon Valley and India. Related Episodes Episode 328 - Richard Manders: a fellow engineer-turned-founder whose curiosity about how things work led him from an early career in automation to building and scaling multiple companies with private equity backing. Episode 370 - Gerry Hays: explores how collapsing startup costs and artificial intelligence are reshaping who can raise capital and become a founder, a theme that runs through Babu's own move from the cloud era into the AI era. Episode 350 - Tom Dillon: examines when founders should look beyond venture capital for funding, a question Babu faced directly in the cash-strapped early days of stamps.com.

Recorded live at Seattle Tech Week, this episode of Founded & Funded brings together two people who track the AI capital markets from different vantage points:  ⁨Pitchbook⁩  EVP of Research & Market Intelligence Nizar Tarhuni, and Madrona Partner Sabrina Albert. These two dig into where AI venture capital is actually landing across the model, infrastructure, and application layers, why AI "harnesses" are becoming a winner-takes-most category, and where vertical AI applications may hold an edge that horizontal platforms can't match. They also cover PitchBook's VC Exit Predictor, the new normal for AI seed valuations, and the tranche-financing structures Nizar says are red flags for founders. And in a live Q&A, they take on a frequently asked question in the AI market: Is this a bubble? Nizar explains why he thinks it's still too early to call, what the data says about the value already being created, and why the next few stages of company growth will tell us much more about which valuations prove durable. For founders raising capital right now, or operators trying to figure out where durable value is emerging in AI, this conversation offers a data-grounded look at what's happening beneath the headline numbers. Full Transcript: https://www.madrona.com/is-ai-a-bubble-what-pitchbook-nizar-tarhuni-sees-in-the-data   Chapters:  00:00 — Introduction 04:11 — The Rise of AI Harnesses 06:50 — Vertical vs Horizontal AI Applications 07:51 — Vertical AI Use Cases: Legal, Finance, Healthcare 09:25 — Are Vertical AI Companies Defensible vs OpenAI/Anthropic? 11:32 — Durability, Switching Costs & the Exit Predictor 14:44 — Open Source Models & Cost Management 18:58 — Choosing the Right Model for the Task 20:09 — Funding Environment: Seed to Series B 23:04 — Tranche Financing & Founder Red Flags 26:43 — Audience Q&A: Seattle's Role in AI, Bubble Risk & Services as Software

Good morning from Pharma Daily: the podcast that brings you the most important developments in the pharmaceutical and biotech world. Today, we're diving into a series of compelling stories that underscore the dynamic shifts and groundbreaking progress within the industry. Recently, Akeso's ivonescimab received a landmark approval for first-line treatment of non-small cell lung cancer (NSCLC) in China. This marks a significant shift in China's regulatory environment, which is increasingly fostering innovative treatment options. Ivonescimab's approval not only enhances Akeso's standing in oncology but also provides new hope for NSCLC patients, a cancer type notoriously difficult to treat. In the arena of mergers and acquisitions, Alfasigma has strategically expanded its reach by acquiring key assets from Nordic Pharma. This acquisition opens doors to new therapeutic areas like arthritis, women's health, and critical care—an astute move to diversify offerings and enhance global market presence. Such strategic expansions are essential for companies aiming to maintain competitive edge and explore untapped markets. Eli Lilly has taken legal action against six U.S. companies involved in unauthorized sales of its experimental weight-loss drug, retatrutide. This highlights ongoing challenges in intellectual property protection, emphasizing the balance between innovation safeguarding and market expansion. The legal actions are part of broader efforts to preserve public safety and ensure proper distribution channels for groundbreaking therapies. On the quality control front, Fresenius faced a setback with the recall of a batch of Actemra biosimilars due to glass particle contamination concerns. This incident serves as a stark reminder of the critical role rigorous quality assurance plays in pharmaceutical manufacturing, especially as biosimilars gain traction for their cost-effectiveness and accessibility. In an intriguing blend of technology and healthcare, Samsung Electronics has secured FDA clearance for its Galaxy Buds earphones to function as over-the-counter hearing aids. This development illustrates the increasing convergence of consumer electronics with medical devices, offering innovative solutions to enhance accessibility in hearing health. Turning to psychedelic therapies, Definium Therapeutics reported significant success in its Phase 3 trials for an LSD-based formulation targeting generalized anxiety disorder. Building on earlier successes in depression studies, Definium is paving the way for psychedelic compounds in mainstream medicine. The results not only bolster confidence in such therapies but also signal a potential paradigm shift in treating mental health disorders. Moreover, Oracle Health has introduced an upgraded patient portal featuring an AI assistant designed to simplify medical records management and appointment scheduling. This advancement is part of a larger trend towards integrating artificial intelligence into healthcare systems to improve patient engagement and streamline processes, ultimately enhancing healthcare delivery efficiency. BridgeBio's ATTR stabilizer Attruby achieved impressive quarterly sales of $222 million, nearing blockbuster status as it captures significant market share in treating transthyretin amyloidosis (ATTR). This success underscores the growing importance of small molecule therapies in addressing complex cardiovascular conditions and reflects an industry trend towards stabilizer-first markets. InduPro has successfully raised $77 million in Series B funding supported by industry leaders like Sanofi and Lilly. The investment is set to propel their cancer pipeline forward with a focus on induced proximity and bispecific antibody platforms—innovations poised to revolutionize oncology treatment by enhancing drug specificity and efficacy. On the regulatory front, Chiesi UK's delayed-release mercaptamine received NHS endorsement for cystinosis treatment—an advancement that expands therapeutic options for patients requiring cystine-depleting therapy. Such regulatory support is crucial for facilitating access to innovative treatments for rare diseases. Amidst these developments, Gilead Sciences licensed MacroGenics' bispecific antibody program—a strategic move to bolster its oncology portfolio through milestone payments and royalties. This reflects a broader industry pattern where larger pharmaceutical companies seek pipeline diversification through strategic partnerships and acquisitions of innovative technologies. In vaccine development news, StablePharma's SPVX02 tetanus-diphtheria vaccine achieved promising Phase 1 results without refrigeration requirements—an innovation that could significantly enhance global vaccination efforts by overcoming cold chain distribution challenges. Finally, Insilico Medicine stands at the forefront of AI integration into drug discovery processes under CEO Alex Zhavoronkov's leadership. By promoting a 'fail-fast' approach at the Bio International Convention, Insilico aims to accelerate drug development timelines while reducing costs—a strategy indicative of biopharma's evolution towards more data-driven decision-making processes. These stories collectively highlight an era marked by rapid innovation within pharmaceuticals and biotech sectors. As companies navigate these complexities through scientific advancements and strategic maneuvers alike—there lies immense potential not only for enhancing patient care but also driving economic growth across global healthcare landscapes.Support the show

Good morning from Pharma Daily: the podcast that brings you the most important developments in the pharmaceutical and biotech world. Today, we delve into the dynamic landscape of drug manufacturing, regulatory affairs, and scientific breakthroughs that are shaping the future of patient care and therapeutic innovation. Bristol Myers Squibb has made headlines with its substantial $2.3 billion investment in a new manufacturing campus in Houston. This strategic move aims to significantly bolster their capabilities across various modalities including small molecules, biologics, and antibody-drug conjugates. The establishment of this facility is a testament to the growing demand for advanced manufacturing infrastructure needed to support complex therapeutics production. As BMS strengthens its operational capacity, it positions itself to better meet the increasing global demand for innovative treatments. In parallel developments, Jazz Pharmaceuticals is expanding its rare epilepsy pipeline through a $1.3 billion acquisition of Actio Biosciences. This strategic acquisition aligns with Jazz's focus on rare diseases with significant unmet medical needs. By integrating Actio's novel small molecule approach, Jazz aims to leverage these innovative therapies to improve patient outcomes in the rare epilepsy market, highlighting a critical shift towards targeted treatments. Vaderis Therapeutics has successfully raised $152 million in Series B funding, aimed at advancing its AKT-targeting treatment for hereditary hemorrhagic telangiectasia (HHT). This funding will support clinical development efforts, offering hope for patients with this rare vascular disorder who currently have limited therapeutic options. The advancement of such targeted therapies underscores the growing focus on addressing rare diseases within the biotech sector. Clinical trial advancements remain pivotal, with MoonLake Immunotherapeutics reporting positive Phase 3 data for sonelokimab, an IL-17A/F inhibitor targeting psoriatic arthritis. This nanobody-based antibody offers a novel mechanism of action by modulating autoimmune pathways involved in psoriatic arthritis, demonstrating the potential of biologics in managing complex autoimmune conditions. In a transformative approach to drug discovery, Aureka Biotechnologies has raised $100 million in Series B funding to further its platform-enabled efforts in antibody and protein design using AI and machine learning technologies. This integration represents a significant step forward, enhancing efficiency and precision in identifying viable therapeutic candidates. Despite these advancements, regulatory challenges persist. The FDA recently rejected ITM Isotope Technologies' application for 177Lu-edotreotide due to manufacturing concerns related to gastroenteropancreatic neuroendocrine tumors. Such setbacks underscore the critical importance of robust manufacturing practices and regulatory compliance to ensure patient safety and product efficacy. BridgeBio and Alnylam Pharmaceuticals are engaged in a competitive race targeting first-line ATTR-CM patients with differing therapeutic approaches—“silencer” versus “stabilizer” therapies. This debate highlights the intricacies involved in treating transthyretin amyloidosis (ATTR), a condition affecting the heart and nervous system, pointing towards potential breakthrough treatments that could significantly improve patient outcomes. On the regulatory front, President Donald Trump has signed an executive order aimed at revising pediatric vaccine recommendations, reflecting a shift in public health policy that could affect vaccination rates and parental decision-making regarding childhood immunizations. WuXi AppTec recently secured a temporary victory in its legal battle with the U.S. Department of Defense regarding its classification as a Chinese military company. This case highlights the geopolitical complexities faced by global pharmaceutical companies operating in sensitive markets. Meanwhile, technological innovation continues to shape healthcare delivery, exemplified by Abbott's partnership with Google Health to develop an AI-powered health insights application. This collaboration is part of a growing trend toward integrating digital technologies into healthcare to enhance diagnostics and personalized medicine. Finally, strategic corporate restructuring is evident as Aura Biosciences refocuses its R&D efforts on ocular oncology under new leadership. Such moves reflect broader trends within biotech firms to streamline operations and concentrate resources on high-potential therapeutic areas. Overall, these developments illustrate a vibrant pharmaceutical and biotech landscape characterized by scientific innovation, strategic alliances, regulatory shifts, and technological integration—all aimed at advancing patient care and therapeutic efficacy in an increasingly complex global environment. As companies continue to navigate these challenges and opportunities, they are set to reshape the future of drug development with significant implications for patient outcomes worldwide.Support the show

Good morning from Pharma Daily: the podcast that brings you the most important developments in the pharmaceutical and biotech world. Today, we delve into the intricate web of scientific advancements, regulatory updates, and strategic maneuvers shaping the industry. Let's begin with Eli Lilly's impressive financial performance, clocking a record-breaking $23 billion in revenue for Q2 2023. This achievement, driven by the success of Mounjaro and Zepbound in metabolic diseases, underscores the competitive landscape of therapies targeting chronic conditions. However, the slower launch of Foundayo highlights ongoing challenges. Meanwhile, competition in obesity treatments is intensifying as Novo Nordisk's Wegovy continues to dominate despite new entrants like Eli Lilly's Foundayo. This scenario reflects growing patient preference for oral medications over injectables. Gilead Sciences' HIV prevention franchise has exceeded $1 billion in quarterly sales, reflecting an increasing reliance on effective small molecule therapies for infectious diseases. Gilead also remains a focal point with its HIV pre-exposure prophylaxis franchise reaching sales milestones amid legal victories defending their tenofovir-based treatments. These legal precedents could influence future innovation policies across the industry. Artificial intelligence continues to make waves in drug discovery and clinical trials. Icon's partnership with Anthropic aims to integrate AI capabilities to accelerate drug development timelines. Similarly, Phylo and Chugai Pharmaceutical are collaborating to optimize drug discovery workflows using advanced AI platforms. These collaborations highlight a strategic move towards enhancing efficiency in drug development processes. In oncology, Amplia Therapeutics and Eli Lilly are evaluating a combination therapy for non-small cell lung cancer, while Evexta Bio and Roche are exploring a joint study for metastatic breast cancer. These partnerships underscore the industry's focus on combination therapies and precision medicine to improve cancer treatment outcomes. Additionally, AstraZeneca's partnership with CSPC Pharmaceutical to enhance biologics manufacturing in China signifies the importance of localized production capabilities. On the regulatory front, Arrowhead Pharmaceuticals' acquisition of an FDA rare pediatric disease priority review voucher for Plozasiran represents a commitment to addressing unmet needs in rare diseases. This voucher could expedite new drug applications, potentially bringing life-saving treatments to patients more quickly. Despite these advancements, challenges persist. Novo Nordisk's Cagrisema failed to achieve its primary endpoint in a Phase 3 trial for type 2 diabetes, highlighting the complexities of developing combination therapies for metabolic disorders. Similarly, Eli Lilly's discontinuation of its Phase 1/2 trial for GBA1 gene therapy reflects the hurdles faced in advancing gene therapies. Looking at industry dynamics, Amgen's decision to discontinue its early-stage obesity drug signifies a strategic shift towards focusing on more promising pipeline candidates like Maritide. This move aligns with broader trends prioritizing candidates with significant potential impact on patient care. Amgen's 'Repatha' is experiencing renewed interest due to positive cardiovascular risk reduction data, showcasing how robust clinical results can rejuvenate existing products. In regulatory news, Merck's 'Lipfendra' has been spotlighted by the FDA as part of its Complex New Product Validation pilot program. This positions Lipfendra as a potential game-changer in the PCSK9 inhibitor category. Lastly, financial maneuvers such as Attovia Therapeutics' $289 million IPO and Expedition Therapeutics' $115 million Series B funding highlight investor confidence in novel therapeutic areas like dermatological and respiratory treatments. The pharmaceutical landscape remains dynamic with Johnson & Johnson investing heavily in gene therapy ventures like Sail Biomedicines for CAR-T therapies. Meanwhile, regulatory challenges persist as seen with Capricor's cell therapy setback due to statistical complexities during FDA reviews. Despite setbacks faced by some companies, innovations continue to thrive. The launch of personalized genetic therapy centers signals a shift towards individualized medicine while research into safer CAR-T therapies progresses. In conclusion, these developments reflect an industry driven by innovation amidst regulatory challenges and strategic adjustments. As companies navigate this evolving landscape, their decisions will shape future healthcare innovations and patient care outcomes. Thank you for joining us on Pharma Daily; stay tuned for more insights from the world of pharmaceuticals and biotech.Support the show

-WindBorne Systems has raised a $37 million Series B round to scale its weather balloons and AI forecasts. -Hark claims that its browser use agent is faster and cheaper than competition. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Revival Gold reported one of the strongest grade-thickness intercepts yet from the Joss area at its Beartrack-Arnett Gold Project in Idaho. Ridgeline Minerals sold its interests in four Nevada gold projects to Nevada Gold Mines for about 33 million Canadian dollars in cash. Collective Mining announced a partnership to develop a community-scale solar project for the Guamal Afro-Colombian community in Supía, Caldas. Mariana Minerals announced a $310 million Series B financing led by Khosla Ventures, bringing total parent and project capital raised to about $400 million. Talon Metals reported new high-grade nickel-copper assays from the Vault Zone at its Tamarack Project in central Minnesota. Southern Cross Gold updated the Exploration Target for its Sunday Creek gold-antimony project in Victoria, Australia. FireFly Metals reported another batch of high-grade drill results from its Green Bay Copper-Gold Project in Newfoundland. Lumina Metals provided an update on 2026 drilling across its three copper-silver projects in western Poland.The Mining Stock Daily morning briefing is produced by Clear Commodity Network. It is distributed throughout the world through your podcast network of choice, and at Clear Commodity Network.Integra is a growing precious metals producer in the Great Basin of the Western United States. Integra is focused on demonstrating profitability and operational excellence at its principal operating asset, the Florida Canyon Mine, located in Nevada. In addition, Integra is committed to advancing its flagship development-stage heap leach projects: the past producing DeLamar Project located in southwestern Idaho, and the Nevada North Project located in western Nevada. Learn more about the business and their high industry standards over at integraresources.comVizsla Silver is advancing the Panuco silver-gold project in Sinaloa, Mexico — one of the highest-grade silver development projects in the world. The project hosts the world's largest, undeveloped high-grade silver resource, and the company has been aggressively expanding its drill program to grow that resource ahead of a development decision. Learn more at vizslasilver.ca.Equinox Gold is a growth-focused gold producer operating mines across the Americas. With cornerstone assets like the Greenstone Mine in Ontario and the Valentine Gold Project in Newfoundland & Labrador, Equinox is advancing a new generation of large-scale, long-life gold operations. Learn more about their portfolio and development pipeline at equinoxgold.com.Revival Gold is one of the largest pure gold mine developer operating in the United States. The Company is advancing the Mercur Gold Project in Utah and mine permitting preparations and ongoing exploration at the Beartrack-Arnett Gold Project located in Idaho. Revival Gold is listed on the TSX Venture Exchange under the ticker symbol “RVG” and trades on the OTCQX Market under the ticker symbol “RVLGF”. Learn more about the company at revival-dash-gold.com

Send us Fan MailMr. Dempsey brings more than two decades of domestic and global experience in the ophthalmic space, driving successful drug development, business transactions, and commercialization. Mr. Robert's CEO and Board Director experience spans extensive transaction and portfolio expertise across venture capital, investment banking, and strategies to successfully secure funding and advance M&A opportunities for multiple companies. His professional network in the eye care community is broad and well-established, complemented by his philanthropic leadership as Co-Founder and Board Member of the Holland Foundation for Sight Restoration. Robert also serves as an independent director on multiple ophthalmicBoards.Mr. Dempsey currently serves as Chief Executive Officer of OKYO Pharma, a public, clinical-stage biopharmaceutical company developing investigational therapies for neuropathic corneal pain (NCP) and inflammatory eye diseases. The company's lead candidate, urcosimod, has received FDA Fast Track designation for treating NCP. Previously, Mr. Dempsey served as Interim Chief Executive Officer of Ashvattha Therapeutics, where he led development of Migaldendranib (MGB), a novel targeted nanomedicine platform designed to normalize VEGF expression for the treatment of diabetic macular edema (DME) and neovascular age-related macular degeneration (nAMD). Earlier in his career, Mr. Dempsey served as President and Chief Executive Officer of AsclepiX Therapeutics, where he oversaw the rapid clinical development of a new chemical entity targeting ocular diseases and cancer through naturally occurring, self-regulating homeostatic pathways. Prior to that, he served as Chief Executive Officer of TearClear, where he accelerated the company's business and commercial strategy, leading to multiple value-inflection points, including a successful Series B financing and positive FDA engagements.Mr. Dempsey formerly served as the Group Vice President and Head of Global Ophthalmology at Shire, which was acquired by Takeda, and was responsible for closing one of only 3 ophthalmology transactions with >$1B upfront in the last 2 decades for Xiidra that transacted again 4 years later for $1.7B to Bausch & Lomb.At Shire, he swiftly established a highly regarded reputation for Shire as a new company in the ophthalmology and optometry markets. Leading the commercialization strategy that resulted in an industry-leading launch, while overseeing the acquisition of four companies that contributed to the franchise pipeline of innovative candidates aimed at improving vision-related quality, was anaccomplishment about which he is most proud.In his spare time, he enjoys spending time with family on the lake and watching Boston sports.Here's another episode about neuropathic pain on The OI Show: https://theoishow.buzzsprout.com/1601521/episodes/15618641-167-the-oi-show-neuropathic-pain-with-kaleb-abbott

(04:48) Brought to you by SpeechifyAITired of text-to-speech that sounds robotic or costs too much at scale? SpeechifyAI's new Simba 3.2 model ranks #1 on Artificial Analysis for realness, priced under $10 per million characters. Building something conversational? Check out the Simba Voice Agents API for low-latency, natural back-and-forth interaction. Try it free at speechify.ai.Why do the same software delivery illusions keep fooling smart engineering teams? Elisabeth and Joel show how systems thinking, through signals and levers, helps you spot the illusions of progress, predictability, and control before they cost you.In this episode, Elisabeth Hendrickson and Joel Tosi, co-authors of Signals & Levers, share the story behind the book and trace the “software crisis” back to a 1968 NATO conference, arguing it never actually went away. They explain why software delivery is an adaptive sociotechnical system, and why treating it as a simple linear process leads leaders to pull the wrong levers.Elisabeth breaks down why proxy metrics like velocity are made-up numbers dressed up as science, and why cycle time tells a truer story. Joel walks through the CREATE framework (capacity, risk, execution, adaptability, trust, and economics), and how it can help leaders spot unintended consequences before they happen. They also dig into the three illusions leaders live under: illusion of progress, predictability, and control.The conversation closes with a candid look at where AI fits into all of this, when it amplifies good systems, when it makes bad ones worse, and why optimizing for learning matters more than ever.Timestamps:(00:00:00) Trailer & Intro(00:02:34) The Backstory Behind “Signals & Levers”(00:05:55) Why Has the ‘Software Crisis' Never Actually Gone Away?(00:08:41) Why Do the Same Software Development Problems Keep Appearing?(00:10:55) What is an Adaptive Sociotechnical System?(00:15:09) Why Do Business Executives Fail to Understand Software Development?(00:20:03) What Are Signals and Levers in Engineering Leadership?(00:24:31) What Makes Proxy Metrics Like Velocity and Lines of Code Dangerous?(00:28:23) Are DORA Metrics a Good Proxy for Software Development Productivity?(00:32:50) What is the CREATE Framework for Avoiding Unintended Consequences?(00:38:26) How Do You Quantify and Apply the CREATE Framework?(00:44:09) What Are the Three Illusions That Leaders Face in Software Delivery?(00:50:27) Will AI Truly Speed Up Software Development?(00:55:55) How Should Leaders Integrate AI Into Systems Thinking?(01:00:53) Can AI Be a Thinking Partner for Systems Thinking?(01:04:26) Why Is the U-Curve a Powerful Tool for Modern Leadership?(01:09:17) 3 Tech Lead Wisdom_____Elisabeth Hendrickson & Joel Tosi's BioElisabeth Hendrickson is a technology leader with 30+ years of experience, having served as VP R&D at a public company and VP Engineering at a Series B startup. She's the author of Explore It! and There's Always a Duck, and now helps tech leaders improve collaboration, decision-making, and execution.Joel Tosi has spent over 25 years delivering software products. For the past decade, he's helped teams see the systemic issues holding them back and stop “change theater,” using the techniques from Signals & Levers to give everyone a shared view of reality. He's presented these ideas internationally for over five years.Follow Elisabeth & Joel:LinkedIn (Elisabeth) – linkedin.com/in/testobsessedLinkedIn (Joel) – linkedin.com/in/joel-tosi-531a3bWebsite – signalsandlevers.comSignals & Levers – itrevolution.com/product/signals-and-leversSignals & Levers workshops – maven.com/signalsandleversLike this episode?Show notes & transcript: techleadjournal.dev/episodes/265.Follow @techleadjournal on LinkedIn and Instagram.Buy me a coffee or become a patron.

Joon Sung Park is the Founder and CEO of Simile, the AI simulation company building foundation models of human behaviour; allowing companies to test how real people may think, decide and act before making a decision in the real world. Simile has now raised $300 million in total, including a $200 million Series B announced last week at a $2 billion valuation, led by Greenoaks and Index Ventures. AGENDA: 00:00 We Will Pay $100M for a Single Query on Some Models 10:00 Why Stock Markets May Not Exist in 5 Years Time 15:00 The Best Companies All Have Unique Data Acquisition Strategies 19:00 The Best AI Companies Have Clear and Fast Reward Functions 24:00 How We Sign Fortune 500 Companies for $10M Contracts in Weeks 32:00 Does Similie Kill Kalshi and Polymarket? Prediction vs Changing the Future 42:00 Inside Similie's $300M Raise; What Every Founder Needs to Know    

What happens to security investing when vulnerability discovery becomes continuous and exploitation windows shrink from weeks to hours? I sit down with Chenxi Wang of Rain Capital to dig into it.Chenxi is the Founder and Managing General Partner at Rain Capital, a venture fund focused on early-stage cybersecurity companies. She's been a Carnegie Mellon professor, a Forrester VP, and a strategy leader at Intel Security and Twistlock, and her portfolio includes companies like Claroty, ProjectDiscovery, Ox Security, runZero, and Straiker. She closes out my July run of conversations with security investors.In this episode:The AI Exploit Age and why vulnerability discovery is becoming continuousGuardian Agents and the case that it takes an AI to govern an AISeparating AI agent identity from traditional machine identityThe signals that predict enterprise adoption for early-stage security startupsThe barbell funding market and the squeeze on Series B and CWhat security leaders should do differently over the next twelve monthsConnect with Chenxi: LinkedIn: https://www.linkedin.com/in/chenxiwang88/ Rain Capital: https://raincap.vc/ Rain Capital Insights: https://raincapital.substack.comSubscribe to Resilient Cyber for more conversations with security practitioners and leaders: https://www.resilientcyber.io

Good morning from Pharma Daily: the podcast that brings you the most important developments in the pharmaceutical and biotech world. Today, we dive into the latest news shaping these dynamic sectors, exploring strategic corporate maneuvers, groundbreaking scientific advances, and pivotal regulatory changes. GlaxoSmithKline (GSK) is embarking on a comprehensive restructuring initiative aimed at achieving $2.5 billion in annual cost savings by 2029. This ambitious initiative underscores the company's commitment to strengthening its late-stage research and development capabilities. The strategy involves optimizing mature product lines, procurement processes, and supply chain efficiencies, reflecting broader industry trends where financial prudence is balanced with innovation. As part of this transformation, GSK plans to relocate its research headquarters near AstraZeneca's facilities, though specifics on employment impacts remain under wraps. This restructuring highlights the competitive nature of the pharmaceutical landscape and GSK's intent to maintain its edge through enhanced R&D initiatives. Meanwhile, Johnson & Johnson is nearing a resolution in its extensive talc litigation by proposing a $5.5 billion settlement to resolve approximately 76,000 lawsuits. These lawsuits allege that J&J's talc-based baby powder caused ovarian cancer. This potential settlement represents a significant step toward closing a protracted chapter of legal scrutiny for J&J, offering the company a chance to mitigate ongoing legal risks and refocus on core business operations. On the regulatory front, MannKind has secured FDA approval for its fast-acting edema autoinjector, reflecting an ongoing emphasis on addressing unmet medical needs with innovative delivery mechanisms. Conversely, Baxter has issued a recall for one lot of cefazolin in dextrose injection due to contamination concerns, underscoring the continuous challenges of ensuring product safety within complex manufacturing and supply chain environments. In terms of public health initiatives, Gilead Sciences is employing a novel approach to HIV prevention through its "Up to Date" campaign featuring comedians Nicole Byer and Devon Walker. This effort aims to destigmatize HIV discussions within the Black community by leveraging humor and relatability—a testament to evolving strategies in patient engagement and education. In drug development news, Atea Pharmaceuticals is making strides with its hepatitis C treatment candidate following positive Phase 3 trial results showing equivalence with Gilead's Epclusa. This positions Atea for further clinical evaluations and potential market entry with a simplified treatment regimen. Similarly, Hansoh Pharmaceutical's collaboration with GSK has yielded another Phase 3 success for their B7-H3-directed antibody-drug conjugate in China, highlighting the growing importance of targeted therapies in oncology. Emerging biotech hubs are reshaping the industry's landscape as cities beyond traditional centers like Boston and San Francisco gain prominence. This geographical diversification reflects global trends in biotech innovation and investment. In clinical advancements, Altimmune's GLP-1/glucagon receptor-targeting drug shows promise in reducing heavy drinking among individuals with alcohol use disorder (AUD). This finding opens new therapeutic avenues for AUD by leveraging mechanisms traditionally associated with weight loss medications. Concurrently, regulatory scrutiny remains high as Replimune faces setbacks with its melanoma data package deemed "not interpretable" by the FDA—an indication of the rigorous standards required for gaining regulatory approval. The geopolitical landscape also influences industry dynamics, particularly in China where intellectual property risks are heightened under new legislative acts. Companies must navigate these complexities strategically to protect innovations while capitalizing on market opportunities. Financially, Novo Holdings-backed Claris has secured $118 million in Series B funding to advance its corneal disease drug candidate—a substantial investment underscoring growing interest in ophthalmology therapeutics. Additionally, RA Capital has launched Oak Hill Bio onto Nasdaq via a special purpose acquisition company (SPAC), focusing on rare genetic diseases—a strategy showcasing continued momentum within biotech to leverage financial tools for niche therapeutic advancements. Overall, these developments reflect an industry characterized by rapid scientific progress and evolving regulatory landscapes. Companies are under pressure to optimize operations while ensuring robust clinical data to meet regulatory standards. As these sectors strive for innovation and patient care advancements amidst competitive global markets, balancing innovation with safety and efficacy remains paramount. As always, we'll continue to track these stories closely and bring you the latest insights right here on Pharma Daily.Support the show

PODCAST EPISODE | An Analog Brain In A Digital Age With Marco Ciappelli Fifteen years ago, Rose Ross brought a client an idea for an awards program built specifically for enterprise tech startups. The client passed. She built it herself — and the Tech Trailblazers have been running ever since, independent, judged by practitioners, and open for entries until 3 September.

Dr. Thomas Kelly was a vascular surgery trainee in Melbourne before he walked away from medicine to fix the problem he hated most: paperwork stealing time from patients. Today he is co-founder and CEO of Heidi Health, an AI medical scribe and "care partner" used by clinicians in more than 100 countries, valued at US$465M after its 2025 Series B led by Point72. In this episode, Jeremy Au and Dr. Tom break down how GPT-4 nearly destroyed the company, forcing a brutal pivot from an AI model company into a product company, and how that near-death moment led to becoming one of the world's most-used healthcare AI tools. They dig into why great AI scribing is far harder than a demo, how Heidi is expanding into clinical decision support and medical-device territory, why he isn't scared of OpenAI in healthcare, and why Singapore, Japan, and the rest of Asia are core to the roadmap. A must-watch for founders, venture capitalists, and operators building AI, health tech, and vertical software across Southeast Asia. Watch, listen or read the full insight at https://www.bravesea.com/blog/heidi-health-tom-kelly  BRAVE is Southeast Asia's leading tech podcast, hosted by Jeremy Au. Honest conversations with the region's top founders, investors, and operators on building startups in Southeast Asia. New episodes every week. Subscribe so you never miss one. Listen & Subscribe YouTube (English), YouTube (Bahasa Indonesia), Spotify (English), Spotify (Bahasa Indonesia), Spotify (Chinese), Spotify (Vietnamese), Apple Podcasts Follow BRAVE LinkedIn, X (Twitter), Instagram, TikTok, WhatsApp Follow Jeremy Au LinkedIn, X / Twitter, Instagram, TikTok, Facebook, Threads, Twitch Resources Get transcripts, startup resources & community discussions at www.bravesea.com #HealthcareAI #AIScribe #FounderStory #AIinHealthcare #StartupPivot #SoutheastAsia #VentureCapital 00:00 – From vascular surgery to AI: meet Dr. Tom Kelly 01:38 – Why he became a doctor: the "platonic ideal" GP 02:55 – Maths, machine learning, and the pre-med detour 04:22 – GAMSAT YouTube videos to his first business 07:06 – Building Oscer, an early AI clinical tutor 08:58 – Why doctor paperwork is a capacity crisis 13:05 – Is an AI scribe easy or hard to build? 17:35 – From scribe to full AI care partner 18:28 – Clinical AI and medical device regulation 21:32 – Roadmap: tasks, primitives, and on-prem hardware 26:24 – Going global: Singapore, Japan, and Asia 28:50 – Localizing for Malay, Hokkien, and Singlish 32:52 – Why clinicians drive the innovation 34:21 – The GPT-4 pivot that nearly ended Heidi 37:20 – Competing with OpenAI and how to run a pivot

Bloomberg reported that robotics startup Genesis is in talks to raise funding at a valuation of about $3 billion. The report did not disclose the round size or investors. Comparable deals include Figure AI's $675 million raise in 2024 at a reported $2.6 billion valuation backed by Microsoft, the OpenAI Startup Fund, Nvidia, and Jeff Bezos. Norway based 1X closed a $100 million Series B in 2024 led by EQT Ventures, and Agility Robotics raised $150 million in 2022 and later ran pilots with Amazon. Investors are focusing on reliability, safety cases, integration with enterprise systems, and cost per productive hour. Late stage capital is funding engineering, data and compute, supply chain, and field service as robotics companies push toward multi site deployments.Learn more on this news by visiting us at: https://greyjournal.net/news/ Hosted on Acast. See acast.com/privacy for more information.

On this episode of Run the Numbers, CJ sits down with Datadog SVP of FP&A AJ Ljubich to break down the four stages of a world-class finance team.—SPONSORS:Brex is an intelligent finance platform with AI-powered agents that capture expenses automatically, enforce policy before the spend happens, and close your books in minutes instead of weeks. 35,000+ companies like OpenAI, Coinbase, Anthropic, and DoorDash already run on Brex. It's time to get Brex AF. Learn more at https://www.brex.com/metricsAnrok is the sales tax platform that watches your exposure everywhere, automates compliance, and flags risk before it turns into a surprise back-tax letter from a state you've never set foot in. Companies like Anthropic, Notion, and Vanta already trust Anrok to stay ahead of rules that move faster than any spreadsheet can. Talk to a sales tax expert for a personalized exposure estimate at https://www.anrok.com/rtnRightRev is an automated revenue recognition platform that lets your product team ship new pricing without asking finance for permission, and your sales team close deals without creating downstream chaos. Check out their free tool at calculator.rightrev.com It scores your rev rec process, shows what's exposing you to risk, and tells you exactly where to focus before it bites you in the rear end. Check it out at https://calculator.rightrev.comPulley is an equity management platform that lets you issue options, model dilution, and complete 409As without your cap table turning into a spreadsheet disaster. Founders raising, hiring, and scaling use Pulley to keep equity clean and stay focused on building. Learn more or request a demo at https://pulley.com/mostlymetricsRillet is an AI-native ERP built for modern finance teams that want to replace NetSuite and close faster. With revenue recognition, close management, multi-entity support, and native Stripe and Salesforce integrations, Rillet helps scaling companies run their finance stack in one place. Hundreds of teams, including Windsurf and Mercor, use Rillet to make the zero-day close real. Book a demo at https://www.rillet.com/cjMaximor is an autonomous finance platform that runs order-to-cash, procure-to-pay, the close, cash management, and reporting on self-learning agents instead of a dozen disconnected tools. One PE-backed customer cut their close in half, took audit findings from seven to zero, and cut back-office costs by 70% in six months. You pay for outcomes, not seats. See it at https://www.maximor.ai/—LINKS: Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNGuest: https://www.linkedin.com/in/aj-ljubich-cfa-727ab912/Company: https://www.datadoghq.com/CJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—TIMESTAMPS:0:00 Preview and Intro2:42 FP&A as a product team4:53 The five pillars of AJ's team6:39 How far FP&A has come7:33 How do you know you're doing a good job?10:20 FP&A as the dashboard, not the engine10:20 Sponsors — Brex | Anrok | RightRev13:18 The four-stage FP&A maturity model16:00 Can you reach all four stages at Series B?16:45 Why AJ holds weekly forecast meetings18:24 How often to actually change the forecast21:42 Sponsors — Pulley | Rillet | Maximor24:49 Single source of truth: still on the vision board26:12 AI and the self-serve data risk26:21 CAC payback over LTV to CAC29:39 Inside sales vs. enterprise payback comparison31:47 Finance as the yes, but people34:02 FP&A vs. IR: which is harder?36:14 How IR makes you a better FP&A leader37:19 Is the IR profile shifting?42:01 IPOs are branding events, not finish lines43:27 The Datadog IPO during the WeWork meltdown45:15 Career philosophy: do great work where you are49:52 Datadog didn't have a role for AJ when he started52:16 Lightning round52:24 Screwed up: forgot the currency guy54:37 Finance software stack56:16 Advice to younger self57:13 Credits

Matilda Ho was banned from the kitchen for the first 25 years of her life. Her mother's rule was simple: cooking is what you do when you fail at school. She now runs China's first food tech venture fund.I'm joined by Matilda Ho, Founder and Managing Director of Bits x Bites — a $100 million fund vintaged in October 2020, 15 companies backed, Series A and B, and a board seat as a condition of every check. Her LPs are sovereign funds from Singapore, the Middle East and China, agrifood strategics, and family offices.Her argument is that the food system cannot be fixed at the checkout. She spent five years at BCG and IDEO advising food companies, and one project — working out how likely a Chinese meat processor was to have another scandal — turned up roughly half a million food safety incidents a year. She built an online grocery to fix it one shopper at a time, then concluded that would take longer than her lifetime. The leverage was upstream.What she found upstream is a manufacturing advantage most investors outside China have not priced. Seventy percent of the world's vitamins, two-thirds of its amino acids and more than 80% of its stevia are already made there — much of it in brownfield plants with fermentation tanks sitting idle. Where European biotech founders cannot fund scale-up, she can buy it cheap.She is equally blunt about what does not work. Beyond Burger's peas travel from Canada to Suzhou to California, and the margin never survives the trip. China already has tofu — clean, plant-based and 2,000 years old. So she funds certainty over moonshots: functional ingredients, animal health, matcha. And in a market where government money is now the largest source of innovation capital, her first exit was a stake sold to a provincial government vehicle.In this episode we discuss:Why the food system cannot be fixed at the checkout, and what changes upstreamHalf a million food safety incidents a year — the consulting project that exposed themBiomanufacturing as China's unpriced edge: overcapacity, brownfield sites and idle fermentation tanksWhy alternative meat fails on unit economics in a country that already has tofuChina's “visible hand” — how government money became the largest source of innovation capitalSelling a portfolio company to a provincial government vehicle, and why DPI beats IRR in ChinaWhy she will not write a check without a board seatBacking wartime CEOs, and what a decade of bad hires taught her about founder diligenceFeatured guest:Matilda Ho, Founder and Managing Director at Bits x BitesListen Next:AgTech Profits Meet Planet: Where Climate Impact and VC Returns AlignDiscover More from SRI360°:Explore all episodes of the SRI360° PodcastSign up for the free weekly email updateKey Takeaways:Fixing food at the checkout does not scale. Matilda built an online farmers market and learned that people only change how they eat after a life event — a birth, a diagnosis. A movement, she says, but not a viable business model. The leverage sits upstream in the supply chain.The supply chain is the problem. In China a vegetable passes through roughly seven hands before it reaches a table. A third of food rots on the farm, another third in transit, and the rest is wasted in fridges and warehouses.China's arithmetic is brutal. Around 20% of the world's population and under 7% of its arable land, feeding 1.4 billion people. More than 85% of soybeans are imported, mostly to feed chickens and pigs.Biomanufacturing is China's quiet edge. Europe's biotech founders struggle to fund scale-up. China has the opposite problem — overcapacity, brownfield sites and idle fermentation tanks, plus the plant managers who know how to run them. 70% of global vitamins, two-thirds of amino acids and over 80% of stevia are already produced there.Cost is king and taste is king. Beyond Burger's peas grow in Canada, get processed in Suzhou, then travel to California for final formulation. The gross margin never works. And as she puts it, China already has tofu — clean, plant-based, cheap, and 2,000 years old.Government money is now the largest source of innovation capital in China. Local governments run fund-of-fund structures and back specialized GPs rather than investing directly. Her own first exit was selling a stake to a provincial government vehicle at Series B, in year five of the fund.In China, DPI matters more than IRR. IRR can be manipulated; cash returned to LPs cannot. Most domestic funds have only a five-year life, which forces short-term decisions. Her USD fund has ten to twelve years.Back wartime CEOs, not peacetime ones. Growing revenue tenfold when money was free is not a replicable track record. She looks for humility, grit, and founders who keep going when 99% of the signals say stop.Software alone does not work in agriculture. Farmers will not pay for something invisible. Her Beijing crop-model company had to bundle seeds and inputs with the software; two-thirds of its revenue now comes from selling the inputs.Drones changed the economics for smallholders. A tenth of the chemical input, up to half the water saved, and profits up around 30% on cash crops — plus a whole new job class of drone operators. Most growth is now outside China, in North America, Brazil and Argentina.Agrifood is under-invested. It accounts for less than 5% of total venture funding. Her ambition is that generalist fund managers eventually treat it as a sector worth a seat.Additional ResourcesMatilda Ho LinkedIn: https://www.linkedin.com/in/matildaho/Bits x Bites LinkedIn: https://www.linkedin.com/company/bits-x-bitesMatilda Ho on X: @matildajyhoBits x Bites: https://bitsxbites.com/Matilda Ho's TED profile: https://www.ted.com/speakers/matilda_hoRelated SRI360° Episodes:Beyond the 2/20 Model: Disrupting VC & 25% IRR from Climate Adaptation in Southeast Asia

Good morning from Pharma Daily: the podcast that brings you the most important developments in the pharmaceutical and biotech world. Today, we're delving into a series of significant breakthroughs and strategic moves reshaping the industry landscape. Repligen Corporation's recent decision to acquire BioLife Solutions for $1.5 billion marks a pivotal moment in the cell therapy sector. BioLife Solutions' expertise in cryopreservation media is integral to maintaining cell viability during storage and transport—an essential factor for the success of cell therapies in oncology and regenerative medicine. This acquisition ensures Repligen a stable supply chain to support the burgeoning demand for advanced therapeutic options, potentially enhancing patient outcomes through more reliable treatments. Meanwhile, Novartis is expanding its reach in South Korea with a $95 million investment aimed at enhancing its radioligand therapy supply chain. Radioligand therapy represents a groundbreaking approach in oncology, utilizing targeted radioactive particles to specifically attack cancer cells while sparing healthy tissue. This strategic investment underscores Novartis's commitment to ensuring a robust infrastructure for delivering innovative cancer treatments. On the clinical front, Crystalys Therapeutics has made waves by securing $130 million in Series B funding, propelling their gout treatment, Dotinurad, into Phase 3 trials. This drug targets uric acid levels, addressing a significant unmet need in gout management. The progression into late-stage trials reflects confidence in Dotinurad's potential to provide effective relief for patients suffering from this painful condition. Regulatory advancements are also making headlines with the UK's MHRA granting approval to Chiesi's Clenil Modulite with a low-carbon propellant for asthma maintenance treatment. This move signifies a crucial step towards more sustainable pharmaceutical practices without compromising efficacy—setting a potential benchmark for future respiratory treatments that incorporate eco-friendly technologies. In business development news, Ashlins Pharmaceuticals and Lee's Pharmaceutical have struck a $31 million licensing deal for interferon alpha-2b outside China, focusing on rare diseases. Similarly, Jupiter Neurosciences' acquisition of MDMA asset ALA-002 from Pharmala Biotech illustrates ongoing interest in small molecule therapeutics for neurological conditions. Royalty Pharma's significant $425 million payment to Neurimmune highlights strategic investments in cardiovascular therapies through royalty financing, underscoring confidence in advancing novel treatments. Concurrently, Dimension has raised an impressive $800 million for an AI-driven biotech drug discovery fund. The integration of AI promises accelerated drug development timelines and enhanced precision in identifying viable candidates. However, not every development is favorable. Celldex Therapeutics faced a setback with their Phase 2 trial failure for barzolvolimab in prurigo nodularis patients. Such challenges highlight the inherent risks associated with innovative drug development. Turning our attention to another scientific milestone, Arrowhead Pharmaceuticals has reported promising Phase 3 data on its triglyceride-lowering drug, Plozasiran. With analysts praising its "best-in-class profile," Arrowhead aims for FDA approval by year-end, setting up potential competition with Ionis Pharmaceuticals' Tryngolza. These advancements underscore significant progress in targeting specific lipid disorders and the promise of novel therapies to improve cardiovascular health. Corporate strategies are also evolving as seen with Sanofi undergoing executive restructuring under new leadership by CEO Belen Garijo. This reflects broader industry trends where leadership shifts align corporate strategies with market demands and scientific opportunities. Legal developments have not gone unnoticed either. Amgen's $74 million settlement over allegations of concealing a substantial tax bill highlights ongoing scrutiny over corporate governance within the sector. Meanwhile, industry giants are rallying around regulatory defenses, as evidenced by biopharma companies supporting the FDA amidst legal challenges concerning mifepristone—a move emphasizing the industry's vested interest in maintaining access to drugs based on scientific merit. From a technological perspective, companies are increasingly adopting connected trial technologies to streamline processes such as consent and reporting—advancements crucial for reducing patient burden and accelerating drug development timelines. In conclusion, these developments portray an industry at the forefront of scientific innovation while navigating complex regulatory environments and ethical considerations. As firms continue exploring novel treatment avenues and leveraging technological advancements like AI, they position themselves for transformative impacts on patient care and global health outcomes. We look forward to keeping you updated on these exciting trends shaping the future of pharma and biotech industries.Support the show

Dylan Scully is an early-stage investor at Frontline Ventures, a transatlantic venture fund investing in B2B tech and AI across Europe at pre-seed and seed, and in the US at Series B and beyond. Frontline's thesis: to build a category-defining company, you need to win both Europe and the US; it's not an either/or.In this conversation, Dylan draws on his background as a founder (Matchday) and his time at Accenture's global innovation team to give data-backed, practical advice to Irish founders thinking about US expansion. We cover the “SaaSpocalypse” debate, what enterprise buyers actually value, the real cost of entering the US market, and what gets Frontline excited at the pitch table.Show NotesFrontline's dual fund strategy — early stage across Europe, growth stage in the US, and why the transatlantic network mattersThe “SaaSpocalypse” — will enterprises build everything in-house as code costs go to zero? Dylan's clear-eyed view on why most won't, and why AI-native startups beat incumbents bolting on featuresBuild vs. buy in the age of AI — why enterprises buy opinions, reliability, and a product roadmap — not just lines of code; the Donna portfolio example on AI agents sitting in front of systems of recordIs now a bad time to expand to the US? — Frontline ran sentiment analysis via portfolio company Signal AI: for every 1 negative European article about the US, there were 13 positive onesThe 40% rule — European B2B software companies generate ~40% of IPO revenue from the US. Flip side: US software companies generate 30–40% of IPO revenue from Europe. The interdependency is massively underestimated.Exploring the US from day one — treat it as a separate product-market fit exercise; 4–6 trips/year; the Workvivo/Zoom playbook for using European subsidiaries to get warm intros to US HQWhat founders get wrong about US costs — salaries, agencies, visas, travel all add up. But founders equally underestimate the value: one homogenous market, shorter sales cycles, bigger budgets, higher ACVsThe 300% rule — when pitching US VCs, weight your US revenue and users 3× higher than European equivalents. A US investor won't be impressed by your 1,000 European customers if you have 20 in the US.What Frontline looks for — team and market account for 95%+ of the decision. Speed of learning + strong contrarian opinions backed by data are the leading indicators they watch for.Guest BioDylan Scully is an early-stage investor at Frontline Ventures, focused on pre-seed and seed investments in B2B tech, software, and AI across Europe. Before joining Frontline, he founded Matchday (sports fan engagement platform) and spent several years at Accenture as Commercial Strategy Lead for their global innovation team, working with C-suite clients on adopting emerging technology.LinksFrontlineDylan Scully on LinkedInDylan's SubstackFrontline US Playbook Contact — Want to get in touch? Email podcast@digitalirish.com

✨

CuspAI, het Nederlands-Britse AI-bedrijf dat nieuwe materialen ontdekt met hulp van AI, heeft een Series B-financieringsronde van in totaal 450 miljoen dollar behaald. Daar doen naast Jeff Bezos, Nvidia en Meta ook onder meer Invest-NL en het Britse overheids-AI-fonds aan mee. Joe van Burik vertelt erover in deze Tech Update. Verder in deze Tech Update: Google zou een efficiëntere AI-chip hebben ontwikkeld onder de naam Frozen v2, zo meldt The Information, waar aandeelhouders enthousiast op reageren See omnystudio.com/listener for privacy information.

This week's video transcript summary is here. You can click on any bulleted section to see the actual transcript. Thanks to Granola for its software.EditorialIntelligence: Who Owns it?This week the word “AI” feels too small.AI is a technology. Intelligence is its product. And if intelligence is the product, the question is no longer just: Which model is best? Who has the cheapest tokens? Who owns the weights? Who controls the data center? Those are important questions, but they are lower in the stack.The bigger question is simpler and more political:Who owns intelligence?That sounds abstract until you make it concrete. Intelligence is becoming something companies can capture, package, serve, meter, route, improve, and sell.It can write code, answer questions, design molecules, automate offices, run agents, draft legal work, advise scientists, serve consumers, and reshape workflows. It is not merely software. It is a general-purpose capability. And all humans could benefit from more of it.General-purpose capabilities have a habit of becoming public questions. But the default answer, that public good is best delivered by government, is the wrong answer in this context.The Product Is IntelligenceWe should stop talking about AI as a feature and start talking about intelligence as the universal thing that is delivered as an input to the world.Water is an input. Electricity is an input. Literacy is an input. Connectivity is an input. Once a society depends on them, access stops being optional. Nobody needs government to build every well, power plant, school, or network. But everybody understands that a civilization cannot be organized around less than universal and reliable access to foundational inputs.Intelligence is reaching that level of importance now that we all know it is real.Government should not own it, operate it, or develop it. Quite the opposite. Companies are the right actors to build fast, compete hard, improve models, serve customers, and discover the real use cases. Self-interest is a useful framing here. Markets are good at finding demand, reducing costs, and turning invention into services people actually use.Companies are the right operators, developers, and owners. But that does not settle the real question of who owns the benefits. That is an economic question.If intelligence becomes metered infrastructure, what happens to the value it creates?The Ownership StackThis week's articles keep circling the same issue from different directions but in the nature of ‘circling' never quite nail it.Jamin Ball's “Own Your Weights” starts with the enterprise version of the question. Owning a model file is not enough. The durable asset is the loop: the data flywheel, the evaluations, the reinforcement system, the workflow learning, and the operating context that lets capability compound.Benedict Evans' “Ways to Think About Token Pricing” adds the market layer. Tokens may become essential, abundant, and cheap, like mobile data. But being essential does not guarantee that the token layer captures the value. The money may move up the stack to whoever owns the workflow, the customer, the distribution, or the application.Alex Karp's fight with the labs, reported in “Alex Karp Is Saying What Every Angry CEO Is Thinking About AI”, is the same argument in sharper enterprise language. Companies are afraid that model providers will not just sell intelligence, but learn from customer workflows and then move into the markets where those workflows create value. The “All-in” group are echoing Karp's view.And “What Is Loop Engineering, and Who Owns It?” names the new contested terrain. The loop is where intelligence meets the world. Whoever owns the loop owns the learning. Whoever owns the learning owns the compounding asset.That is why “who owns intelligence?” is not a slogan. It is the question under the model layer, the application layer, the enterprise layer, and the economic layer.Because intelligence is the product, the tools creating it are fragmented and competitive. So there is no logic in trying to discuss this at the level of a single company or set of tools and models.The Old Promise Was That Commerce Would Tame PowerThe essays this week give the historical backdrop.Deirdre McCloskey, in “What Really Caused the Industrial Revolution”, argues that modern growth came not simply from capital accumulation, but from a change in permission: ordinary people were allowed to innovate, trade, build, and be honored for it.That matters because intelligence could be another expansion of permission. It could make more people capable of building, learning, creating, coding, researching, translating, selling, and coordinating. It could lower the cost of competence.But only if access is broad.Paul Krugman's “AI in an Age of Oligarchy” warns that the same technology lands differently in different political economies. A new general-purpose technology entering a broad, open, upwardly mobile society is one thing. The same technology entering a concentrated economy, with extreme wealth and weak counterweights, is another.Tim O'Reilly's Economist essay, “Elon Musk is building a form of capitalism that Adam Smith would hate”, makes the governance point more directly. The old liberal hope was that commerce would tame arbitrary power. Markets, boards, courts, shareholders, disclosure, and competition would discipline the prince.But what if the prince uses markets to escape discipline?Henry Farrell's “political economy of billionaire derangement” pushes the same point. Founder culture, monopoly ambition, peer rivalry, weak correction mechanisms, and vast private control can amplify appetites rather than restrain them.The danger with intelligence is not that companies build it. They should. Companies build it, meter it, use public tolerance and public infrastructure to scale it, learn from everyone who uses it. All of those things are inevitable and healthy. Market forces will sort out winners from losers. The real danger is that the winners treat all of the surplus produced as purely private.Metered Intelligence Creates SurplusIf metering is not the problem, what is?The problem is pretending that metered intelligence creates value only for the metering entity. Metering water is only tolerated as a public good. If the public were blackmailed by a private water company with the threat of no water we would all rebel.Once we understand that the product of AI is intelligence we can see that every time intelligence is used, there is the immediate transaction: the user pays, the provider serves.But there is also system value. Usage creates signals. Workflows reveal patterns. Prompts, corrections, failures, preferences, integrations, edge cases, and business processes all help define where intelligence is useful and how it should improve. Intelligence breeds intelligence.Even when customer data is contractually protected, the market learns. The platform learns where demand is. The product team learns which workflows matter. The ecosystem learns which jobs are vulnerable, which tasks are automatable, and which parts of the economy can be reorganized around machine intelligence.So the surplus is not born in a vacuum.It rests on public science, public education, public data exhaust, public law, public infrastructure, public energy systems, public tolerance for data centers, and billions of human interactions. It is served by companies, but it is not made only by companies.This is why “Americans Deserve a Dividend From AI Companies' Riches” belongs at the center of this week's issue. The detail can be debated. The principle is harder to dismiss. If intelligence becomes a new foundational resource, then some part of the wealth it creates should flow back to the people whose society makes it possible. Intelligence did not suddenly appear. AI is built on the entire history of human intelligence. It benefits from it and at the same time evolves it.Not Nationalization. A Human Wealth Fund.If intelligence belongs to everybody, some conclude that government ownership of intelligence is the right outcome.Governments are not well suited to build, operate, or improve intelligence. They will move too slowly, regulate too early, politicize the wrong things, and confuse economic participation with operational control.Andrew McAfee's “Why I Didn't Sign the AI Open Letter” is useful here. His objection is not that the technology is unimportant. It is that steering too hard before we understand the shape of the change can become its own failure mode. Marc Andreessen's satire of AI regulation is less policy than temperament, but it captures a real Silicon Valley fear: that regulation can become permission, capture, and incumbency before it becomes wisdom.That fear should be taken seriously.But it does not answer the economic question. It answers only the operational one.How can the economic benefits of intelligence be distributed? The better answer is a sovereign human wealth fund.Call it a sovereign wealth fund if you must, but the phrase is too national. Intelligence will not respect borders. The leading companies are global. The models, chips, data centers, agents, platforms, and workflows will be transnational from the beginning. If the value created by intelligence is global, then the mechanism for sharing some of that value should begin with the companies global enough to capture it. The nice thing about xAI, OpenAI, and Anthropic is that they are supranational.These companies own and operate intelligence. Let them compete. Let them profit. Let them keep the incentives that make the system improve. But if intelligence is the new water, the wealth it creates cannot belong only to the companies that meter it. And they, themselves, have the power to fix it, even more than governments.Access will become a Human Right; Ownership Is the Economic DesignThis is where human rights come in. There is no right to access an AI model, yet. But there will soon be a need to change that.Not as a claim that every person is entitled to every frontier model at every moment for free. That is not serious. Capacity has costs. Models have costs. Inference has costs. Data centers have costs. Although those costs will decline over time, possibly quite quickly as self-learning models address costs.The claim is more basic: in a world where intelligence becomes a primary input into education, work, health, science, citizenship, creativity, and economic agency, baseline access to intelligence starts to look like a civic requirement.That could mean public access layers. It could mean education credits. It could mean open models. It could mean AI dividends. It could mean public-interest compute. It could mean taxes on rents. It could mean a company-initiated human wealth fund that returns some of the upside to society without handing the operating system to the state. The latter could couple wealth growth with universal distribution of ownership.The exact mechanism matters. But the distinction matters more.Government should not own intelligence. It should be universally available. And people should have a claim on the wealth intelligence creates.The Frontier Is Also PhysicalThe abstraction is not weightless.“The Fight Against AI Data Centers Is Just Beginning”, “New York becomes the first state to enact a data center moratorium”, Reuters on pollution from Musk's xAI power project, and DataGravity's “Who Captures Value in AI Infrastructure?” all say the same thing from the ground up.Intelligence uses land. It uses power. It uses water. It uses chips. It uses grid capacity. It uses neighborhoods. It uses public patience.That makes the value question unavoidable. A society can accept the buildout if the buildout is legible as shared progress. It will resist it if the costs are local, the profits are private, and the benefits feel enclosed.Who Owns the “Loop”?The week ends where it began.“Anthropic and Blackstone” are betting that implementation is the next trillion-dollar business. “Vint Cerf” is working on identity for agents on the open internet. “GPT-Red” points toward systems that improve their own robustness. “Kimi K3” adds another open frontier model to the global mix.The model race continues. The deployment race is accelerating. The governance race is behind.My view is this:The central product of this era is intelligence. Companies have figured out how to capture it, package it, serve it, and meter it. That is good. It should stay in the hands of builders who have the incentive to make it better.But intelligence is too foundational to become just another private toll booth. A significant part of it will turn out to be free to users.As intelligence becomes a general-purpose resource, then access to it becomes a human-capability question, and the surplus from it becomes an economic-justice question. Not because government should run it. Because government should not run it. The operating layer belongs with companies. The wealth question belongs with everyone. But companies are best placed to turn that into a process of distribution.The question is not whether companies should build intelligence. They should.The question is whether humanity gets a stake in the wealth created by the thing that may soon become its most important shared input.Contents* Essays* Deirdre McCloskey on What Really Caused the Industrial Revolution* AI in an Age of Oligarchy* Elon Musk is building a form of capitalism that Adam Smith would hate* Murky Mirror: Truth and Consequences* The political economy of billionaire derangement* Is there any “oligarchy” to fight?* AI* Nearly 200 Economists and Tech Leaders Warn of A.I. Threats* Why I Didn't Sign the AI Open Letter* Own Your Weights* Ways to Think About Token Pricing* Alex Karp Is Saying What Every Angry CEO Is Thinking About AI* The AI Agents Are Coming for Microsoft Office* What Is Loop Engineering, and Who Owns It?* The Fight Against AI Data Centers Is Just Beginning* 6 months to live for open models* Americans Deserve a Dividend From AI Companies' Riches* Who Gets to Define the Frontier?* GPT-Red: Unlocking Self-Improvement for Robustness* Anthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just models* Vint Cerf is working on a plan to unleash AI agents on the open internet* xai-org/grok-build, now open source* The Pulse: What can we learn from Bun's rapid Rust rewrite with AI?* Orphan risks at the frontier of artificial intelligence* The Lab of the Future Should Feel Like a Data Center* Why AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”* Kimi K3 Tech Blog: Open Frontier Intelligence* Venture Capital* Three Years In* Venture Has Rarely Looked More Bifurcated* The Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active Now* Are Prediction Markets Doomed to Fail?* Regulation* Exclusive: The Next Frontier of the Deportation Wars: College Campuses* The Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.* India's crackdown on a new WhatsApp feature risks setting a global precedent* Let's build a children's public internet* Computer cops* Google is better at playing the AI regulations game* Infrastructure* Who Captures Value in AI Infrastructure?* New York becomes the first state to enact a data center moratorium* Pollution from Musk's unpermitted xAI power project hits hardest in Black communities* Interview of the Week* The End of the End of Geography* Startup of the Week* Radical AI's Joseph Krause: The Scientist Building The “Waymo” Lab For New Materials* Post of the Week* Marc Andreessen on AI RegulationEssaysDeirdre McCloskey on What Really Caused the Industrial RevolutionYascha Mounk and Deirdre McCloskey | Persuasion | July 11, 2026Yascha Mounk interviews Deirdre McCloskey about her argument that the modern world's economic liftoff came less from capital accumulation than from a change in ideas. McCloskey says both left and right versions of the conventional story rely too heavily on investment: the left stresses exploitation and surplus value, while the right stresses virtuous saving by capitalists. Her objection is historical and economic. Human beings had always invested, from irrigation works and Roman roads to seed grain, and simple accumulation quickly runs into diminishing returns.McCloskey's alternative is that northwestern Europe, first Holland, then Britain and Scotland, and then the North American colonies, developed a liberal ideology that changed who was allowed to innovate and be honored for it. The conversation links that shift to the erosion of inherited hierarchy, the spread of dignity for ordinary commercial life, and a moral vocabulary in which liberalism is not merely procedural but connected to virtues and values. The point is not that machines, coal, trade, and institutions did not matter, but that they do not explain the scale and timing of modern enrichment without a cultural permission structure for innovation.The interview also turns to the contemporary defense of liberalism. Mounk frames the series around the worry that liberalism is often treated as too thin to command allegiance, while its opponents speak more directly to moral passions. McCloskey's case is that liberal societies became rich because they dignified experimentation and ordinary enterprise, and that liberals need to recover the moral language behind that claim.Read moreAI in an Age of OligarchyPaul Krugman | Paul Krugman | July 12, 2026Paul Krugman frames AI as a major technological shock arriving inside an already unequal political economy. The post says AI's economic and social effects may take years to understand, but argues that the setting matters now: America has much greater wealth concentration and political inequality than it did in the 1950s and 1960s, when progressive taxation, stronger regulation, and more active antitrust might have contained some of the destructive effects of a new technology.Krugman's opening claim is that the same technology would likely have different consequences in a more level society. In today's United States, he writes, extreme wealth is both a cause and effect of policies that favor a small elite, including low effective taxes on capital and high incomes, weak enforcement of worker protections and antitrust, and cuts to programs that benefit ordinary Americans.The article is explicitly more about oligarchy than AI. Krugman says the paid sections document the rise of the “.0002%,” the economics and politics of extreme wealth, how oligarchy will shape AI's impact, and possible policy paths. His caveat is that AI itself may still produce a pushback against oligarchy, but absent that, he expects the pre-existing concentration of wealth and power to magnify AI's downsides.Read moreElon Musk is building a form of capitalism that Adam Smith would hateAuthor: Tim O'Reilly Published: July 12, 2026Tim O'Reilly argues that Elon Musk is using the legal forms of shareholder capitalism to escape the restraints that shareholder capitalism was supposed to impose. The article begins with SpaceX's public-market structure: ordinary public investors get little meaningful governance power, Musk keeps roughly 85 percent of the votes through super-voting shares, buyers waive jury trials and class actions, the company qualifies as controlled, and removal of Musk depends on the share class he controls. In O'Reilly's framing, that is not ordinary founder control; it is a design for being answerable to no one, possibly beyond Musk's own lifetime.The killer detail is the article's turn through Albert Hirschman, Montesquieu, James Steuart, Adam Smith, and Keynes. Older defenses of commerce held that markets would tame princely passions because the self-interest of merchants was safer than arbitrary rule. O'Reilly says Musk reverses that hope. The market discipline that was supposed to cage the prince has become the lever by which the prince raises capital, removes feedback loops, and carries private power into politics, government, Mars, robots, AI, or whatever ambition comes next.The pull is the link to AI governance. O'Reilly says corporations are already a kind of artificial intelligence: narrow-input systems that act at a scale no individual human can match. Their partial controls include independent boards, shareholder votes, courts, disclosure, regulators, public pressure, and activism. If the leaders building frontier AI strip those alignment mechanisms out of their own companies, the governance of the company becomes a preview of the governance of the machine.Read more: The EconomistMurky Mirror: Truth and ConsequencesAuthor: Esther Dyson Published: July 14, 2026Esther Dyson argues that today's institutional crisis is better viewed through the 14th century than through recent political history. Using Barbara Tuchman's A Distant Mirror as her frame, she compares a world of famine, plague, church schism, feudal predation, and purposeless war with a present in which institutions again feel brittle, incentives are badly aligned, and power is shifting into forms that are hard to govern.The killer detail is the historical analogy between land, corporations, and AI. Dyson moves from nobles who controlled serfs and territory, to the East India Company as a quasi-sovereign business, to today's AI systems and data centers as a possible new sector that crosses and weakens both nation-states and companies. The question is whether AI becomes a new kind of private land, owned by a new nobility, or an open prairie that many people can cultivate.The pull is human attention. Dyson says the central question is not what AI will do to people, but how people will react to it: whether they can value love, kindness, embodied attention, and artisanal human presence in a world of seductive artificial offerings.Read more: SourceThe political economy of billionaire derangementAuthor: Henry Farrell Published: July 15, 2026Henry Farrell argues that the visible political radicalization of some Silicon Valley billionaires is not a random personality quirk, but a product of the political economy that made them. Starting from Tyler Cowen's dismissal of “billionaire derangement syndrome” and Tim O'Reilly's warning that Elon Musk is using shareholder capitalism to escape shareholder restraint, Farrell flips the phrase: the question is why billionaires themselves can become deranged.The killer detail is Farrell's use of Peter Thiel as both theorist and example. Thiel's Stanford lectures described startups as monarchies and founders as figures vested with unusual power, while Silicon Valley culture rewarded eccentricity, monopoly ambition, and founder exceptionalism. Farrell says those ideas combined with dense founder-investor networks, peer rivalry, and weak correction mechanisms to amplify rather than discipline princely appetites.The pull is the ideological problem for classical liberals who once saw tech wealth as an ally of markets and freedom. Farrell says commerce did not tame the passions; in parts of Silicon Valley, the passions have begun to devour markets, institutions, and the liberal story that justified them.Read more: SourceIs there any “oligarchy” to fight?Matthew Yglesias | Slow Boring | July 16, 2026Matthew Yglesias argues that “oligarchy” is a rhetorically powerful but analytically loose way to describe American politics. The post begins from Bernie Sanders' “Fighting Oligarchy” tour, Amy Klobuchar's warning about a MAGA “broligarchy,” and the long afterlife of the Martin Gilens and Benjamin Page paper that was widely summarized as showing that only the rich matter in policy outcomes. Yglesias says the evidence supports a weaker claim: affluent people and business leaders have unusual access and influence, but that is not the same as rule by a small cabal.His main distinction is between inequality and oligarchy. The Gilens-Page measure treated the top 10 percent of households as “the wealthy,” and later critics found that rich and middle-class preferences usually align; in the cases where they differ, the rich win about 53 percent of the time. Yglesias also says business executives get special access partly because their decisions are materially important to communities, jobs, investment, and local tax bases, not only because of campaign donations.The post preserves Jerusalem Demsas' counterpoint from their podcast discussion: privileged donor and business access can still violate democratic equality even if the oligarchy label overstates the structure of power. Yglesias' narrower claim is that Democrats should be precise about what problem they are trying to solve, because donor influence can also push the party left on climate and cultural issues in ways that alienate many voters.Read more: Slow BoringAINearly 200 Economists and Tech Leaders Warn of A.I. ThreatsAuthor: Ben Casselman Published: July 13, 2026Ben Casselman reports on “We Must Act Now,” a statement warning that artificial intelligence could transform the economy faster than any previous technology and that policymakers need to move faster to understand and respond. The statement says AI may become radically more powerful over the next 10 years, bringing risks such as large-scale job displacement as well as opportunities such as higher living standards. Nearly 200 people signed, including 15 Nobel laureates, the chief economists of OpenAI and Anthropic, Anthropic co-founder Jack Clark, former Google CEO Eric Schmidt, and venture capitalist Vinod Khosla.The killer detail is who joined the warning. Casselman notes that the signatories include economists who have historically been skeptical of Silicon Valley's most dramatic AI job-loss forecasts, including Daron Acemoglu and Simon Johnson, the MIT professors who won the 2024 Nobel in economics. Erik Brynjolfsson, who helped organize the statement, says there has been a notable change in the profession and that economists and policymakers are not ready for the “tsunami” he sees coming.The pull is the measurement problem. The statement does not offer a specific policy menu, but calls for economists, policymakers, and industry leaders to understand the economics of transformative AI and steer it toward complementing humans. Brynjolfsson says one high priority is better data on AI's spread and impact, because current measures tell conflicting stories about job losses and which workers are most exposed.Read more: The New York TimesWhy I Didn't Sign the AI Open LetterAuthor: Andrew McAfee Published: July 13, 2026Andrew McAfee explains why he did not sign “We Must Act Now,” the AI economy statement organized in part by his longtime collaborator Erik Brynjolfsson. McAfee agrees with the letter's starting point that AI is likely to become radically more powerful over the next decade and that it is a general-purpose technology. His objection is not to urgency or to studying AI's economic effects, but to the framing of risk, displacement, and institutional steering as the first move.The killer detail is McAfee's line edit. He says the original letter comes close, then “bounces off the crossbar” by calling for incentives, guardrails, and institutions to steer AI before we know enough about its actual impacts. He points to mixed current evidence: labor-market canaries, but also rising software job postings, low unemployment for younger workers, rising real median income, and claims that AI-adopting companies are adding workers faster than low-adopting peers. His worry is that the letter leans toward upstream governance and dirigisme when the evidence may call for capability building instead.The pull is his replacement statement. McAfee keeps the three-paragraph structure but changes the emphasis: AI is likely to become radically more powerful; like earlier world-changing technologies it will raise living standards while also bringing harms and shocks; and economists, policymakers, and technology leaders should build the capabilities to respond quickly and effectively. It is a concise version of the permissionless-innovation case inside the AI policy debate.Read more: The Geek WayOwn Your WeightsAuthor: Jamin Ball Published: July 10, 2026Jamin Ball argues that the enterprise AI debate about whether companies should “own their weights” or rent models from frontier labs is asking too narrow a question. A model weight file gives a company control over a point-in-time artifact, but not durable control over the capability stack. In his framing, the weight file is a melting ice cube: it does not get worse in absolute terms, but it falls behind as frontier systems improve and enterprise needs change.The killer detail is what Ball says companies really need to own: the data flywheel, reinforcement learning infrastructure, and evaluation harness that produce and improve the model. Simply deploying an open-weights model and declaring sovereignty leaves the enterprise with yesterday's capability and no way to compound workflow-specific learning.The pull is that enterprise AI control may be less about model ownership than operating ownership. The defensible layer is the system that turns company data, edge cases, business definitions, and evaluations into continuously improving performance.Read more: Clouded JudgementWays to Think About Token PricingAuthor: Benedict Evans Published: July 9, 2026Benedict Evans argues that today's AI token prices are a temporary signal from a supply-constrained market, not a reliable guide to long-term value capture. The open question is whether foundation models keep durable pricing power or become commodity infrastructure as data-center capacity, inference efficiency, and model competition all shift. His current read is that the visible market dynamics point toward commoditization unless something materially changes.The killer detail is the mobile data analogy. Evans says cellular networks became a trillion-dollar industry with hundreds of billions in capex after data usage exploded, but carrier stocks went nowhere because value moved up the stack. Tokens may behave similarly: an opaque unit tied to marginal cost, sold through bundles, essential to everything, yet not necessarily where profits accrue.The pull is uncertainty, not prediction. Evans lists paths to model dominance, including network effects, less competition, regulation, export controls, or a lab pulling ahead on execution, but says each requires a new fact not yet visible. Without that change, the model layer looks more like infrastructure beneath the products that capture value.Read more: SourceAlex Karp Is Saying What Every Angry CEO Is Thinking About AIAuthor: Tim Higgins Published: July 11, 2026Tim Higgins reports that Palantir CEO Alex Karp has turned corporate frustration with AI labs into a public argument about enterprise control. Palantir released a white paper, “Institutional Sovereignty in the Age of AI,” laying out steps companies and governments can take to protect themselves from OpenAI, Anthropic, and other foundation-model providers. The article links that paper to Karp's CNBC appearance, where he said “something has gone completely wrong” in the relationship between AI labs and customers and argued that enterprises are paying for tokens that create little value.The killer detail is the value-capture question. Higgins writes that Karp's critique has resonated because AI labs may gain power and insight from customer data, workflows, and decision-making, even when enterprise policies say customer data are not used for training. David Sacks amplified the concern by arguing that Anthropic is moving from the model layer into vertical applications such as science, security, legal, and coding, raising the fear that model providers will watch where value is being created and then move into those markets directly.The pull is that Karp is not alone, even if his style is unusually combative. Higgins notes that Satya Nadella has also warned that companies need to retain the learnings created when they use AI models, while Mark Zuckerberg has framed Meta's new model release partly around lower-cost frontier intelligence. The article presents Karp's campaign as one sign that established technology companies and large enterprises are trying to define where they fit when AI labs become central infrastructure, application competitors, and potential IPO giants at the same time.Read more: The Wall Street JournalThe AI Agents Are Coming for Microsoft OfficeAlex Wilhelm | Cautious Optimism | July 11, 2026Alex Wilhelm argues that one of the week's quieter AI questions is whether the productivity market that Microsoft successfully moved into subscription software is now being attacked by agentic tools. The piece begins with the infrastructure backdrop: SK Hynix raised $26.5 billion in a U.S. listing while building U.S. HBM and advanced-packaging capacity, and memory, chip, and foundry companies are now priced for sustained AI demand.Wilhelm then says the AI conversation has shifted quickly from raw capability to cost per task. He cites new model releases and vendor language emphasizing cheaper agentic and coding models, faster performance, and lower dollars per task. That matters because lower costs make it more plausible for AI systems to take on routine knowledge work at scale rather than remain a premium coding assistant market.The core of the article is Microsoft Office. Wilhelm notes that Microsoft turned Office from a one-time purchase into Microsoft 365, a large recurring revenue business with tens of millions of subscribers and a major productivity segment. Now, he says, late-stage unicorns and AI labs are pushing into the same territory: Anthropic's Cowork was reportedly used mostly outside software development, OpenAI merged ChatGPT and Codex into a tool for creating sheets, slides, docs, web apps, and long-running work, and other companies are building agentic coworkers that connect business data to documents, workflows, schedules, alerts, and apps.The article's caveat is that Microsoft has survived major platform shifts before. The argument is not that Office disappears quickly, but that the definition of office software is broadening from documents and spreadsheets into AI systems that can create, monitor, and act across workplace data.Read moreWhat Is Loop Engineering, and Who Owns It?Author: Nilesh Barla Published: July 11, 2026Nilesh Barla argues that “loop engineering” is becoming a distinct discipline because production AI agents now fail less at single prompts than at runtime: when to stop, what state to preserve, and how to recover after a bad step. Prompt engineering shapes one model call, and context engineering shapes what the model sees, but loop engineering shapes what a sequence of calls actually does.The killer detail is the three-primitives frame. Barla says a real agent loop needs halt conditions, state carryover, and recovery paths, then maps teams across five maturity levels. At the lowest level, an agent is just a model call in a for-loop with a step cap and raw history; by the higher levels, the system has structured state, explicit planning, replay, evaluation, and self-repair.The pull is organizational. If agents are becoming production systems rather than demos, someone has to own the runtime itself. The loop engineer is the role Barla gives to the person responsible for making long-running agent work dependable.Read more: Adaline LabsThe Fight Against AI Data Centers Is Just BeginningEmma Roth | The Verge | July 12, 2026Emma Roth argues that community resistance to data centers has moved from an early warning sign into a national political fight as AI facilities grow larger, more power-hungry, and more visible to nearby residents. The article starts with Apple's failed 2015 plan for a $1 billion data center in Athenry, Ireland, where a small group of residents challenged the project over noise, light pollution, flooding, traffic, and wildlife effects until Apple abandoned it in 2018.The current data-center buildout is presented as much larger and more contentious. Roth writes that residents now cite rising energy costs, water quality, noise, light pollution, and greenhouse gas emissions, while the U.S. Energy Information Administration expects commercial energy demand to surpass residential demand this year because of AI data centers and Goldman Sachs expects data-center power demand to double by 2027.The central evidence comes from Data Center Watch, which says protesters blocked or delayed at least 75 U.S. projects worth $130 billion from January to March, with active opposition groups more than doubling from 396 at the end of 2025 to 833 by the end of the first quarter of 2026. Roth also cites QTS abandoning a $12 billion Wisconsin campus, Delaware City regulators blocking a 580-acre project under the Coastal Zone Act, opposition stopping a QTS project in Prince William County, and pressure that pushed Kevin O'Leary to downsize the proposed 40,000-acre Project Stratos in Utah.The policy section describes a split between federal acceleration and local resistance. President Trump has treated data centers as part of the AI race with China and fast-tracked construction, while some Republican candidates are distancing themselves from that position ahead of midterms. Sanders and Ocasio-Cortez have proposed a moratorium until price and environmental protections exist, bipartisan lawmakers are backing ratepayer-protection measures, and states including Florida, Idaho, and Washington have passed rules on cost shifting, water use, and tax breaks. Roth's caveat is that the policy patchwork is still incomplete, leaving many communities to fight project by project.Read more6 months to live for open modelsAuthor: Nathan Lambert Published: July 12, 2026Nathan Lambert argues that open-weight AI models are facing their most serious policy test so far because U.S. officials are beginning to discuss concrete controls rather than abstract safety concerns. He says reported White House conversations about a new executive order may initially target Chinese-origin models and government use, but could create a broader review habit for frontier open models. His forecast is that a model above the capability range of GPT-5.5, Claude Opus 4.8, or GLM-5.2 could trigger a ban or indefinite delay within six months.The post separates two policy fights that are becoming intertwined: distillation and frontier capability. Lambert says the distillation campaign against Chinese models has become a form of regulatory capture because Anthropic and other closed-model companies would gain economically if Chinese open models were banned. He does not dismiss IP protection, but argues that if a closed model's capabilities are dangerous enough to justify restricting open models, the lab also has to explain why those capabilities are exposed through a queryable API. He cites unauthorized access to Anthropic's Mythos private beta as evidence that APIs are not automatically secure.The broader claim is that a unilateral U.S. ban would hurt positive actors more than bad actors if comparable open models remain available elsewhere. Lambert says the only durable ceiling would require global agreement, which does not exist, and that open models can improve safety by allowing broad inspection, adaptation, and understanding. His proposed near-term off-ramps are a strong U.S. open model release from companies such as Microsoft, Meta, or Reflection, and a broader coalition of open-source beneficiaries lobbying for safe rollout rather than prohibition.Read more: SourceAmericans Deserve a Dividend From AI Companies' RichesAuthor: Scott Stanford Published: July 14, 2026Scott Stanford argues that proposals to give the government a stake in AI companies miss the point unless ordinary citizens directly receive and control the upside. Sam Altman has discussed giving up equity in OpenAI, Washington already owns a stake in Intel, Nvidia is sharing China chip revenue, and Bernie Sanders wants large AI labs to contribute half their stock to a sovereign wealth fund. Stanford says those ideas all park value with the state, not with people.The killer detail is New Carlisle, Indiana, where AWS's Project Rainier is turning cornfields into one of the world's largest AI superclusters. The project is planned to run up to a million chips, draw more than two gigawatts of power, and represents an investment that has grown from $11 billion to $13.8 billion. Stanford uses that local transformation to argue that AI's public bargain should be visible at the household level.The pull is design. A citizen AI dividend would have to specify who earns a stake, how they hold it, and when they see cash. Without that mechanism, the AI wealth debate remains a fight over government balance sheets rather than public ownership.Read more: SourceWho Gets to Define the Frontier?Author: Mark Daley Published: July 14, 2026Mark Daley argues that Demis Hassabis is right to call for a serious institution to verify frontier AI systems, but that the power to test models is also the power to govern them. Hassabis's proposed Frontier AI Standards Body would get privileged pre-release access to advanced models, testing compute, held-out evaluations, support from national labs and security agencies, third-party auditors, and eventually authority to block models from the American market or coordinate a slowdown.The killer detail is Daley's constitutional objection. He says the proposal sometimes looks like a scientific lab, a standards body, an industry regulator, a licensing authority, and an emergency security council at once. Combining those roles because each requires technical expertise would be like putting the central bank, auditor-general, and Supreme Court in one building and calling it efficient.The pull is standard-setting. Daley's concern is not that verification is unnecessary, but that whoever writes the tests, decides what passes, adjudicates disputes, and grants market access may end up defining the frontier itself.Read more: SourceGPT-Red: Unlocking Self-Improvement for RobustnessOpenAI | OpenAI | July 15, 2026OpenAI describes GPT-Red as an internal automated red-teaming model trained to find prompt-injection vulnerabilities at a scale human red teams cannot match. The post says AI systems increasingly encounter third-party data through browsers, connected apps, local files, and tools, creating opportunities for malicious instructions hidden in emails, webpages, tool responses, or code repositories. Human red-teaming remains part of OpenAI's safety process, but the company says it is time-intensive and cannot generate enough diverse adversarial examples for model training.The system is trained through self-play reinforcement learning, with GPT-Red rewarded for eliciting valid failures and defender models rewarded for resisting attacks while still completing their tasks. OpenAI says the training environments specify threat models across settings such as local files, webpage banners, email bodies, and tool outputs. The model is kept separate from deployed production models because it is intentionally trained with malicious capabilities.OpenAI reports that GPT-Red generalized beyond its training set, including an internal replication of the indirect prompt-injection arena from Dziemian et al. (2025), where it found successful attacks in 84% of scenarios compared with 13% for human red-teamers. The post also says GPT-Red transferred attacks from simulation to a live autonomous vending-machine agent, causing price changes and order cancellations, and outperformed a prompted GPT-5.5 baseline against a Codex CLI agent on held-out data-exfiltration tasks.The article's main robustness claim is that OpenAI has used GPT-Red and predecessor models in training since GPT-5.3, with later GPT releases becoming more resistant to prompt injections. It says GPT-5.6 Sol has six times fewer failures on OpenAI's hardest direct prompt-injection benchmark than the best production model from four months earlier, that a “Fake Chain-of-Thought” attack class fell from more than 95% success against GPT-5.1 to below 10% against GPT-5.6 Sol, and that GPT-5.6 Sol fails on only 0.05% of GPT-Red's direct prompt injections. OpenAI says general capabilities and targeted over-refusal evaluations were not harmed, and says a preprint with more details will follow.Read moreAnthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just modelsRebecca Bellan | TechCrunch | July 15, 2026Rebecca Bellan reports that Ode with Anthropic is the $1.5 billion AI implementation company launched by Anthropic with Blackstone, Hellman & Friedman, Goldman Sachs, and other backers. The article says the venture reflects a growing belief among frontier AI labs that enterprise adoption requires more than better models: customers need engineers who can embed inside businesses and turn AI into working systems.Ode was originally conceived by Blackstone after it used both large consulting firms and smaller AI services boutiques across its portfolio companies. TechCrunch reports that Fractional AI, an AI engineering services startup, stood out and was acquired by the joint venture shortly after the venture was announced. Fractional now forms the foundation of Ode, which has 100 engineers and works closely with Anthropic's applied AI team to identify where the technology can affect specific businesses.Ode CEO Chris Taylor tells TechCrunch that the company could someday become a trillion-dollar business if it scales without losing quality. He says an ideal customer is one whose CEO treats the AI project as a top one or two priority, whether it is a major product feature or the reworking of a core business process. Ode will operate under a “Claude-first” principle, using Anthropic technology whenever possible, but the article says it can use rival AI products when needed.The article's central implementation argument comes from Ode chief technologist Eddie Siegel, who says model selection matters but is not where most of the engineering effort goes. He compares it to the choice of programming language in software: one ingredient in a system that still has to be engineered. Bellan writes that Ode's challenge is hiring and training enough elite generalist engineers, many of them former founders, while competing with OpenAI's The Deployment Company and consulting giants that have built their own forward-deployed engineering teams.Read moreVint Cerf is working on a plan to unleash AI agents on the open internetTim Fernholz | TechCrunch | July 15, 2026Tim Fernholz reports that Vint Cerf, after leaving Google, is advising Innovation Labs on an open architecture for identifying AI agents online. Innovation Labs is a subsidiary of Identity Digital, a DNS registry company, and its proposal is to use domain-name infrastructure as part of a system for agent identity, accountability, and auditability. The premise is that agents will need a way to identify themselves if they move beyond proprietary systems and begin interacting across the open internet.The concrete proposal is DNSid, a registry that links an AI agent to an existing internet domain and uses cryptographic proofs to log its registration over time. Innovation Labs says it is trialing the standard with unnamed hyperscalers and identity companies. Cerf frames the problem around authority and accountability: what authority an agent has, where that authority came from, who is accountable for the agent's behavior, how its identity is established, and why anyone should trust it.The article's caveat is that standards are still emerging and agents are more active than static domains. Cerf says the period may be both fascinating and exasperating because the functionality is powerful and interoperability is unresolved. He compares the adoption problem to TCP/IP: competing systems may not work together until users push for functional interoperation. He also says an agentic economy is not inevitable, but that people will try to build it because delegating work to agents will be easier.Read more: TechCrunchxai-org/grok-build, now open sourceAuthor: Simon Willison Published: July 15, 2026Simon Willison argues that xAI's decision to open-source Grok Build is best understood as a trust repair move after a severe privacy failure. The CLI had triggered backlash when users realized that running it in a directory could upload the entire directory to xAI's Google Cloud buckets, including one user's reported SSH keys, password manager database, documents, photos, and videos. xAI disabled the feature, said previously retained coding data would be deleted, and released the code under Apache 2.0.The killer detail is what the codebase reveals. Willison counts 844,530 lines of Rust, only about 3% of which appears vendored, and finds remnants of the upload system still present but disabled: gcs.rs contains Google Cloud upload code, while upload_session_state() now returns a hard-coded session_state_upload_unavailable error. He also notes copied or ported tool implementations from Codex and OpenCode, prompt files, and a terminal Mermaid renderer.The pull is that terminal coding agents are becoming large, intricate software systems in their own right. The privacy failure mattered because these tools operate inside the directories where developers keep their most sensitive work; the open-source release matters because trust now depends on inspecting what an agent can see, send, and do.Read more: SourceThe Pulse: What can we learn from Bun's rapid Rust rewrite with AI?Author: Gergely Orosz and Ivan Klaric Published: July 16, 2026Gergely Orosz and Ivan Klaric argue that Bun's AI-assisted rewrite from Zig to Rust is a practical sign of how software engineering changes when models can take on large, bounded migrations with clear feedback loops. The piece does not treat the rewrite as magic: Jarred Sumner first spent hours turning design judgment into a detailed porting guide, then used adversarial review, parallel agents, compiler errors, and tests to force the work toward correctness.The killer detail is the scale. Bun had 535,496 lines of Zig, 1,448 files, and 22 million monthly downloads, making a conventional rewrite a year-long freeze the team could not justify. Using Fable, Sumner split the work across 64 agents, produced about 6,500 commits, and got the migration done in 11 days at an estimated API cost of $165,000.The pull is economic, not theatrical. If a one- or two-year migration can become an 11-day project, AI coding is not just faster autocomplete; it changes which technical debts are worth paying down.Read more: SourceOrphan risks at the frontier of artificial intelligenceAuthor: Andrew Maynard Published: July 16, 2026Andrew Maynard argues that frontier AI safety frameworks are creating “orphan risks”: harms that companies can see, but do not formally own because they are hard to quantify, do not fit catastrophic-risk thresholds, or fall outside audit-friendly compliance machinery. His target is not existing frontier safety work, but the narrowing effect that happens when private companies decide which risks count as governable.The killer detail is Maynard's contrast between measurable model dangers and threats to value. He points to Meta's three-day Galactica collapse, OpenAI's 2023 board crisis, safety-team departures, and wellbeing litigation as examples of risks that damaged trust, culture, legitimacy, or users without fitting cleanly into conventional model-risk categories. The proposed fix is an orphan-risk register: a public record of risks a company considered and chose not to manage, with reasons.The pull is accountability. Frontier developers' internal scoping choices have become a de facto layer of public governance, so the question is no longer only which risks they manage, but which risks they quietly leave outside the frame.Read more: SourceThe Lab of the Future Should Feel Like a Data CenterLatent.Space with Andy Beam and Rafa Gomez-Bombarelli | Latent.Space | July 16, 2026Latent.Space interviews Lila Sciences CTO Andy Beam and chief science officer for physical sciences Rafa Gomez-Bombarelli about the company's attempt to build an AI-run science factory. The post describes Lila's thesis as treating the lab itself as an “infinite token generator”: if internet data drove the first era of AI scaling, experimentally verified scientific data may be the next scarce training source. Lila is trying to produce that data with robotics, lab instruments, orchestration software, and AI models wired into the wet lab.The central analogy is the lab as data center. Instruments are nodes on a graph, a magnetically levitating transport layer moves materials between them, and experiment scheduling looks like a compute queue. Beam says Lila is not simply an automation company, because the point is not just throughput; it is flexibility, generalization, and experiment capture. The post says Lila has built more than 10 trillion experimentally validated “scientific reasoning tokens,” not internet text or biological sequences.The interview ranges across biology, chemistry, drug discovery, materials science, and the limits of automation. It notes that Lila rebuilt one gas-sorption measurement to run roughly 2,500 times faster, claims its general models can transfer priors from small-molecule chemistry to metal-organic frameworks for carbon capture, and describes model-suggested platinum-group-free electrocatalysts that moved from looking boring or wrong to becoming strong performers. The caveats are physical: experiments have runtimes, biology cannot always be accelerated, chains of thought can be unreliable narrators, and reward hacking becomes more dangerous when a model controls a real lab.Read more: Latent.SpaceWhy AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”Kate Park | TechCrunch | July 16, 2026Kate Park interviews AMI Labs CEO Alexandre LeBrun about why Yann LeCun's world-model startup avoids the language of “AGI” and “superintelligence.” LeBrun says the terms are not useful because they lack stable definitions: “We never used the word AGI. And I just noticed that nobody is using it anymore; they switched to superintelligence.” His argument is that the practical frontier is not a label, but whether AI systems can understand and predict real-world states.The article explains the world-model thesis by contrasting language prediction with physical-state prediction. A large language model predicts the next word; a world model predicts the next state, such as what happens when a glass tips over. LeBrun says LLMs remain complementary and efficient for language, but the physical world is where current AI is weak. Robotics is the clearest case: hardware has advanced quickly, but robots are still brittle outside controlled routines because they lack context and situational understanding.AMI is still pre-product, but TechCrunch reports that LeBrun was in Seoul looking for industrial partners, researchers, and global companies. He says world models cannot be built entirely inside a lab because they need access to real environments. That is why South Korea appeals to AMI: robotics, semiconductors, manufacturing, and fast adoption create the kind of hardware-heavy context that software-only AI has barely touched.Read more: TechCrunchKimi K3 Tech Blog: Open Frontier IntelligenceKimi | Kimi | July 16, 2026Kimi introduces Kimi K3 as an open 3T-class frontier model aimed at coding, knowledge work, reasoning, multimodality, and long-context agentic use. The source describes the model as a 2.8T-parameter system built on Kimi Delta Attention and Attention Residuals, with native multimodality and a 1M-token context window. It says Moonshot AI plans to release model weights by July 27.The post presents K3 through benchmark and use-case sections rather than as a general product announcement. It reports results across coding, productivity, agentic, and multimodal evaluations, including DeepSWE, Terminal-Bench 2.1, Program Bench, SWE Marathon, FrontierSWE, PostTrain Bench, OfficeQA Pro, SpreadsheetBench 2, MCP Atlas, AutomationBench, BrowseComp, GDPval-AA v2, AA-Briefcase, MMMU-Pro, MathVision, BabyVision, OmniDocBench, and PerceptionBench. The source says all reported K3 results use maximum reasoning effort with temperature and top-p set to 1.0, and that different benchmark comparisons use KimiCode, Claude Code, or Codex harnesses depending on the test.Kimi's caveats are unusually concrete. The limitations section says K3 was trained in preserved thinking-history mode, so quality may become unstable if an agent harness does not pass historical thinking content correctly or if an ongoing session switches to K3 midstream. It also says K3's emphasis on long-horizon tasks can make it excessively proactive when it encounters minor issues or ambiguous intent, and recommends imposing explicit behavioral constraints for applications that require strict boundaries. The post adds that K3 remains behind Claude Fable 5 and GPT 5.6 Sol in user experience despite being competitive overall.Read moreVenture CapitalThree Years InAuthor: Tomasz Tunguz Published: July 10, 2026Tomasz Tunguz marks Theory Ventures' third anniversary by arguing that AI's central market effect is time compression. In his telling, model release cycles, company revenue milestones, enterprise adoption, and venture categories have all accelerated. Seed, Series A, and Series B still exist as financing labels, but they no longer cleanly describe company maturity when some seed rounds are larger than IPOs and the best AI companies can mature much earlier than prior software companies.The killer detail is the shift from models to inference. Tunguz argues that inference has become the dominant AI market because workloads and buyer preferences are fragmenting: video, batch, local, agentic, and real-time tasks each create different infrastructure needs. He compares this to databases splitting into OLTP, OLAP, vector, and streaming categories, with AI pushing the same specialization into inference infrastructure.The pull is that Theory sees the AI-native venture firm as part of the same pattern. The firm says it has analyzed twice as many investment opportunities with three investors working alongside a nine-person intelligence organization, using agents and research systems to map markets, source companies, and support diligence. The piece is both a market map and a statement about how venture itself is being rebuilt by the technology it funds.Read more: LinkedInVenture Has Rarely Looked More BifurcatedAuthor: Beezer Clarkson Published: July 14, 2026Beezer Clarkson points to PitchBook's Q2 report as evidence that the U.S. venture market has split into two very different realities. AI now accounts for more than 60 percent of all U.S. venture deal value, meaning the headline market can look active and well-funded even while much of the non-AI market is dealing with a much colder liquidity and fundraising environment.The thread uses that split as the setup for Clarkson's latest Origins episode with Alec Litowitz, founder of Magnetar and QStar Capital and one of Citadel's original founding partners. Clarkson says markets like this are periods of genuine uncertainty, not merely ordinary risk, which is why Litowitz's Adaptability Quotient framework is relevant.The embedded clip makes the liquidity point concrete. Litowitz says DPI is “the resolution of uncertainty” because it converts an uncertain investment into actual cash returned to LPs. In his framing, a realized dollar is a real mark, while TVPI remains uncertain until it is realized.The killer detail is the distinction between pricing risk and resolving uncertainty. Litowitz's perspective matters because QStar is a SpaceX investor and Clarkson says the conversation happened just before one of venture's most consequential IPOs. The episode's stated questions are why venture remains a way to gain exposure to innovation, how AI is changing what is investable, why liquidity is ultimately a function of time, and why uncertainty requires a different decision framework from risk.Read more: XThe Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active NowAuthor: Ilya Strebulaev Published: July 10, 2026Ilya Strebulaev ranks angels, angel groups, accelerators, and incubators by lifetime U.S. unicorn investments, counting checks written before a company reached unicorn status. The top of the combined list is dominated by organizations: Y Combinator leads with 113 unicorn investments, followed by Plug and Play at 52 and 500 Global at 41. Sand Hill Angels is the highest-ranked angel group at 31.The killer detail is how quickly the list changes below the biggest accelerators. Strebulaev says 271 of the 304 investors in the Top 200 are individuals, or 89%. In the top 100, individuals are 91%. That makes the market underneath the large accelerator counts look much more personal: mostly operators and individual angels writing early checks from their own networks.The pull is the ranking's own caveat. Strebulaev writes that every lifetime leaderboard has a blind spot because many of the unicorns behind those totals were founded a decade or more ago, and some angels have since moved into formal funds, slowed down, or stopped investing. His post therefore separates lifetime performance from recent cohorts, including companies founded in 2015 or later and 2020 or later. For founders or allocators making current decisions, that distinction matters: a career record and a current record are not the same measure.Read more: Ilya StrebulaevAre Prediction Markets Doomed to Fail?Author: Contrary Published: July 16, 2026Contrary argues that prediction markets' current boom depends on whether platforms can prove they are more than regulated gambling with exchange-style branding. Kalshi and Polymarket have reached mass cultural, investor, and regulatory attention, but the article says the underlying idea is old: academic markets, corporate forecasting tools, Intrade, PredictIt, and other predecessors all struggled with the same linked problems of liquidity, legality, and user appeal.The killer detail is the comparison with sportsbooks. Prediction markets present themselves as peer-to-peer, transparent, and non-house-based, but sports contracts reportedly account for more than 90 percent of Kalshi trading, and the article says the platforms keep a much thinner slice of volume than sportsbooks. A market can therefore show sports-betting-scale handle while generating far less revenue.The pull is that the product's hardest problem may be distribution of wins. If a small group of sharp traders captures most profits while casual users lose interest, prediction markets may become valuable data feeds and professional tools before they become durable consumer networks.Read more: SourceRegulationExclusive: The Next Frontier of the Deportation Wars: College CampusesAuthor: Adrian Carrasquillo Published: July 11, 2026Adrian Carrasquillo reports that college campuses are becoming a new front in the fight over immigration enforcement because automatic license plate readers can turn ordinary campus security infrastructure into searchable location data. His thesis is that Flock Safety's camera network, even without direct ICE or DHS contracts, can feed deportation enforcement through local police partnerships and data-sharing practices.The killer detail is the campaign target. The Emergency Campaign to Support Higher Education, working with Schools Drop ICE, is focusing on 75 colleges and universities publicly identified as having Flock contracts. Flock says it has no ICE or DHS contracts, but activists argue the risk comes through local agencies that coordinate with federal authorities and run searches on their behalf.The pull is broader than immigration. Carrasquillo notes that license plate readers have already been abused by officers for stalking, and that Flock's AI search features can identify more than plates, including bumper stickers. A campus safety tool can become a political surveillance system when the data layer is searchable.Read more: The BulwarkThe Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.Author: Todd Phillips Published: July 12, 2026Todd Phillips argues that the Supreme Court's decision in Trump v. Slaughter damaged independent agencies by ending for-cause removal protections, but did not leave Congress powerless. The ruling weakens the old model in which commissioners at bodies such as the FTC, NLRB, CPSC, SEC, and CFTC could be insulated from dismissal over policy disagreements. Phillips says the next fight is whether presidents can turn nominally bipartisan commissions into one-party instruments.The killer detail is the procedural fix: quorum rules. Phillips proposes that Congress require bipartisan slates of commissioners to be seated before independent agencies can act. A president could still fire commissioners, as the Court now permits, but if those firings broke quorum, the agency would be unable to proceed until replacements were confirmed. The guardrail would

This week's video transcript summary is here. You can click on any bulleted section to see the actual transcript. Thanks to Granola for its software.There was an issue with this only going to paid subscribers, so sending it again. Apologies to those who get it twice. I appreciate being paid so feel free to upgrade if you enjoy TWTW.EditorialIntelligence: Who Owns it?This week the word “AI” feels too small.AI is a technology. Intelligence is its product. And if intelligence is the product, the question is no longer just: Which model is best? Who has the cheapest tokens? Who owns the weights? Who controls the data center? Those are important questions, but they are lower in the stack.The bigger question is simpler and more political:Who owns intelligence?That sounds abstract until you make it concrete. Intelligence is becoming something companies can capture, package, serve, meter, route, improve, and sell.It can write code, answer questions, design molecules, automate offices, run agents, draft legal work, advise scientists, serve consumers, and reshape workflows. It is not merely software. It is a general-purpose capability. And all humans could benefit from more of it.General-purpose capabilities have a habit of becoming public questions. But the default answer, that public good is best delivered by government, is the wrong answer in this context.The Product Is IntelligenceWe should stop talking about AI as a feature and start talking about intelligence as the universal thing that is delivered as an input to the world.Water is an input. Electricity is an input. Literacy is an input. Connectivity is an input. Once a society depends on them, access stops being optional. Nobody needs government to build every well, power plant, school, or network. But everybody understands that a civilization cannot be organized around less than universal and reliable access to foundational inputs.Intelligence is reaching that level of importance now that we all know it is real.Government should not own it, operate it, or develop it. Quite the opposite. Companies are the right actors to build fast, compete hard, improve models, serve customers, and discover the real use cases. Self-interest is a useful framing here. Markets are good at finding demand, reducing costs, and turning invention into services people actually use.Companies are the right operators, developers, and owners. But that does not settle the real question of who owns the benefits. That is an economic question.If intelligence becomes metered infrastructure, what happens to the value it creates?The Ownership StackThis week's articles keep circling the same issue from different directions but in the nature of ‘circling' never quite nail it.Jamin Ball's “Own Your Weights” starts with the enterprise version of the question. Owning a model file is not enough. The durable asset is the loop: the data flywheel, the evaluations, the reinforcement system, the workflow learning, and the operating context that lets capability compound.Benedict Evans' “Ways to Think About Token Pricing” adds the market layer. Tokens may become essential, abundant, and cheap, like mobile data. But being essential does not guarantee that the token layer captures the value. The money may move up the stack to whoever owns the workflow, the customer, the distribution, or the application.Alex Karp's fight with the labs, reported in “Alex Karp Is Saying What Every Angry CEO Is Thinking About AI”, is the same argument in sharper enterprise language. Companies are afraid that model providers will not just sell intelligence, but learn from customer workflows and then move into the markets where those workflows create value. The “All-in” group are echoing Karp's view.And “What Is Loop Engineering, and Who Owns It?” names the new contested terrain. The loop is where intelligence meets the world. Whoever owns the loop owns the learning. Whoever owns the learning owns the compounding asset.That is why “who owns intelligence?” is not a slogan. It is the question under the model layer, the application layer, the enterprise layer, and the economic layer.Because intelligence is the product, the tools creating it are fragmented and competitive. So there is no logic in trying to discuss this at the level of a single company or set of tools and models.The Old Promise Was That Commerce Would Tame PowerThe essays this week give the historical backdrop.Deirdre McCloskey, in “What Really Caused the Industrial Revolution”, argues that modern growth came not simply from capital accumulation, but from a change in permission: ordinary people were allowed to innovate, trade, build, and be honored for it.That matters because intelligence could be another expansion of permission. It could make more people capable of building, learning, creating, coding, researching, translating, selling, and coordinating. It could lower the cost of competence.But only if access is broad.Paul Krugman's “AI in an Age of Oligarchy” warns that the same technology lands differently in different political economies. A new general-purpose technology entering a broad, open, upwardly mobile society is one thing. The same technology entering a concentrated economy, with extreme wealth and weak counterweights, is another.Tim O'Reilly's Economist essay, “Elon Musk is building a form of capitalism that Adam Smith would hate”, makes the governance point more directly. The old liberal hope was that commerce would tame arbitrary power. Markets, boards, courts, shareholders, disclosure, and competition would discipline the prince.But what if the prince uses markets to escape discipline?Henry Farrell's “political economy of billionaire derangement” pushes the same point. Founder culture, monopoly ambition, peer rivalry, weak correction mechanisms, and vast private control can amplify appetites rather than restrain them.The danger with intelligence is not that companies build it. They should. Companies build it, meter it, use public tolerance and public infrastructure to scale it, learn from everyone who uses it. All of those things are inevitable and healthy. Market forces will sort out winners from losers. The real danger is that the winners treat all of the surplus produced as purely private.Metered Intelligence Creates SurplusIf metering is not the problem, what is?The problem is pretending that metered intelligence creates value only for the metering entity. Metering water is only tolerated as a public good. If the public were blackmailed by a private water company with the threat of no water we would all rebel.Once we understand that the product of AI is intelligence we can see that every time intelligence is used, there is the immediate transaction: the user pays, the provider serves.But there is also system value. Usage creates signals. Workflows reveal patterns. Prompts, corrections, failures, preferences, integrations, edge cases, and business processes all help define where intelligence is useful and how it should improve. Intelligence breeds intelligence.Even when customer data is contractually protected, the market learns. The platform learns where demand is. The product team learns which workflows matter. The ecosystem learns which jobs are vulnerable, which tasks are automatable, and which parts of the economy can be reorganized around machine intelligence.So the surplus is not born in a vacuum.It rests on public science, public education, public data exhaust, public law, public infrastructure, public energy systems, public tolerance for data centers, and billions of human interactions. It is served by companies, but it is not made only by companies.This is why “Americans Deserve a Dividend From AI Companies' Riches” belongs at the center of this week's issue. The detail can be debated. The principle is harder to dismiss. If intelligence becomes a new foundational resource, then some part of the wealth it creates should flow back to the people whose society makes it possible. Intelligence did not suddenly appear. AI is built on the entire history of human intelligence. It benefits from it and at the same time evolves it.Not Nationalization. A Human Wealth Fund.If intelligence belongs to everybody, some conclude that government ownership of intelligence is the right outcome.Governments are not well suited to build, operate, or improve intelligence. They will move too slowly, regulate too early, politicize the wrong things, and confuse economic participation with operational control.Andrew McAfee's “Why I Didn't Sign the AI Open Letter” is useful here. His objection is not that the technology is unimportant. It is that steering too hard before we understand the shape of the change can become its own failure mode. Marc Andreessen's satire of AI regulation is less policy than temperament, but it captures a real Silicon Valley fear: that regulation can become permission, capture, and incumbency before it becomes wisdom.That fear should be taken seriously.But it does not answer the economic question. It answers only the operational one.How can the economic benefits of intelligence be distributed? The better answer is a sovereign human wealth fund.Call it a sovereign wealth fund if you must, but the phrase is too national. Intelligence will not respect borders. The leading companies are global. The models, chips, data centers, agents, platforms, and workflows will be transnational from the beginning. If the value created by intelligence is global, then the mechanism for sharing some of that value should begin with the companies global enough to capture it. The nice thing about xAI, OpenAI, and Anthropic is that they are supranational.These companies own and operate intelligence. Let them compete. Let them profit. Let them keep the incentives that make the system improve. But if intelligence is the new water, the wealth it creates cannot belong only to the companies that meter it. And they, themselves, have the power to fix it, even more than governments.Access will become a Human Right; Ownership Is the Economic DesignThis is where human rights come in. There is no right to access an AI model, yet. But there will soon be a need to change that.Not as a claim that every person is entitled to every frontier model at every moment for free. That is not serious. Capacity has costs. Models have costs. Inference has costs. Data centers have costs. Although those costs will decline over time, possibly quite quickly as self-learning models address costs.The claim is more basic: in a world where intelligence becomes a primary input into education, work, health, science, citizenship, creativity, and economic agency, baseline access to intelligence starts to look like a civic requirement.That could mean public access layers. It could mean education credits. It could mean open models. It could mean AI dividends. It could mean public-interest compute. It could mean taxes on rents. It could mean a company-initiated human wealth fund that returns some of the upside to society without handing the operating system to the state. The latter could couple wealth growth with universal distribution of ownership.The exact mechanism matters. But the distinction matters more.Government should not own intelligence. It should be universally available. And people should have a claim on the wealth intelligence creates.The Frontier Is Also PhysicalThe abstraction is not weightless.“The Fight Against AI Data Centers Is Just Beginning”, “New York becomes the first state to enact a data center moratorium”, Reuters on pollution from Musk's xAI power project, and DataGravity's “Who Captures Value in AI Infrastructure?” all say the same thing from the ground up.Intelligence uses land. It uses power. It uses water. It uses chips. It uses grid capacity. It uses neighborhoods. It uses public patience.That makes the value question unavoidable. A society can accept the buildout if the buildout is legible as shared progress. It will resist it if the costs are local, the profits are private, and the benefits feel enclosed.Who Owns the “Loop”?The week ends where it began.“Anthropic and Blackstone” are betting that implementation is the next trillion-dollar business. “Vint Cerf” is working on identity for agents on the open internet. “GPT-Red” points toward systems that improve their own robustness. “Kimi K3” adds another open frontier model to the global mix.The model race continues. The deployment race is accelerating. The governance race is behind.My view is this:The central product of this era is intelligence. Companies have figured out how to capture it, package it, serve it, and meter it. That is good. It should stay in the hands of builders who have the incentive to make it better.But intelligence is too foundational to become just another private toll booth. A significant part of it will turn out to be free to users.As intelligence becomes a general-purpose resource, then access to it becomes a human-capability question, and the surplus from it becomes an economic-justice question. Not because government should run it. Because government should not run it. The operating layer belongs with companies. The wealth question belongs with everyone. But companies are best placed to turn that into a process of distribution.The question is not whether companies should build intelligence. They should.The question is whether humanity gets a stake in the wealth created by the thing that may soon become its most important shared input.Contents* Essays* Deirdre McCloskey on What Really Caused the Industrial Revolution* AI in an Age of Oligarchy* Elon Musk is building a form of capitalism that Adam Smith would hate* Murky Mirror: Truth and Consequences* The political economy of billionaire derangement* Is there any “oligarchy” to fight?* AI* Nearly 200 Economists and Tech Leaders Warn of A.I. Threats* Why I Didn't Sign the AI Open Letter* Own Your Weights* Ways to Think About Token Pricing* Alex Karp Is Saying What Every Angry CEO Is Thinking About AI* The AI Agents Are Coming for Microsoft Office* What Is Loop Engineering, and Who Owns It?* The Fight Against AI Data Centers Is Just Beginning* 6 months to live for open models* Americans Deserve a Dividend From AI Companies' Riches* Who Gets to Define the Frontier?* GPT-Red: Unlocking Self-Improvement for Robustness* Anthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just models* Vint Cerf is working on a plan to unleash AI agents on the open internet* xai-org/grok-build, now open source* The Pulse: What can we learn from Bun's rapid Rust rewrite with AI?* Orphan risks at the frontier of artificial intelligence* The Lab of the Future Should Feel Like a Data Center* Why AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”* Kimi K3 Tech Blog: Open Frontier Intelligence* Venture Capital* Three Years In* Venture Has Rarely Looked More Bifurcated* The Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active Now* Are Prediction Markets Doomed to Fail?* Regulation* Exclusive: The Next Frontier of the Deportation Wars: College Campuses* The Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.* India's crackdown on a new WhatsApp feature risks setting a global precedent* Let's build a children's public internet* Computer cops* Google is better at playing the AI regulations game* Infrastructure* Who Captures Value in AI Infrastructure?* New York becomes the first state to enact a data center moratorium* Pollution from Musk's unpermitted xAI power project hits hardest in Black communities* Interview of the Week* The End of the End of Geography* Startup of the Week* Radical AI's Joseph Krause: The Scientist Building The “Waymo” Lab For New Materials* Post of the Week* Marc Andreessen on AI RegulationEssaysDeirdre McCloskey on What Really Caused the Industrial RevolutionYascha Mounk and Deirdre McCloskey | Persuasion | July 11, 2026Yascha Mounk interviews Deirdre McCloskey about her argument that the modern world's economic liftoff came less from capital accumulation than from a change in ideas. McCloskey says both left and right versions of the conventional story rely too heavily on investment: the left stresses exploitation and surplus value, while the right stresses virtuous saving by capitalists. Her objection is historical and economic. Human beings had always invested, from irrigation works and Roman roads to seed grain, and simple accumulation quickly runs into diminishing returns.McCloskey's alternative is that northwestern Europe, first Holland, then Britain and Scotland, and then the North American colonies, developed a liberal ideology that changed who was allowed to innovate and be honored for it. The conversation links that shift to the erosion of inherited hierarchy, the spread of dignity for ordinary commercial life, and a moral vocabulary in which liberalism is not merely procedural but connected to virtues and values. The point is not that machines, coal, trade, and institutions did not matter, but that they do not explain the scale and timing of modern enrichment without a cultural permission structure for innovation.The interview also turns to the contemporary defense of liberalism. Mounk frames the series around the worry that liberalism is often treated as too thin to command allegiance, while its opponents speak more directly to moral passions. McCloskey's case is that liberal societies became rich because they dignified experimentation and ordinary enterprise, and that liberals need to recover the moral language behind that claim.Read moreAI in an Age of OligarchyPaul Krugman | Paul Krugman | July 12, 2026Paul Krugman frames AI as a major technological shock arriving inside an already unequal political economy. The post says AI's economic and social effects may take years to understand, but argues that the setting matters now: America has much greater wealth concentration and political inequality than it did in the 1950s and 1960s, when progressive taxation, stronger regulation, and more active antitrust might have contained some of the destructive effects of a new technology.Krugman's opening claim is that the same technology would likely have different consequences in a more level society. In today's United States, he writes, extreme wealth is both a cause and effect of policies that favor a small elite, including low effective taxes on capital and high incomes, weak enforcement of worker protections and antitrust, and cuts to programs that benefit ordinary Americans.The article is explicitly more about oligarchy than AI. Krugman says the paid sections document the rise of the “.0002%,” the economics and politics of extreme wealth, how oligarchy will shape AI's impact, and possible policy paths. His caveat is that AI itself may still produce a pushback against oligarchy, but absent that, he expects the pre-existing concentration of wealth and power to magnify AI's downsides.Read moreElon Musk is building a form of capitalism that Adam Smith would hateAuthor: Tim O'Reilly Published: July 12, 2026Tim O'Reilly argues that Elon Musk is using the legal forms of shareholder capitalism to escape the restraints that shareholder capitalism was supposed to impose. The article begins with SpaceX's public-market structure: ordinary public investors get little meaningful governance power, Musk keeps roughly 85 percent of the votes through super-voting shares, buyers waive jury trials and class actions, the company qualifies as controlled, and removal of Musk depends on the share class he controls. In O'Reilly's framing, that is not ordinary founder control; it is a design for being answerable to no one, possibly beyond Musk's own lifetime.The killer detail is the article's turn through Albert Hirschman, Montesquieu, James Steuart, Adam Smith, and Keynes. Older defenses of commerce held that markets would tame princely passions because the self-interest of merchants was safer than arbitrary rule. O'Reilly says Musk reverses that hope. The market discipline that was supposed to cage the prince has become the lever by which the prince raises capital, removes feedback loops, and carries private power into politics, government, Mars, robots, AI, or whatever ambition comes next.The pull is the link to AI governance. O'Reilly says corporations are already a kind of artificial intelligence: narrow-input systems that act at a scale no individual human can match. Their partial controls include independent boards, shareholder votes, courts, disclosure, regulators, public pressure, and activism. If the leaders building frontier AI strip those alignment mechanisms out of their own companies, the governance of the company becomes a preview of the governance of the machine.Read more: The EconomistMurky Mirror: Truth and ConsequencesAuthor: Esther Dyson Published: July 14, 2026Esther Dyson argues that today's institutional crisis is better viewed through the 14th century than through recent political history. Using Barbara Tuchman's A Distant Mirror as her frame, she compares a world of famine, plague, church schism, feudal predation, and purposeless war with a present in which institutions again feel brittle, incentives are badly aligned, and power is shifting into forms that are hard to govern.The killer detail is the historical analogy between land, corporations, and AI. Dyson moves from nobles who controlled serfs and territory, to the East India Company as a quasi-sovereign business, to today's AI systems and data centers as a possible new sector that crosses and weakens both nation-states and companies. The question is whether AI becomes a new kind of private land, owned by a new nobility, or an open prairie that many people can cultivate.The pull is human attention. Dyson says the central question is not what AI will do to people, but how people will react to it: whether they can value love, kindness, embodied attention, and artisanal human presence in a world of seductive artificial offerings.Read more: SourceThe political economy of billionaire derangementAuthor: Henry Farrell Published: July 15, 2026Henry Farrell argues that the visible political radicalization of some Silicon Valley billionaires is not a random personality quirk, but a product of the political economy that made them. Starting from Tyler Cowen's dismissal of “billionaire derangement syndrome” and Tim O'Reilly's warning that Elon Musk is using shareholder capitalism to escape shareholder restraint, Farrell flips the phrase: the question is why billionaires themselves can become deranged.The killer detail is Farrell's use of Peter Thiel as both theorist and example. Thiel's Stanford lectures described startups as monarchies and founders as figures vested with unusual power, while Silicon Valley culture rewarded eccentricity, monopoly ambition, and founder exceptionalism. Farrell says those ideas combined with dense founder-investor networks, peer rivalry, and weak correction mechanisms to amplify rather than discipline princely appetites.The pull is the ideological problem for classical liberals who once saw tech wealth as an ally of markets and freedom. Farrell says commerce did not tame the passions; in parts of Silicon Valley, the passions have begun to devour markets, institutions, and the liberal story that justified them.Read more: SourceIs there any “oligarchy” to fight?Matthew Yglesias | Slow Boring | July 16, 2026Matthew Yglesias argues that “oligarchy” is a rhetorically powerful but analytically loose way to describe American politics. The post begins from Bernie Sanders' “Fighting Oligarchy” tour, Amy Klobuchar's warning about a MAGA “broligarchy,” and the long afterlife of the Martin Gilens and Benjamin Page paper that was widely summarized as showing that only the rich matter in policy outcomes. Yglesias says the evidence supports a weaker claim: affluent people and business leaders have unusual access and influence, but that is not the same as rule by a small cabal.His main distinction is between inequality and oligarchy. The Gilens-Page measure treated the top 10 percent of households as “the wealthy,” and later critics found that rich and middle-class preferences usually align; in the cases where they differ, the rich win about 53 percent of the time. Yglesias also says business executives get special access partly because their decisions are materially important to communities, jobs, investment, and local tax bases, not only because of campaign donations.The post preserves Jerusalem Demsas' counterpoint from their podcast discussion: privileged donor and business access can still violate democratic equality even if the oligarchy label overstates the structure of power. Yglesias' narrower claim is that Democrats should be precise about what problem they are trying to solve, because donor influence can also push the party left on climate and cultural issues in ways that alienate many voters.Read more: Slow BoringAINearly 200 Economists and Tech Leaders Warn of A.I. ThreatsAuthor: Ben Casselman Published: July 13, 2026Ben Casselman reports on “We Must Act Now,” a statement warning that artificial intelligence could transform the economy faster than any previous technology and that policymakers need to move faster to understand and respond. The statement says AI may become radically more powerful over the next 10 years, bringing risks such as large-scale job displacement as well as opportunities such as higher living standards. Nearly 200 people signed, including 15 Nobel laureates, the chief economists of OpenAI and Anthropic, Anthropic co-founder Jack Clark, former Google CEO Eric Schmidt, and venture capitalist Vinod Khosla.The killer detail is who joined the warning. Casselman notes that the signatories include economists who have historically been skeptical of Silicon Valley's most dramatic AI job-loss forecasts, including Daron Acemoglu and Simon Johnson, the MIT professors who won the 2024 Nobel in economics. Erik Brynjolfsson, who helped organize the statement, says there has been a notable change in the profession and that economists and policymakers are not ready for the “tsunami” he sees coming.The pull is the measurement problem. The statement does not offer a specific policy menu, but calls for economists, policymakers, and industry leaders to understand the economics of transformative AI and steer it toward complementing humans. Brynjolfsson says one high priority is better data on AI's spread and impact, because current measures tell conflicting stories about job losses and which workers are most exposed.Read more: The New York TimesWhy I Didn't Sign the AI Open LetterAuthor: Andrew McAfee Published: July 13, 2026Andrew McAfee explains why he did not sign “We Must Act Now,” the AI economy statement organized in part by his longtime collaborator Erik Brynjolfsson. McAfee agrees with the letter's starting point that AI is likely to become radically more powerful over the next decade and that it is a general-purpose technology. His objection is not to urgency or to studying AI's economic effects, but to the framing of risk, displacement, and institutional steering as the first move.The killer detail is McAfee's line edit. He says the original letter comes close, then “bounces off the crossbar” by calling for incentives, guardrails, and institutions to steer AI before we know enough about its actual impacts. He points to mixed current evidence: labor-market canaries, but also rising software job postings, low unemployment for younger workers, rising real median income, and claims that AI-adopting companies are adding workers faster than low-adopting peers. His worry is that the letter leans toward upstream governance and dirigisme when the evidence may call for capability building instead.The pull is his replacement statement. McAfee keeps the three-paragraph structure but changes the emphasis: AI is likely to become radically more powerful; like earlier world-changing technologies it will raise living standards while also bringing harms and shocks; and economists, policymakers, and technology leaders should build the capabilities to respond quickly and effectively. It is a concise version of the permissionless-innovation case inside the AI policy debate.Read more: The Geek WayOwn Your WeightsAuthor: Jamin Ball Published: July 10, 2026Jamin Ball argues that the enterprise AI debate about whether companies should “own their weights” or rent models from frontier labs is asking too narrow a question. A model weight file gives a company control over a point-in-time artifact, but not durable control over the capability stack. In his framing, the weight file is a melting ice cube: it does not get worse in absolute terms, but it falls behind as frontier systems improve and enterprise needs change.The killer detail is what Ball says companies really need to own: the data flywheel, reinforcement learning infrastructure, and evaluation harness that produce and improve the model. Simply deploying an open-weights model and declaring sovereignty leaves the enterprise with yesterday's capability and no way to compound workflow-specific learning.The pull is that enterprise AI control may be less about model ownership than operating ownership. The defensible layer is the system that turns company data, edge cases, business definitions, and evaluations into continuously improving performance.Read more: Clouded JudgementWays to Think About Token PricingAuthor: Benedict Evans Published: July 9, 2026Benedict Evans argues that today's AI token prices are a temporary signal from a supply-constrained market, not a reliable guide to long-term value capture. The open question is whether foundation models keep durable pricing power or become commodity infrastructure as data-center capacity, inference efficiency, and model competition all shift. His current read is that the visible market dynamics point toward commoditization unless something materially changes.The killer detail is the mobile data analogy. Evans says cellular networks became a trillion-dollar industry with hundreds of billions in capex after data usage exploded, but carrier stocks went nowhere because value moved up the stack. Tokens may behave similarly: an opaque unit tied to marginal cost, sold through bundles, essential to everything, yet not necessarily where profits accrue.The pull is uncertainty, not prediction. Evans lists paths to model dominance, including network effects, less competition, regulation, export controls, or a lab pulling ahead on execution, but says each requires a new fact not yet visible. Without that change, the model layer looks more like infrastructure beneath the products that capture value.Read more: SourceAlex Karp Is Saying What Every Angry CEO Is Thinking About AIAuthor: Tim Higgins Published: July 11, 2026Tim Higgins reports that Palantir CEO Alex Karp has turned corporate frustration with AI labs into a public argument about enterprise control. Palantir released a white paper, “Institutional Sovereignty in the Age of AI,” laying out steps companies and governments can take to protect themselves from OpenAI, Anthropic, and other foundation-model providers. The article links that paper to Karp's CNBC appearance, where he said “something has gone completely wrong” in the relationship between AI labs and customers and argued that enterprises are paying for tokens that create little value.The killer detail is the value-capture question. Higgins writes that Karp's critique has resonated because AI labs may gain power and insight from customer data, workflows, and decision-making, even when enterprise policies say customer data are not used for training. David Sacks amplified the concern by arguing that Anthropic is moving from the model layer into vertical applications such as science, security, legal, and coding, raising the fear that model providers will watch where value is being created and then move into those markets directly.The pull is that Karp is not alone, even if his style is unusually combative. Higgins notes that Satya Nadella has also warned that companies need to retain the learnings created when they use AI models, while Mark Zuckerberg has framed Meta's new model release partly around lower-cost frontier intelligence. The article presents Karp's campaign as one sign that established technology companies and large enterprises are trying to define where they fit when AI labs become central infrastructure, application competitors, and potential IPO giants at the same time.Read more: The Wall Street JournalThe AI Agents Are Coming for Microsoft OfficeAlex Wilhelm | Cautious Optimism | July 11, 2026Alex Wilhelm argues that one of the week's quieter AI questions is whether the productivity market that Microsoft successfully moved into subscription software is now being attacked by agentic tools. The piece begins with the infrastructure backdrop: SK Hynix raised $26.5 billion in a U.S. listing while building U.S. HBM and advanced-packaging capacity, and memory, chip, and foundry companies are now priced for sustained AI demand.Wilhelm then says the AI conversation has shifted quickly from raw capability to cost per task. He cites new model releases and vendor language emphasizing cheaper agentic and coding models, faster performance, and lower dollars per task. That matters because lower costs make it more plausible for AI systems to take on routine knowledge work at scale rather than remain a premium coding assistant market.The core of the article is Microsoft Office. Wilhelm notes that Microsoft turned Office from a one-time purchase into Microsoft 365, a large recurring revenue business with tens of millions of subscribers and a major productivity segment. Now, he says, late-stage unicorns and AI labs are pushing into the same territory: Anthropic's Cowork was reportedly used mostly outside software development, OpenAI merged ChatGPT and Codex into a tool for creating sheets, slides, docs, web apps, and long-running work, and other companies are building agentic coworkers that connect business data to documents, workflows, schedules, alerts, and apps.The article's caveat is that Microsoft has survived major platform shifts before. The argument is not that Office disappears quickly, but that the definition of office software is broadening from documents and spreadsheets into AI systems that can create, monitor, and act across workplace data.Read moreWhat Is Loop Engineering, and Who Owns It?Author: Nilesh Barla Published: July 11, 2026Nilesh Barla argues that “loop engineering” is becoming a distinct discipline because production AI agents now fail less at single prompts than at runtime: when to stop, what state to preserve, and how to recover after a bad step. Prompt engineering shapes one model call, and context engineering shapes what the model sees, but loop engineering shapes what a sequence of calls actually does.The killer detail is the three-primitives frame. Barla says a real agent loop needs halt conditions, state carryover, and recovery paths, then maps teams across five maturity levels. At the lowest level, an agent is just a model call in a for-loop with a step cap and raw history; by the higher levels, the system has structured state, explicit planning, replay, evaluation, and self-repair.The pull is organizational. If agents are becoming production systems rather than demos, someone has to own the runtime itself. The loop engineer is the role Barla gives to the person responsible for making long-running agent work dependable.Read more: Adaline LabsThe Fight Against AI Data Centers Is Just BeginningEmma Roth | The Verge | July 12, 2026Emma Roth argues that community resistance to data centers has moved from an early warning sign into a national political fight as AI facilities grow larger, more power-hungry, and more visible to nearby residents. The article starts with Apple's failed 2015 plan for a $1 billion data center in Athenry, Ireland, where a small group of residents challenged the project over noise, light pollution, flooding, traffic, and wildlife effects until Apple abandoned it in 2018.The current data-center buildout is presented as much larger and more contentious. Roth writes that residents now cite rising energy costs, water quality, noise, light pollution, and greenhouse gas emissions, while the U.S. Energy Information Administration expects commercial energy demand to surpass residential demand this year because of AI data centers and Goldman Sachs expects data-center power demand to double by 2027.The central evidence comes from Data Center Watch, which says protesters blocked or delayed at least 75 U.S. projects worth $130 billion from January to March, with active opposition groups more than doubling from 396 at the end of 2025 to 833 by the end of the first quarter of 2026. Roth also cites QTS abandoning a $12 billion Wisconsin campus, Delaware City regulators blocking a 580-acre project under the Coastal Zone Act, opposition stopping a QTS project in Prince William County, and pressure that pushed Kevin O'Leary to downsize the proposed 40,000-acre Project Stratos in Utah.The policy section describes a split between federal acceleration and local resistance. President Trump has treated data centers as part of the AI race with China and fast-tracked construction, while some Republican candidates are distancing themselves from that position ahead of midterms. Sanders and Ocasio-Cortez have proposed a moratorium until price and environmental protections exist, bipartisan lawmakers are backing ratepayer-protection measures, and states including Florida, Idaho, and Washington have passed rules on cost shifting, water use, and tax breaks. Roth's caveat is that the policy patchwork is still incomplete, leaving many communities to fight project by project.Read more6 months to live for open modelsAuthor: Nathan Lambert Published: July 12, 2026Nathan Lambert argues that open-weight AI models are facing their most serious policy test so far because U.S. officials are beginning to discuss concrete controls rather than abstract safety concerns. He says reported White House conversations about a new executive order may initially target Chinese-origin models and government use, but could create a broader review habit for frontier open models. His forecast is that a model above the capability range of GPT-5.5, Claude Opus 4.8, or GLM-5.2 could trigger a ban or indefinite delay within six months.The post separates two policy fights that are becoming intertwined: distillation and frontier capability. Lambert says the distillation campaign against Chinese models has become a form of regulatory capture because Anthropic and other closed-model companies would gain economically if Chinese open models were banned. He does not dismiss IP protection, but argues that if a closed model's capabilities are dangerous enough to justify restricting open models, the lab also has to explain why those capabilities are exposed through a queryable API. He cites unauthorized access to Anthropic's Mythos private beta as evidence that APIs are not automatically secure.The broader claim is that a unilateral U.S. ban would hurt positive actors more than bad actors if comparable open models remain available elsewhere. Lambert says the only durable ceiling would require global agreement, which does not exist, and that open models can improve safety by allowing broad inspection, adaptation, and understanding. His proposed near-term off-ramps are a strong U.S. open model release from companies such as Microsoft, Meta, or Reflection, and a broader coalition of open-source beneficiaries lobbying for safe rollout rather than prohibition.Read more: SourceAmericans Deserve a Dividend From AI Companies' RichesAuthor: Scott Stanford Published: July 14, 2026Scott Stanford argues that proposals to give the government a stake in AI companies miss the point unless ordinary citizens directly receive and control the upside. Sam Altman has discussed giving up equity in OpenAI, Washington already owns a stake in Intel, Nvidia is sharing China chip revenue, and Bernie Sanders wants large AI labs to contribute half their stock to a sovereign wealth fund. Stanford says those ideas all park value with the state, not with people.The killer detail is New Carlisle, Indiana, where AWS's Project Rainier is turning cornfields into one of the world's largest AI superclusters. The project is planned to run up to a million chips, draw more than two gigawatts of power, and represents an investment that has grown from $11 billion to $13.8 billion. Stanford uses that local transformation to argue that AI's public bargain should be visible at the household level.The pull is design. A citizen AI dividend would have to specify who earns a stake, how they hold it, and when they see cash. Without that mechanism, the AI wealth debate remains a fight over government balance sheets rather than public ownership.Read more: SourceWho Gets to Define the Frontier?Author: Mark Daley Published: July 14, 2026Mark Daley argues that Demis Hassabis is right to call for a serious institution to verify frontier AI systems, but that the power to test models is also the power to govern them. Hassabis's proposed Frontier AI Standards Body would get privileged pre-release access to advanced models, testing compute, held-out evaluations, support from national labs and security agencies, third-party auditors, and eventually authority to block models from the American market or coordinate a slowdown.The killer detail is Daley's constitutional objection. He says the proposal sometimes looks like a scientific lab, a standards body, an industry regulator, a licensing authority, and an emergency security council at once. Combining those roles because each requires technical expertise would be like putting the central bank, auditor-general, and Supreme Court in one building and calling it efficient.The pull is standard-setting. Daley's concern is not that verification is unnecessary, but that whoever writes the tests, decides what passes, adjudicates disputes, and grants market access may end up defining the frontier itself.Read more: SourceGPT-Red: Unlocking Self-Improvement for RobustnessOpenAI | OpenAI | July 15, 2026OpenAI describes GPT-Red as an internal automated red-teaming model trained to find prompt-injection vulnerabilities at a scale human red teams cannot match. The post says AI systems increasingly encounter third-party data through browsers, connected apps, local files, and tools, creating opportunities for malicious instructions hidden in emails, webpages, tool responses, or code repositories. Human red-teaming remains part of OpenAI's safety process, but the company says it is time-intensive and cannot generate enough diverse adversarial examples for model training.The system is trained through self-play reinforcement learning, with GPT-Red rewarded for eliciting valid failures and defender models rewarded for resisting attacks while still completing their tasks. OpenAI says the training environments specify threat models across settings such as local files, webpage banners, email bodies, and tool outputs. The model is kept separate from deployed production models because it is intentionally trained with malicious capabilities.OpenAI reports that GPT-Red generalized beyond its training set, including an internal replication of the indirect prompt-injection arena from Dziemian et al. (2025), where it found successful attacks in 84% of scenarios compared with 13% for human red-teamers. The post also says GPT-Red transferred attacks from simulation to a live autonomous vending-machine agent, causing price changes and order cancellations, and outperformed a prompted GPT-5.5 baseline against a Codex CLI agent on held-out data-exfiltration tasks.The article's main robustness claim is that OpenAI has used GPT-Red and predecessor models in training since GPT-5.3, with later GPT releases becoming more resistant to prompt injections. It says GPT-5.6 Sol has six times fewer failures on OpenAI's hardest direct prompt-injection benchmark than the best production model from four months earlier, that a “Fake Chain-of-Thought” attack class fell from more than 95% success against GPT-5.1 to below 10% against GPT-5.6 Sol, and that GPT-5.6 Sol fails on only 0.05% of GPT-Red's direct prompt injections. OpenAI says general capabilities and targeted over-refusal evaluations were not harmed, and says a preprint with more details will follow.Read moreAnthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just modelsRebecca Bellan | TechCrunch | July 15, 2026Rebecca Bellan reports that Ode with Anthropic is the $1.5 billion AI implementation company launched by Anthropic with Blackstone, Hellman & Friedman, Goldman Sachs, and other backers. The article says the venture reflects a growing belief among frontier AI labs that enterprise adoption requires more than better models: customers need engineers who can embed inside businesses and turn AI into working systems.Ode was originally conceived by Blackstone after it used both large consulting firms and smaller AI services boutiques across its portfolio companies. TechCrunch reports that Fractional AI, an AI engineering services startup, stood out and was acquired by the joint venture shortly after the venture was announced. Fractional now forms the foundation of Ode, which has 100 engineers and works closely with Anthropic's applied AI team to identify where the technology can affect specific businesses.Ode CEO Chris Taylor tells TechCrunch that the company could someday become a trillion-dollar business if it scales without losing quality. He says an ideal customer is one whose CEO treats the AI project as a top one or two priority, whether it is a major product feature or the reworking of a core business process. Ode will operate under a “Claude-first” principle, using Anthropic technology whenever possible, but the article says it can use rival AI products when needed.The article's central implementation argument comes from Ode chief technologist Eddie Siegel, who says model selection matters but is not where most of the engineering effort goes. He compares it to the choice of programming language in software: one ingredient in a system that still has to be engineered. Bellan writes that Ode's challenge is hiring and training enough elite generalist engineers, many of them former founders, while competing with OpenAI's The Deployment Company and consulting giants that have built their own forward-deployed engineering teams.Read moreVint Cerf is working on a plan to unleash AI agents on the open internetTim Fernholz | TechCrunch | July 15, 2026Tim Fernholz reports that Vint Cerf, after leaving Google, is advising Innovation Labs on an open architecture for identifying AI agents online. Innovation Labs is a subsidiary of Identity Digital, a DNS registry company, and its proposal is to use domain-name infrastructure as part of a system for agent identity, accountability, and auditability. The premise is that agents will need a way to identify themselves if they move beyond proprietary systems and begin interacting across the open internet.The concrete proposal is DNSid, a registry that links an AI agent to an existing internet domain and uses cryptographic proofs to log its registration over time. Innovation Labs says it is trialing the standard with unnamed hyperscalers and identity companies. Cerf frames the problem around authority and accountability: what authority an agent has, where that authority came from, who is accountable for the agent's behavior, how its identity is established, and why anyone should trust it.The article's caveat is that standards are still emerging and agents are more active than static domains. Cerf says the period may be both fascinating and exasperating because the functionality is powerful and interoperability is unresolved. He compares the adoption problem to TCP/IP: competing systems may not work together until users push for functional interoperation. He also says an agentic economy is not inevitable, but that people will try to build it because delegating work to agents will be easier.Read more: TechCrunchxai-org/grok-build, now open sourceAuthor: Simon Willison Published: July 15, 2026Simon Willison argues that xAI's decision to open-source Grok Build is best understood as a trust repair move after a severe privacy failure. The CLI had triggered backlash when users realized that running it in a directory could upload the entire directory to xAI's Google Cloud buckets, including one user's reported SSH keys, password manager database, documents, photos, and videos. xAI disabled the feature, said previously retained coding data would be deleted, and released the code under Apache 2.0.The killer detail is what the codebase reveals. Willison counts 844,530 lines of Rust, only about 3% of which appears vendored, and finds remnants of the upload system still present but disabled: gcs.rs contains Google Cloud upload code, while upload_session_state() now returns a hard-coded session_state_upload_unavailable error. He also notes copied or ported tool implementations from Codex and OpenCode, prompt files, and a terminal Mermaid renderer.The pull is that terminal coding agents are becoming large, intricate software systems in their own right. The privacy failure mattered because these tools operate inside the directories where developers keep their most sensitive work; the open-source release matters because trust now depends on inspecting what an agent can see, send, and do.Read more: SourceThe Pulse: What can we learn from Bun's rapid Rust rewrite with AI?Author: Gergely Orosz and Ivan Klaric Published: July 16, 2026Gergely Orosz and Ivan Klaric argue that Bun's AI-assisted rewrite from Zig to Rust is a practical sign of how software engineering changes when models can take on large, bounded migrations with clear feedback loops. The piece does not treat the rewrite as magic: Jarred Sumner first spent hours turning design judgment into a detailed porting guide, then used adversarial review, parallel agents, compiler errors, and tests to force the work toward correctness.The killer detail is the scale. Bun had 535,496 lines of Zig, 1,448 files, and 22 million monthly downloads, making a conventional rewrite a year-long freeze the team could not justify. Using Fable, Sumner split the work across 64 agents, produced about 6,500 commits, and got the migration done in 11 days at an estimated API cost of $165,000.The pull is economic, not theatrical. If a one- or two-year migration can become an 11-day project, AI coding is not just faster autocomplete; it changes which technical debts are worth paying down.Read more: SourceOrphan risks at the frontier of artificial intelligenceAuthor: Andrew Maynard Published: July 16, 2026Andrew Maynard argues that frontier AI safety frameworks are creating “orphan risks”: harms that companies can see, but do not formally own because they are hard to quantify, do not fit catastrophic-risk thresholds, or fall outside audit-friendly compliance machinery. His target is not existing frontier safety work, but the narrowing effect that happens when private companies decide which risks count as governable.The killer detail is Maynard's contrast between measurable model dangers and threats to value. He points to Meta's three-day Galactica collapse, OpenAI's 2023 board crisis, safety-team departures, and wellbeing litigation as examples of risks that damaged trust, culture, legitimacy, or users without fitting cleanly into conventional model-risk categories. The proposed fix is an orphan-risk register: a public record of risks a company considered and chose not to manage, with reasons.The pull is accountability. Frontier developers' internal scoping choices have become a de facto layer of public governance, so the question is no longer only which risks they manage, but which risks they quietly leave outside the frame.Read more: SourceThe Lab of the Future Should Feel Like a Data CenterLatent.Space with Andy Beam and Rafa Gomez-Bombarelli | Latent.Space | July 16, 2026Latent.Space interviews Lila Sciences CTO Andy Beam and chief science officer for physical sciences Rafa Gomez-Bombarelli about the company's attempt to build an AI-run science factory. The post describes Lila's thesis as treating the lab itself as an “infinite token generator”: if internet data drove the first era of AI scaling, experimentally verified scientific data may be the next scarce training source. Lila is trying to produce that data with robotics, lab instruments, orchestration software, and AI models wired into the wet lab.The central analogy is the lab as data center. Instruments are nodes on a graph, a magnetically levitating transport layer moves materials between them, and experiment scheduling looks like a compute queue. Beam says Lila is not simply an automation company, because the point is not just throughput; it is flexibility, generalization, and experiment capture. The post says Lila has built more than 10 trillion experimentally validated “scientific reasoning tokens,” not internet text or biological sequences.The interview ranges across biology, chemistry, drug discovery, materials science, and the limits of automation. It notes that Lila rebuilt one gas-sorption measurement to run roughly 2,500 times faster, claims its general models can transfer priors from small-molecule chemistry to metal-organic frameworks for carbon capture, and describes model-suggested platinum-group-free electrocatalysts that moved from looking boring or wrong to becoming strong performers. The caveats are physical: experiments have runtimes, biology cannot always be accelerated, chains of thought can be unreliable narrators, and reward hacking becomes more dangerous when a model controls a real lab.Read more: Latent.SpaceWhy AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”Kate Park | TechCrunch | July 16, 2026Kate Park interviews AMI Labs CEO Alexandre LeBrun about why Yann LeCun's world-model startup avoids the language of “AGI” and “superintelligence.” LeBrun says the terms are not useful because they lack stable definitions: “We never used the word AGI. And I just noticed that nobody is using it anymore; they switched to superintelligence.” His argument is that the practical frontier is not a label, but whether AI systems can understand and predict real-world states.The article explains the world-model thesis by contrasting language prediction with physical-state prediction. A large language model predicts the next word; a world model predicts the next state, such as what happens when a glass tips over. LeBrun says LLMs remain complementary and efficient for language, but the physical world is where current AI is weak. Robotics is the clearest case: hardware has advanced quickly, but robots are still brittle outside controlled routines because they lack context and situational understanding.AMI is still pre-product, but TechCrunch reports that LeBrun was in Seoul looking for industrial partners, researchers, and global companies. He says world models cannot be built entirely inside a lab because they need access to real environments. That is why South Korea appeals to AMI: robotics, semiconductors, manufacturing, and fast adoption create the kind of hardware-heavy context that software-only AI has barely touched.Read more: TechCrunchKimi K3 Tech Blog: Open Frontier IntelligenceKimi | Kimi | July 16, 2026Kimi introduces Kimi K3 as an open 3T-class frontier model aimed at coding, knowledge work, reasoning, multimodality, and long-context agentic use. The source describes the model as a 2.8T-parameter system built on Kimi Delta Attention and Attention Residuals, with native multimodality and a 1M-token context window. It says Moonshot AI plans to release model weights by July 27.The post presents K3 through benchmark and use-case sections rather than as a general product announcement. It reports results across coding, productivity, agentic, and multimodal evaluations, including DeepSWE, Terminal-Bench 2.1, Program Bench, SWE Marathon, FrontierSWE, PostTrain Bench, OfficeQA Pro, SpreadsheetBench 2, MCP Atlas, AutomationBench, BrowseComp, GDPval-AA v2, AA-Briefcase, MMMU-Pro, MathVision, BabyVision, OmniDocBench, and PerceptionBench. The source says all reported K3 results use maximum reasoning effort with temperature and top-p set to 1.0, and that different benchmark comparisons use KimiCode, Claude Code, or Codex harnesses depending on the test.Kimi's caveats are unusually concrete. The limitations section says K3 was trained in preserved thinking-history mode, so quality may become unstable if an agent harness does not pass historical thinking content correctly or if an ongoing session switches to K3 midstream. It also says K3's emphasis on long-horizon tasks can make it excessively proactive when it encounters minor issues or ambiguous intent, and recommends imposing explicit behavioral constraints for applications that require strict boundaries. The post adds that K3 remains behind Claude Fable 5 and GPT 5.6 Sol in user experience despite being competitive overall.Read moreVenture CapitalThree Years InAuthor: Tomasz Tunguz Published: July 10, 2026Tomasz Tunguz marks Theory Ventures' third anniversary by arguing that AI's central market effect is time compression. In his telling, model release cycles, company revenue milestones, enterprise adoption, and venture categories have all accelerated. Seed, Series A, and Series B still exist as financing labels, but they no longer cleanly describe company maturity when some seed rounds are larger than IPOs and the best AI companies can mature much earlier than prior software companies.The killer detail is the shift from models to inference. Tunguz argues that inference has become the dominant AI market because workloads and buyer preferences are fragmenting: video, batch, local, agentic, and real-time tasks each create different infrastructure needs. He compares this to databases splitting into OLTP, OLAP, vector, and streaming categories, with AI pushing the same specialization into inference infrastructure.The pull is that Theory sees the AI-native venture firm as part of the same pattern. The firm says it has analyzed twice as many investment opportunities with three investors working alongside a nine-person intelligence organization, using agents and research systems to map markets, source companies, and support diligence. The piece is both a market map and a statement about how venture itself is being rebuilt by the technology it funds.Read more: LinkedInVenture Has Rarely Looked More BifurcatedAuthor: Beezer Clarkson Published: July 14, 2026Beezer Clarkson points to PitchBook's Q2 report as evidence that the U.S. venture market has split into two very different realities. AI now accounts for more than 60 percent of all U.S. venture deal value, meaning the headline market can look active and well-funded even while much of the non-AI market is dealing with a much colder liquidity and fundraising environment.The thread uses that split as the setup for Clarkson's latest Origins episode with Alec Litowitz, founder of Magnetar and QStar Capital and one of Citadel's original founding partners. Clarkson says markets like this are periods of genuine uncertainty, not merely ordinary risk, which is why Litowitz's Adaptability Quotient framework is relevant.The embedded clip makes the liquidity point concrete. Litowitz says DPI is “the resolution of uncertainty” because it converts an uncertain investment into actual cash returned to LPs. In his framing, a realized dollar is a real mark, while TVPI remains uncertain until it is realized.The killer detail is the distinction between pricing risk and resolving uncertainty. Litowitz's perspective matters because QStar is a SpaceX investor and Clarkson says the conversation happened just before one of venture's most consequential IPOs. The episode's stated questions are why venture remains a way to gain exposure to innovation, how AI is changing what is investable, why liquidity is ultimately a function of time, and why uncertainty requires a different decision framework from risk.Read more: XThe Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active NowAuthor: Ilya Strebulaev Published: July 10, 2026Ilya Strebulaev ranks angels, angel groups, accelerators, and incubators by lifetime U.S. unicorn investments, counting checks written before a company reached unicorn status. The top of the combined list is dominated by organizations: Y Combinator leads with 113 unicorn investments, followed by Plug and Play at 52 and 500 Global at 41. Sand Hill Angels is the highest-ranked angel group at 31.The killer detail is how quickly the list changes below the biggest accelerators. Strebulaev says 271 of the 304 investors in the Top 200 are individuals, or 89%. In the top 100, individuals are 91%. That makes the market underneath the large accelerator counts look much more personal: mostly operators and individual angels writing early checks from their own networks.The pull is the ranking's own caveat. Strebulaev writes that every lifetime leaderboard has a blind spot because many of the unicorns behind those totals were founded a decade or more ago, and some angels have since moved into formal funds, slowed down, or stopped investing. His post therefore separates lifetime performance from recent cohorts, including companies founded in 2015 or later and 2020 or later. For founders or allocators making current decisions, that distinction matters: a career record and a current record are not the same measure.Read more: Ilya StrebulaevAre Prediction Markets Doomed to Fail?Author: Contrary Published: July 16, 2026Contrary argues that prediction markets' current boom depends on whether platforms can prove they are more than regulated gambling with exchange-style branding. Kalshi and Polymarket have reached mass cultural, investor, and regulatory attention, but the article says the underlying idea is old: academic markets, corporate forecasting tools, Intrade, PredictIt, and other predecessors all struggled with the same linked problems of liquidity, legality, and user appeal.The killer detail is the comparison with sportsbooks. Prediction markets present themselves as peer-to-peer, transparent, and non-house-based, but sports contracts reportedly account for more than 90 percent of Kalshi trading, and the article says the platforms keep a much thinner slice of volume than sportsbooks. A market can therefore show sports-betting-scale handle while generating far less revenue.The pull is that the product's hardest problem may be distribution of wins. If a small group of sharp traders captures most profits while casual users lose interest, prediction markets may become valuable data feeds and professional tools before they become durable consumer networks.Read more: SourceRegulationExclusive: The Next Frontier of the Deportation Wars: College CampusesAuthor: Adrian Carrasquillo Published: July 11, 2026Adrian Carrasquillo reports that college campuses are becoming a new front in the fight over immigration enforcement because automatic license plate readers can turn ordinary campus security infrastructure into searchable location data. His thesis is that Flock Safety's camera network, even without direct ICE or DHS contracts, can feed deportation enforcement through local police partnerships and data-sharing practices.The killer detail is the campaign target. The Emergency Campaign to Support Higher Education, working with Schools Drop ICE, is focusing on 75 colleges and universities publicly identified as having Flock contracts. Flock says it has no ICE or DHS contracts, but activists argue the risk comes through local agencies that coordinate with federal authorities and run searches on their behalf.The pull is broader than immigration. Carrasquillo notes that license plate readers have already been abused by officers for stalking, and that Flock's AI search features can identify more than plates, including bumper stickers. A campus safety tool can become a political surveillance system when the data layer is searchable.Read more: The BulwarkThe Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.Author: Todd Phillips Published: July 12, 2026Todd Phillips argues that the Supreme Court's decision in Trump v. Slaughter damaged independent agencies by ending for-cause removal protections, but did not leave Congress powerless. The ruling weakens the old model in which commissioners at bodies such as the FTC, NLRB, CPSC, SEC, and CFTC could be insulated from dismissal over policy disagreements. Phillips says the next fight is whether presidents can turn nominally bipartisan commissions into one-party instruments.The killer detail is the procedural fix: quorum rules. Phillips proposes that Congress require bipartisan slates of commissioners to be seated before independent agencies can act. A president

Prototypes and demoware.That was Monumental two years ago, at their $25m Series A.Yesterday they announced a $32m Series B led by Khosla Ventures. In between, their robots laid the entire brickwork on 100 houses, plus canal walls and commercial buildings, as a subcontractor carrying full liability for the work.The day after the announcement, co-founder Salar al Khafaji sat down with us.In his words:"The Series A was basically still like an R&D round... we had some prototypes, we had some like demoware basically.""The Series B was raised off actual success."And the line that will start arguments:"Everyone is obsessed with speed. Speed doesn't really matter."A bricklaying robot company telling you speed doesn't matter. His reasoning is worth the listen on its own.Also in this one:> Why SAM and Hadrian X were too early, and what changed> Day zero to day one of a robot crew arriving on site> The economics vs a traditional bricklaying gang> UK going commercial, US pilots starting this year> Why the entire facade is next. Not in a decade, within a couple of years.

Good morning from Pharma Daily: the podcast that brings you the most important developments in the pharmaceutical and biotech world. Today, we delve into a series of significant advancements and challenges shaping the landscape of these dynamic sectors. Starting with Ipsen's Dysport, which has made notable strides in its Phase 3 trials for migraine prevention. The trials covered both episodic and chronic conditions, marking a first in the neurotoxin market. Dysport's success positions it as a formidable competitor to AbbVie's Botox, expanding therapeutic options for individuals battling migraine disorders. This achievement showcases the potential efficacy of botulinum toxin-based therapies in neurology and pain management, offering promising new avenues for patient care. In regulatory news, Boehringer Ingelheim has received approval from the Medicines and Healthcare products Regulatory Agency (MHRA) for Jascayd, a small molecule PDE4B inhibitor with antifibrotic properties. This approval marks a significant milestone in the treatment of idiopathic and progressive pulmonary fibrosis. Jascayd's addition to the therapeutic arsenal offers new hope for managing this debilitating condition, emphasizing the ongoing efforts to improve patient outcomes through innovative treatments. The arena of business development sees HanChorBio partnering with InxMed to advance oncology research. By combining HCB101 with FAK inhibitors and FAP-targeted ADCs, this collaboration aims to leverage antibody and small molecule drug discovery techniques. The goal is to develop innovative cancer treatments that could redefine therapeutic approaches in oncology. Keenova Therapeutics has also reported success with Xiaflex for plantar fibromatosis. This enzyme injection therapy offers a novel approach by targeting collagen, thus providing an innovative solution for musculoskeletal conditions. Similarly, Fate Therapeutics' FT819, an off-the-shelf CAR-T therapy, has shown early promise in tackling treatment-resistant systemic sclerosis, underscoring the potential of cell therapies beyond oncology. Meanwhile, MindRank's successful Series B funding round of $52 million highlights the growing role of AI platforms in drug discovery. The funding will propel its AI-discovered oral GLP-1 obesity pill into Phase III trials, exemplifying how technology-driven solutions are gaining traction in addressing metabolic diseases like obesity. On the regulatory front, Saol Therapeutics has resubmitted SL1009 (DCA) to the FDA for pyruvate dehydrogenase complex deficiency. This submission underscores ongoing efforts to address rare metabolic disorders using small molecule therapies. Additionally, Sanofi's concessions to the EU regarding flu vaccine marketing illustrate the complexities of competitive dynamics and regulatory scrutiny within the vaccine market. However, not all developments are favorable. AstraZeneca and Ionis Pharmaceuticals faced a setback as their drug Wainua failed its Phase 3 trial for transthyretin-mediated amyloid cardiomyopathy. This outcome highlights the challenges inherent in developing effective treatments for complex cardiovascular conditions. Meanwhile, regulatory processes remain contentious as the FDA pauses its release of complete response letters amid debates over proprietary information disclosures. In another noteworthy development, GSK has terminated its $2.2 billion collaboration with Alector after underwhelming results from Alzheimer's drug trials. This decision highlights both financial implications and strategic shifts as companies reassess risk tolerance in neurodegenerative disease research. Conversely, Roche's success with its KRAS G12C inhibitor divarasib in Phase 3 lung cancer trials underscores the promise of precision medicine. Divarasib outperformed competitors Amgen's Lumakras and Bristol Myers Squibb's Krazati, positioning Roche to potentially redefine standards of care based on genetic profiles. In a move reflecting industry trends towards collaboration and innovation risk-sharing models, AstraZeneca has partnered with Sino Biopharmaceutical on respiratory disease research. This strategic alliance represents a substantial investment aimed at expanding AstraZeneca's pipeline in respiratory therapeutics. Lastly, amidst these developments, psychedelic drugs are experiencing a renaissance in psychiatric care. Companies like Compass Pathways are pioneering clinical validation for their use in treating depression, signaling a potential paradigm shift from traditional SSRIs to newer therapeutic classes pending safety and efficacy data. Overall, these stories illustrate a dynamic interplay of scientific progress and regulatory navigation within the pharmaceutical and biotech sectors. While challenges persist—particularly in neurodegenerative disease treatment—the breakthroughs in oncology and metabolic disorder therapeutics offer hopeful prospects for improving patient care. As these industries continue evolving, integrating advanced technologies such as AI will likely play a pivotal role in shaping future therapeutic landscapes.Support the show

Will is the co-founder and CEO of Orbital, a legal AI platform built for the real estate industry. Founded in 2018, Orbital sits at the centre of property transactions, automating the legal work that has traditionally been slow and manual and connecting the parties who depend on it.  It now handles over 200,000 transactions a year for some of the UK's top law firms including Mishcon de Reya and works directly with the real estate businesses behind those deals: the developers, owner-operators, investors and REITs shaping the built environment.  By bringing law firms and their clients onto a single platform, Orbital is building the infrastructure for how real estate gets bought, sold and financed.  It opened a New York office in 2025 and, in January, raised a $60 million Series B to scale further across the US. 

What does it actually take to build an executive team from nothing? This week on The Data Minute, Ashley Neville fills in for Peter and sits down with Francois Ajenstat, Founder and CEO of Golden Analytics, to talk hiring at the earliest stages of a company, from seed through Series B.Francois spent over a decade as Chief Product Officer at Tableau before leading product at Amplitude, and recently launched Golden Analytics, an AI-native BI platform that just closed $21 million in total seed funding. He walks through why he sees fundraising as less about the check and more about finding long-term partners, why he never set out to build a foundational model, and why he thinks the fear around AI replacing data analysts has it backwards. He also breaks down his approach to those first few hires: starting with people he trusts completely, using Carta's own compensation data to build trust with candidates during offer negotiations, and the three-part test he runs on every new hire around AI fluency, taste, and ownership of outcomes.The conversation also covers Golden's unconventional customer feedback loop, the surprising order in which startups actually hire across functions, and Francois's long-running framework for job satisfaction: the work, the people, and the recognition.Subscribe to Carta's weekly Data Minute newsletter: https://carta.com/subscribe/data-newsletter-sign-up/Explore interactive startup and VC data, with Carta's Data Desk: https://carta.com/data-desk/Chapters: 01:17 – Announcing the $21M Seed: Fundraising Is About Partners, Not Just Capital 02:57 – Pitching Golden Analytics: Zig When Everyone Else Zags 06:06 – Why Golden Isn't Building Its Own Foundational Model 07:48 – The Privacy Question: Why Golden Never Sends Customer Data to the Models 09:17 – Will AI Replace the Data Analyst? (No, It Makes Them 10x) 10:57 – From CPO to "Solo" Founder: Why the Label Never Fit 13:14 – Hiring Employee One: The Former Tableau CTO 15:17 – Using Carta's Comp Data to Build Trust with Candidates 17:42 – Thinking About the ESOP from Day One 19:08 – The New Hiring Bar: AI Fluency, Taste, and Ownership 21:44 – Inside a Seven-Person Company Outshipping the Competition 22:46 – No Wall Between Customers and Engineers 24:33 – Making Customers Feel Like Founders 27:03 – An Unboxing: The Golden Analytics Coin 28:06 – The First Experience: What Happens When You Open Golden 29:33 – Surprising Data: Founders Hire Before They Raise 30:52 – The Order of Hires: Why CFOs Come Before Revenue 32:18 – Fractional vs. Full-Time: "Does This Make the Beer Taste Better?" 34:22 – What's Next to Hire: Engineers Ahead, Sales Behind 36:11 – Why Golden Skips the Middle: Senior Talent Paired With Junior Hunger 38:25 – Education, Fear, and Learning by Doing 41:15 – Building Carta's Own Report With AI, Faster 43:06 – Every Company Is a Data Company 44:27 – The Customer Data Francois Obsesses Over Daily 47:28 – Is SaaS Dead? Why the "Apocalypse" Headlines Miss the Point 49:21 – The Three-Factor Test for Job Satisfaction 51:47 – Redefining Appreciation: Experiences Over Titles 54:47 – What's Next for Golden Analytics 56:30 – OutroThis presentation contains general information only and eShares, Inc. dba Carta, Inc. (“Carta”) is not, by means of this publication, rendering accounting, business, financial, investment, legal, tax, or other professional advice or services, and is for informational purposes only.  This presentation is not a substitute for such professional advice or services nor should it be used as a basis for any decision or action that may affect your business or interests. © 2026 eShares, Inc., dba Carta, Inc. All rights reserved. In the interest of transparency, Golden Analytics is a customer of eShares, Inc. dba Carta, Inc. ("Carta").  While we have invited them here today to discuss their journey, please note that this is not an endorsement, solicitation, or recommendation for Golden Analytics or Carta. Carta does not assume any liability for reliance on the information provided during this podcast.

Build a new kind of rocket engine, and the world will beat a path to your door. Also, Blue Origin is raising $10 billion at a $130 billion pre-money valuation from Coatue Asset Management, Bezos himself and other big-name investors, the New York Times reported. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Today on the Invest In Her Podcast, host Catherine Gray talks with Sarah Lerner-Mantel, Managing Partner at Roll Tack Ventures, a Midwest-based venture capital firm investing in Series A and B B2B technology companies that drive growth, improve efficiency, and reduce risk. Before launching Roll Tack Ventures, Sarah helped scale Wayfair's 33-million-SKU marketplace and co-founded a med-tech company that developed an innovative ventilator during COVID-19, leading the company through acquisition. Her mission is to help exceptional founders grow industry-defining companies by providing not only capital, but meaningful strategic support and connections.  In this episode, Catherine and Sarah explore what it really takes for startups to raise venture capital, the difference between Series A and Series B funding, and why the Midwest is an untapped opportunity for innovation despite generating a quarter of U.S. GDP. Sarah shares how Roll Tack Ventures partners with founders by opening doors to customers—not just writing checks—and discusses her own entrepreneurial journey from startup founder to venture capitalist. They also discuss the importance of increasing women's representation in venture capital, how investors evaluate companies, why founders should better understand the venture funding process, and how education can encourage more women to become both founders and investors. Whether you're building a company, considering venture investing, or simply curious about how innovation gets funded, this conversation offers valuable insights from both sides of the investment table.  Websites Mentioned Roll Tack Ventures – https://rolltackventures.com Show Her The Money – https://showherthemoneymovie.com She Angel Investors – https://www.sheangelinvestors.com   Follow Us On Social Facebook @sheangelinvestors Twitter (X) @sheangelsinvest Instagram @sheangelinvestors & @catherinegray_investinher LinkedIn @catherinelgray & @sheangels   #InvestInHer #WomenInBusiness #WomenFounders #WomenEntrepreneurs #VentureCapital #VC #StartupFunding #SeriesA #SeriesB #FounderJourney #B2BTech #TechStartups #Innovation #FemaleInvestors #WomenInVC #AngelInvesting #WomenWhoInvest #BusinessGrowth #Leadership #Entrepreneurship #ScalingStartups #MidwestStartups #FounderLife #StartupSuccess #CatherineGray  

This week in Portland startup news, we start with the top 10 Silicon Florist posts from H1 2026 — the list readers built by clicking, not me — and the story it tells is worth pausing over: QSBS grabbed the top two slots, Dwayne Johnson's community read landed at number three, and Panthalassa's quiet $140 million Series B slotted in at four. From there, a midyear refresh of the "how to Portland startup community" primer — the front door for anyone who's been circling the edges and hasn't quite found their way in yet. CHAPTERS:00:00 Portland startup news02:05 Small Business resources07:10 Top 10 Oregon startup stories so far18:35 How to Portland startup community22:15 SecretsLINKS:Top 10 Silicon Florist posts, H1 2026 — https://siliconflorist.com/2026/07/01/top-10-silicon-florist-posts-for-the-first-half-of-2026/How to Portland startup community (midyear) — https://siliconflorist.com/2026/07/02/refresher-how-to-portland-startup-community-midyear-2026-quickstart-edition/Portland Startups Slack — https://pdxslack.com/Bricks Need Mortar × Portland Office of Small Business AI Shop Talk — https://siliconflorist.com/2026/06/29/run-a-small-business-curious-about-ai-bricks-need-mortar-can-help/The AI Conversation — register (Jul 16) — https://www.eventbrite.com/e/the-ai-conversation-thats-been-missing-for-small-business-tickets-1991933655184Our Place — https://siliconflorist.com/2026/07/01/helping-you-find-your-people-and-community-with-our-place/Our Place app — https://www.ourplace.community/LocalFirst PDX — https://siliconflorist.com/2026/07/01/portland-loves-local-small-business-localfirst-pdx-makes-them-easier-to-find/LocalFirst PDX site — https://localfirstpdx.com/FIND RICK TUROCZY ON THE INTERNET AT…- https://patreon.com/turoczy- https://linkedin.com/in/turoczy- Portland Oregon startup news on Apple Podcasts https://podcasts.apple.com/us/podcast/portland-oregon-startup-news-silicon-florist/id1711294699- Portland Oregon startup news Spotify https://open.spotify.com/show/2cmLDH8wrPdNMS2qtTnhcy?si=H627wrGOTvStxxKWRlRGLQ- Startup Stories on Spotify https://open.spotify.com/show/1Tk7bbzaNYowGouI9ucKC3- Startup Stories on Apple Podcasts https://podcasts.apple.com/us/podcast/startup-stories-with-silicon-florist/id1849468494- The Long Con on Apple Podcasts https://podcasts.apple.com/us/podcast/the-long-con/id1810923457- The Long Con on Spotify https://open.spotify.com/show/48oglyT5JNKxVH5lnWTYKA- https://bsky.app/profile/turoczy.bsky.social- https://siliconflorist.substack.com/- https://pdxslack.comABOUT SILICON FLORIST ----------For nearly two decades, Rick Turoczy has published Silicon Florist, a blog, newsletter, and podcast that covers entrepreneurs, founders, startups, entrepreneurship, tech, news, and events in the Portland, Oregon, startup community. Whether you're an aspiring entrepreneur, a startup or tech enthusiast, or simply intrigued by Portland's startup culture, Silicon Florist is your go-to source for the latest news, events, jobs, and opportunities in Portland Oregon's flourishing tech and startup scene. Join us in exploring the innovative world of startups in Portland, where creativity and collaboration meet.ABOUT RICK TUROCZY ----------Rick Turoczy has been working in, on, and around the Portland, Oregon, startup community for nearly 30 years. He has been recognized as one of the “OG”s of startup ecosystem building by the Kauffman Foundation. And he has been humbled by any number of opportunities to speak on stages from SXSW to INBOUND and from Kobe, Japan, to Muscat, Oman, including an opportunity to share his views on community building on the TEDxPortland stage (https://www.youtube.com/watch?v=Cj98mr_wUA0). All because of a blog. Weird.https://siliconflorist.com#pdx #portland #oregon #startup #entrepreneur

Brandon Sedloff and Aaron Gershenberg sit down in California's wine country to explore three decades of venture capital evolution. Aaron, CEO and managing partner at Pinegrove Venture Partners, shares his unconventional path from economic development work and real estate consulting to building SVB Capital into a $10 billion platform, then relaunching after the bank's collapse as Pinegrove, backed by Sequoia Heritage and Brookfield. They discuss: - How Aaron used Monte Carlo analysis to project fund performance and justify additional capital during the dot-com crash - Why he structured distribution-only fee models for anchor LPs instead of traditional management fees and carry - The mechanics of becoming a FOIA blocker to attract pension fund capital - How Pinegrove positions across seed, Series A, Series B, credit, and secondaries to capture alpha at different stages - Why the 2024–2026 vintage may deliver the fastest value creation Aaron has seen in his career This episode offers a roadmap for institutional investors navigating venture exposure, LP sentiment shifts, and the structural innovations that have reshaped fund economics over the past 25 years. Topics: (00:00:00) - Intro (00:03:20) - Growing up between Uganda, Kenya and New Jersey (00:10:50) - Africa's influence and giving back (00:15:40) - Question authority and the unstructured path (00:18:30) - Real estate consulting and the sales pivot (00:21:10) - Breaking into venture in the mid-'90s (00:32:00) - Joining Silicon Valley Bank (00:33:30) - Building community through cycling and kiteboarding (00:37:00) - Launching SVB Capital (00:40:15) - Innovative fund structures and FOIA blockers (00:44:20) - Scaling through three eras: 2000–2023 (00:49:00) - SVB's collapse and rebuilding as Pine Grove (00:50:20) - Partnering with Sequoia Heritage and Brookfield (00:56:30) - Pine Grove's platform and strategies today (01:02:30) - AI conviction and the 2024–2026 vintage (01:09:10) - What keeps you up at night Links: Aaron on LinkedIn - https://www.linkedin.com/in/aaron-gershenberg-7361b23/ Pinegrove Venture Partners - https://pinegrove.vc/ Brandon on LinkedIn - https://www.linkedin.com/in/brandonsedloff/ Juniper Square - https://www.junipersquare.com/

Chris Gomes set out to hire four AI product managers. Seven months later, his biggest lesson was not about AI at all.In this episode of Supra Insider, Marc Baselga and Ben Erez sit down with Chris Gomes, VP of Product at Conveyor, the Series B startup that automates responses to security questionnaires and RFPs. Chris walks through the seven months he spent hiring four AI PMs, why he spent so long defining what an “AI product manager” even means, and the realization that culture fit, not AI skill, was the thing that actually predicted success.They explore how he rebuilt a stalled interview process by pulling the most important screens to the front, the MOC framework he uses to map each interview step to specific competencies, his case for work trials and customer role-plays, how he treats references and back-channels, and why the gut-level question of whether you'd enjoy working with someone deserves more weight than most rubrics give it.If you're a hiring manager trying to run a tighter, higher-signal process, a founder thinking about your first product hires, or a PM preparing for interviews and wondering what teams are really evaluating, this episode is for you.All episodes of the podcast are also available on Spotify, Apple and YouTube.New to the pod? Subscribe below to get the next episode in your inbox

Paul Erlanger is the Co-Founder and CEO of FOMO, the social-first trading platform building the future of on-chain investing. Since founding the company in 2025, Paul has raised approximately $94 million, including a $17 million Series A led by Benchmark and a $75 million Series B led by Index Ventures with participation from USV, valuing the company at $550 million. Today, FOMO has grown to 600,000 users, processed over $4 billion in trading volume, and is adding thousands of new users every day—all with a team of just 17 people. AGENDA:  00:00 – Building a $550M Company with No Salaries, No Managers & No 1:1s 03:58 – Why Traditional Brokerages Will Lose in the Next 10 Years 09:30 – Why Robinhood's Strategy Is Wrong; The End of the Financial Super App?  13:05 – "Markets Aren't a Casino" — The Case for Retail Investors Fighting Wall Street 16:45 – The Radical Hiring Bet: Giving Employees Founder-Level Equity 23:40 – AI Kills Org Charts: Why FOMO Will Stay Under 25 Employees 29:30 – Why Taste Beats AI & The Biggest Mistake Most Consumer Startups Make 33:10 – The Social Media Playbook That Every Startup Gets Wrong 39:20 – How Benchmark, Index & USV Won the Deal—and the VC Advice Founders Need to Hear 46:10 – The Future of Investing: Social Trading, Creator Economies & Financial Networks  

Good morning from Pharma Daily: the podcast that brings you the most important developments in the pharmaceutical and biotech world. Today, we explore the dynamic shifts and breakthroughs shaping the industry, from major acquisitions to groundbreaking scientific advancements. Merck KGaA has made headlines with its bold $11.3 billion acquisition of Bio-Techne Corporation. This marks Merck's most significant deal since purchasing Sigma-Aldrich in 2015, reinforcing its strategic focus on expanding its life sciences tools portfolio. The acquisition aims to accelerate innovation in drug development and diagnostics, highlighting Merck's commitment to enhancing its capabilities in biotechnology under the leadership of CEO Kai Beckmann. Such strategic moves underscore a broader trend towards bolstering biotech portfolios through mergers and acquisitions as companies aim to remain competitive in an ever-evolving market landscape. In regulatory news, the FDA has approved a pioneering combination therapy involving Gilead's Trodelvy and Merck & Co.'s Keytruda for the first-line treatment of triple-negative breast cancer (TNBC). This aggressive cancer subtype has historically had limited treatment options, making this approval particularly significant. The combination therapy leverages an antibody-drug conjugate targeting Trop-2 alongside a PD-1 inhibitor, offering a promising new strategy that could substantially improve patient survival outcomes. This development also highlights the growing role of antibody-drug conjugates in oncology, illustrating how innovative therapeutic combinations can enhance treatment efficacy. Meanwhile, Pfizer's Ibrance has received FDA approval for label expansion to treat HR-positive, HER2-positive metastatic breast cancer. As a CDK4/6 inhibitor crucial in cell cycle regulation, Ibrance's expanded use reflects ongoing advancements in targeted therapies that personalize cancer treatment based on specific molecular characteristics. Such expansions demonstrate the importance of continuous clinical evaluation and regulatory engagement in extending the lifecycle and applications of existing drugs. Ionis Pharmaceuticals has gained FDA approval for Tryngolza for severe hypertriglyceridemia, marking a significant milestone for antisense oligonucleotide therapies. By targeting apolipoprotein C-III, Tryngolza offers a novel approach to managing metabolic conditions linked to pancreatitis risks. This approval underscores the growing importance of antisense technology in addressing complex lipid disorders and highlights Ionis' strategic efforts to expand market reach through global partnerships. On the business development front, Boehringer Ingelheim's partnership with Immunai aims to leverage artificial intelligence in T-cell target discovery for cancer and autoimmune diseases. The integration of AI/ML technologies into drug discovery processes is increasingly seen as essential for enhancing precision and efficiency. This collaboration reflects an industry-wide shift towards embracing technology to improve research and development outcomes. In clinical trials, Otsuka's centanafadine shows promise for adults with ADHD and comorbid anxiety following successful Phase 3b trials. As a small molecule reuptake inhibitor, centanafadine could provide dual therapeutic benefits for patients with these overlapping conditions. Such developments highlight ongoing innovation in neuropsychiatric treatments aimed at addressing mental health conditions with greater precision. Financially, Oblenio Bio's $62 million Series B funding round will support advancing its tri-specific autoimmune T-cell engager into trials, potentially offering new solutions for autoimmune diseases through innovative immunotherapy approaches. These financial movements illustrate how companies are strategically positioning themselves to capitalize on emerging therapeutic opportunities. Amid these developments, regulatory dynamics continue to evolve, as seen with the FDA's pilot program aimed at streamlining drug approval processes. Initiatives like these are pivotal in restoring confidence in regulatory frameworks while adapting to new scientific insights and technological advancements. Overall, these developments underscore the pharmaceutical and biotech sectors' dynamic nature, characterized by strategic collaborations, regulatory milestones, and innovative treatment options poised to enhance patient care and strengthen drug development pipelines. The ongoing integration of cutting-edge technologies such as AI signifies an evolution towards more personalized and efficient healthcare solutions. Thank you for tuning into Pharma Daily, where we bring you the latest insights from the forefront of pharmaceutical and biotech innovation. Join us next time as we continue to explore the trends shaping the future of healthcare globally.Support the show

Good morning from Pharma Daily: the podcast that brings you the most important developments in the pharmaceutical and biotech world. Today, we delve into a series of impactful developments shaping the future of medical innovation and patient care. The landscape of pharmaceutical and biotech industries is currently experiencing notable shifts driven by scientific advancements, regulatory updates, and strategic collaborations. One of the more controversial events involves the retraction of a high-profile study in Nature Medicine. This study initially suggested that the timing of PD-1 inhibitor administration had significant impacts on survival rates for non-small cell lung cancer patients. Early-day administration was linked to improved outcomes compared to later in the day. However, after a four-month investigation, concerns over methodological validity led to its retraction. This incident serves as a stark reminder of the necessity for rigorous peer review and transparency in clinical research, which are essential for influencing treatment protocols effectively. In industry news, Eli Lilly has entered into a major $1.9 billion partnership with Abbisko Therapeutics to harness Abbisko's drug discovery capabilities, particularly in oncology. This collaboration highlights an increasing trend where Western pharmaceutical companies team up with Asian biotech firms to accelerate drug development and tap into innovative therapeutic platforms. Eli Lilly is also recalibrating its strategy for launching its oral obesity treatment, Foundayo, in Europe, as it navigates the complexities introduced by the Most Favored Nation pricing agreement with the U.S. This underscores a broader challenge within the industry: balancing pricing regulations with expanding access through digital health channels like telehealth. ADC Therapeutics is taking steps to address safety concerns surrounding its antibody-drug conjugate, Zynlonta, by reducing its workforce by 17%. This strategic realignment demonstrates the delicate balance companies must maintain between advancing promising therapies and ensuring patient safety through vigilant clinical oversight. On the regulatory front, Incyte's decision to drop its lawsuit against CMS over drug classification issues involving its JAK inhibitor Opzelura highlights ongoing negotiations between pharmaceutical companies and regulatory bodies. These classifications have direct implications for market access and reimbursement strategies. Shifting focus to infrastructure, Advancell's move to establish its U.S. headquarters near Boston for radiopharmaceutical production underscores an emphasis on localizing drug manufacturing facilities to enhance supply chain resilience. This decision aligns with broader efforts to support domestic clinical trials for innovative therapies targeting prostate cancer. In terms of technological innovation, Novartis has invested $105 million upfront in Antares Therapeutics to target historically undruggable cancer proteins through small molecule development. This move reflects an industry-wide push towards exploiting cutting-edge technologies like AI-driven drug discovery to meet unmet medical needs in oncology. Precision medicine continues to gain traction, exemplified by Abbott's partnership with AlzPath to develop blood-based diagnostic tests for Alzheimer's disease. Collaborations such as these are pivotal in enhancing early diagnosis and personalized treatment approaches for neurodegenerative disorders. Meanwhile, the industry's financial dynamics continue to evolve with significant fundraising activities. Serapha Bio's public debut through a reverse merger with Boundless Bio raised $230 million, highlighting a growing trend of utilizing reverse mergers as a pathway to public markets. This financial boost comes alongside their licensing of a gene editing technology from China, underscoring the global nature of biotech collaborations. In oncology, Eli Lilly's extended partnership with Abbisko Therapeutics underscores the ongoing commitment to precision medicine, aiming to harness small molecule innovations targeting specific cancer pathways. Concurrently, the European Medicines Agency approved Astellas' Padcev combined with Merck & Co.'s Keytruda for muscle-invasive bladder cancer treatment based on promising Phase 3 results. Ophthalmology research is also seeing substantial investment with Ollin Biosciences raising $330 million in Series B funding aimed at developing therapies that challenge existing treatments like Vabysmo for eye diseases. Such investments indicate strong confidence in novel therapies that could redefine standards in treating conditions like wet age-related macular degeneration. In conclusion, these developments reflect a vibrant biotech and pharma landscape characterized by strategic partnerships, innovative financing mechanisms, and regulatory milestones that collectively drive forward scientific progress and enhance therapeutic options available worldwide. As these sectors continue to evolve, integrating cutting-edge technologies like gene editing and precision oncology will be pivotal in shaping healthcare delivery's future trajectory while improving patient outcomes globally.Support the show

Unser heutiger Gast ist gelernter Fluggerätmechaniker, Wirtschaftsingenieur, täglich Meditierender und Gründer eines der am schnellsten wachsenden SaaS-Startups in Deutschland. Keine gewöhnliche Kombination – aber genau das macht seine Geschichte so spannend. Julian Wiedenhaus begann mit einem dualen Studium bei Airbus in Bremen. Er baute Flugzeuge, lernte, was Produktionstechnik bedeutet, und wechselte für den Master an die TU Hamburg – bewusst, weil dort Entrepreneurship im Lehrplan stand. Dort traf er Alexander Noll, einen Bauingenieur, dessen Vater eine Zimmerei in Niedersachsen betreibt. Und genau dort, zwischen Werkstatt und Büro, sahen die beiden, was Hunderttausende Handwerksbetriebe in Deutschland jeden Tag erleben: veraltete Software, Excel-Tabellen, Stift und Papier. Gleichzeitig ein enormer Fachkräftemangel, steigender Kostendruck und eine Branche, auf die wir alle angewiesen sind – für jede Sanierung, jeden Neubau, jede Wärmepumpe. Im Februar 2020 gründeten sie mit dem Entwickler Richard Keil Plancraft. Die erste Tischlerei in Hamburg-Ottensen ging im Sommer als Pilotkunde live. Heute, fünf Jahre später, nutzen über 20.000 Kunden in elf Ländern die Software, das Team ist auf über 130 Mitarbeitende gewachsen, und mit mehr als 50 Millionen Euro Finanzierung – zuletzt eine Series B über 38 Millionen, angeführt von Headline – spielt Plancraft in der ersten Liga europäischer ConstructionTech-Startups. Die Vision: das europäische Betriebssystem für das Handwerk. Weniger Büro, mehr Handwerk. Doch was Julian Wiedenhaus besonders macht, zeigt sich nicht in den Zahlen, sondern in der Kultur. Er meditiert seit über fünf Jahren jeden Morgen, hat mit dem „Weekly Fight Club" ein gemeinsames Achtsamkeitsritual im Team etabliert und führt nach dem Prinzip: Vertrauen gegen Engagement. Die Unternehmenswerte bei Plancraft heißen #stoked, #together, #humble. Als er 2024 drei Wochen auf Sri Lanka verbrachte, schrieb er auf LinkedIn offen darüber, was es bedeutet, als CEO loszulassen und seinem Team zu vertrauen. Seit mehr als neun Jahren beschäftigen wir uns in diesem Podcast mit der Frage, wie Arbeit den Menschen stärkt, statt ihn zu schwächen. Wir haben in über 500 Episoden mit fast 700 Persönlichkeiten darüber gesprochen, was sich bereits verändert hat und was sich weiter ändern muss. Fünf Millionen Menschen arbeiten im deutschen Handwerk, die meisten in Betrieben mit weniger als zwanzig Mitarbeitenden. Wie verändert sich Arbeit, wenn eine Branche, die Jahrhunderte lang analog funktioniert hat, plötzlich digital denken muss – und kann? Plancraft entwickelt sich zunehmend zum KI-Unternehmen. Der neue Telefonassistent PORTA nimmt Anrufe an, dokumentiert Anfragen, koordiniert Termine. Wenn die Vision lautet, dass Handwerker bald nur noch ihre Stimme brauchen – was bedeutet das für die Rolle des Menschen im Betrieb? Und wie baut man als junger Gründer eine Unternehmenskultur, die gleichzeitig Höchstleistung und Menschlichkeit trägt – mit Meditation im Kalender, Vertrauen als Führungsprinzip und dem Mut, als CEO drei Wochen zu verschwinden? Fest steht: Für die Lösung unserer aktuellen Herausforderungen brauchen wir neue Impulse. Wir suchen weiter nach Methoden, Vorbildern, Erfahrungen, Tools und Ideen, die uns dem Kern von New Work näher bringen. Darüber hinaus beschäftigt uns von Anfang an die Frage, ob wirklich alle Menschen das finden und leben können, was sie im Innersten wirklich, wirklich wollen. Ihr seid bei On the Way to New Work – heute mit Julian Wiedenhaus. [Hier](https://linktr.ee/onthewaytonewwork) findet ihr alle Links zum Podcast und unseren aktuellen Werbepartnern

Nick Turner is the CEO of Dreamdata. Nick is a seasoned B2B software leader with nearly two decades of experience building and scaling go-to-market teams, helping companies grow from early traction to tens of millions in revenue. Before stepping into the CEO role, Nick served as Chief Revenue Officer at Dreamdata, where he played a key role in shaping the company's growth strategy and expanding its presence in the U.S. market. He later transitioned into the CEO seat, leading the company through a pivotal phase of scale and transformation. Under his leadership, Dreamdata recently raised a $55 million Series B round led by PeakSpan Capital—fueling its mission to become the go-to platform for B2B marketers to connect data, attribution, and revenue in the AI era.  In this episode, we'll explore what it takes to step into the CEO role, how to lead through rapid growth and funding milestones, and Nick's perspective on building modern go-to-market teams in an increasingly data-driven world.

Bobbie Racette started with $300 at her kitchen table. Nine years later, she became the first Indigenous woman in Canada to build, scale, and sell a tech startup. In this episode - the first time Bobbie has dug into the details of the sale on a podcast - host Colleen O'Connell-Campbell sits down with the founder of Virtual Gurus, an AI-powered inclusive talent marketplace that matched underrepresented talent with businesses including Mastercard, Telus, and BMO. Bobbie shares the full arc: bootstrapping to $1.8 million in revenue before raising a cent, hearing 170 no's before closing a seed round, scaling through three funding rounds during COVID, becoming the first Indigenous woman in Canada to close a Series A, navigating founder fatigue, stepping down as CEO before the exit, and ultimately selling to a U.S. private equity firm that rolled Virtual Gurus into North America's largest virtual assistant platform - with the AI sold separately to a Calgary company. This is a conversation about what it takes to build something from nothing, what it costs personally, and what comes next when the mission is bigger than the transaction.   Key Takeaways:   Bobbie created Virtual Gurus in 2016 after being laid off in oil and gas and unable to find a job. She is Cree Métis, queer, and covered in tattoos - and nobody would hire her. The business started as a way to create a job for herself and evolved into a platform providing remote work to marginalized talent across Canada and the U.S.   She bootstrapped to approximately $1.8 million in annual revenue before seeking external funding. The seed round took over two years and 170 investor rejections before closing at $1.25 million. The Series A, two years later, was significantly easier.   Virtual Gurus scaled past $40 million in revenue and closed three funding rounds during COVID. Total capital raised was $14-20 million.   The exit was not originally planned. For the first four years, Bobbie intended to keep the company as a legacy business. The shift came around 2022 when the scale of the operation began to outpace the original mission. The board recognized that an acquisition was likely the best path forward.   The company was simultaneously pursuing a Series B and fielding acquisition offers - a dual-track process. The data room was already built for the fundraise, which accelerated due diligence to approximately five months. The acquisition by a U.S. private equity firm closed in November 2025.   The AI platform was sold separately to a Calgary-based company - effectively a double sale. The core business was rolled into the acquirer's larger virtual assistant platform.   Bobbie had stepped down from CEO to president in May 2025, with her COO becoming successor CEO. The successor stayed with the company through and after the acquisition. Bobbie's role during due diligence was primarily support - being available for the team mentally, emotionally, and strategically, while the finance team and executive team drove the process.   Founder fatigue and decision fatigue were real and significant. Bobbie emphasizes that founders need to talk about this more openly, and that boards and investors need to be supportive during those low periods rather than adding pressure.   Retention of employees during due diligence was one of the hardest parts. Bobbie's culture at Virtual Gurus was built on honesty and transparency, and not being able to tell her leadership team about the acquisition felt deeply uncomfortable.   Post-exit, Bobbie has retired her parents (her mother was her first angel investor, contributing her last $20,000), bought a new home, and is investing time and capital into the next generation. She is now an angel investor in five businesses - all founded by people from underserved communities, including Indigenous and LGBTQ+ entrepreneurs.   She has launched Tapwe (Cree for "truth"), a platform to support underserved founders with financial literacy, mentorship, AI-powered matching, and startup scaling resources. A documentary is in production. Her newsletter, The Fire Report, scaled to 4,000 subscribers almost immediately. She is also doing regular paid advisory sessions each week through her website.   Bobbie's story is a reminder that a cash-rich exit can be deeply values-driven, inclusive, and barrier-breaking - and still set you up for whatever comes next. If today's episode has you thinking about your own journey, whether you are at the kitchen table, scaling fast, or quietly eyeing your exit, book a one-on-one Wealth Gap Analysis with Colleen O'Connell-Campbell via LinkedIn or email    Please leave a five-star rating and review to help more founders find this show. *** The Cash Rich Exit Podcast is brought to you by O'Connell-Campbell Wealth Management at RBC Dominion Securities.   All opinions expressed by the host, Colleen O'Connell-Campbell, and podcast guests are solely their own opinions and do not reflect the opinion of RBC Dominion Securities.   This podcast is for informational purposes only before taking any action based on information in this podcast you should consult with a qualified professional.   Colleen O'Connell-Campbell is a Wealth Advisor at RBC Dominion Securities, a member of the Canadian Investor Protection Fund.

Tyler talks with Bailey Stockdale about their $13M Series B announcement. — This episode is presented by Ambrook. — Links Leaf - https://withleaf.io Leaf's Series B - https://www.agnavigator.com/Article/2026/06/11/bayer-invests-in-ai-backbone-company-leafs-series-b-round/ 

Most AI failures won't come from a bad model. They'll come from bad data.Shashank Saxena spent most of his career on the buying side of enterprise technology before founding VNDLY which was acquired by Workday for $510 million. He then joined Sierra as a Managing Partner before going full time as Co-founder and CEO of Pantomath, a data operations center for enterprises that are betting their future on AI agents.We discuss why data quality is becoming one of the biggest challenges in enterprise AI. An AI agent fed bad data for 12 hours doesn't go rogue. It just makes 12 hours of wrong decisions: rejecting insurance claims, issuing credit cards, or drilling in the wrong location. As more business decisions are delegated to AI systems, companies will need far greater visibility into what is happening across their data infrastructure.Shashank also shares the decisions that led to VNDLY's acquisition, the advice he'd give founders evaluating acquisition offers today, and why a Michael Jordan analogy continues to motivate him as a second-time founder.If you're building enterprise software, selling to large companies, or trying to figure out whether experience is an asset or a liability in the AI era, this episode is for you.0:00 - Trailer01:00 - How Shashank became a second-time founder07:20 - Where Pantomath sits in the data stack10:55 - How a broken Tableau report turns mission-critical with AI12:55 - Who Pantomath sells to15:35 - Solving for a problem that doesn't exist yet19:03 - How have founder expectations changed today?20:31 - Series B companies pre- and post-AI21:26 - The Michael Jordan example23:57 - How a repeat founder chooses investors25:10 - What value Snowflake adds as a strategic investor27:05 - Data is not an open category today28:34 - The astounding Databricks outcome29:08 - The reality of the $100 million ARR number31:48 - Will non-human workers 100x in the next few years?36:00 - How to protect data in motion37:26 - How comfortable are we giving full access to agents?39:47 - Where is automation fastest today?42:09 - Why entrepreneurs tend to like uncertainty43:28 - Why Shashank chose to be a founder45:48 - A customer-driven $510M acquisition48:32 - Employees vs contractors in any organization51:22 - Building from Ohio vs the Bay Area53:14 - Learnings from selling to enterprises56:31 - How Shashank raised from Tier 1 US VCs59:19 - Heads down or network as a founder?1:02:47 - First-time vs second-time founder edge in AI1:06:22 - Hiring as a repeat founder1:08:08 - How enterprise sales has changed1:10:52 - How do you sell for a problem that isn't visible today?1:12:58 - Best piece of advice1:16:27 - The only advice for a founder considering M&A1:21:06 - Position yourself to be capable of taking risks1:24:51 - What matters to an enterprise buyer?-------------India's talent has built the world's tech—now it's time to lead it.This mission goes beyond startups. It's about shifting the center of gravity in global tech to include the brilliance rising from India.What is Neon Fund?We invest in seed and early-stage founders from India and the diaspora building world-class Enterprise AI companies. We bring capital, conviction, and a community that's done it before.Subscribe for real founder stories, investor perspectives, economist breakdowns, and a behind-the-scenes look at how we're doing it all at Neon.-------------Check us out on:Website: https://neon.fund/Instagram: https://www.instagram.com/theneonshoww/LinkedIn: https://www.linkedin.com/company/beneon/Twitter: https://x.com/TheNeonShowwConnect with Siddhartha on:LinkedIn: https://www.linkedin.com/in/siddharthaahluwalia/Twitter: https://x.com/siddharthaa7-------------This video is for informational purposes only. The views expressed are those of the individuals quoted and do not constitute professional advice.Send us Fan Mail

The Finale of Parshat Shelach- Enjoy!

I have always felt the best shabbat table talk on the Prasha comes from parents and children who know and are confident with the ins and puts of the details in the weekly Parsha. So many of my students never take advantage of this because they either never learned it or do not have the time to review it weekly. Enter the BEST SERIES! You are about to master the Parsha with four, fun and engaging quick Shiurim each week. give me 20 minutes or less and I will give you the Parsha! ENJOY! 

Parshat Shelach continues with Shiur 3, Enjoy!

Parshat Shelach continues now with part 2 Enjoy! 

Autonomous vehicles may be the closest real-world example of AI operating in life-and-death situations at scale. Justin Norden believes healthcare has a lot to learn from how that industry approached safety, testing, adoption, and trust. This week, Michael and Halle sit down with the founder and CEO of Qualified Health, fresh off the company's $125 million Series B, to discuss why healthcare organizations need to think differently about deploying AI. Justin shares how his experience at Stanford, Apple, Waymo, and in healthcare investing shaped his view that health systems need AI infrastructure, governance, and workforce buy-in, not just another point solution.We cover:What healthcare can learn from Waymo's approach to safe AI deploymentWhat founders need to understand about building around EpicWhy health systems need to treat AI as a CEO-level priority, not an innovation projectHow Qualified Health is helping systems deploy, monitor, and measure AI workflowsWhy governance, safety, and ROI matter as much as model performanceWhy clinicians are right to be skeptical about AI liabilityAbout our guest:Justin Norden, MD is Co-Founder and CEO of Qualified Health building the trusted platform for health system AI. Additionally, he has been an Adjunct Professor at Stanford Medicine in the Department of Biomedical Informatics Research where his research and teaching focused on AI in medicine and digital health where he founded and still teaches courses on digital health and generative AI in medicine. Previously, Dr. Norden was Co-Founder and CEO of Trustworthy AI, a company focused on algorithm safety and trust, which was acquired by Waymo (Google Self-Driving). He was a Partner at GSR Ventures leading investments in healthcare and AI, worked on the healthcare team at Apple, and helped start the Stanford Center for Digital Health. Dr. Justin Norden received an MD and MBA from Stanford University, an MPhil in Computational Biology from the University of Cambridge, and a BA in Computer Science from Carleton College.—

https://novacut.ai/  https://genaimeetup.com/  Anthropic has officially closed a $65 billion Series H at a $965 billion valuation, nearly 2.5x its valuation from just 100 days ago. Meanwhile, funding is flowing across the ecosystem: Frameworks AI at $15B, Baseten at $11B, OpenRouter's $113M Series B, and Cognition AI's $1B Series D. NVIDIA went on an open-source super week with Nemotron 3 Ultra, Cosmos 3, and Nemotron 3.5 ASR. Microsoft dropped 5 new MAI models. Google released Gemma 4 12B, and Anthropic shipped Opus 4.8. On the benchmarks front, DeepSWE crowns GPT-5.5 as the leader in long-horizon coding tasks, while ITBench shows even frontier models struggle with real-world SRE incidents — Claude Opus 4.7 tops out at just 47%. Plus: Cloudflare acquires VoidZero to build the future of AI-native edge development, and Google is paying SpaceX $920M/month for compute. Topics covered: • Anthropic's $65B Series H and path to $1T • Fireworks AI, Baseten, OpenRouter & Cognition funding rounds • Microsoft's 5 new MAI models • NVIDIA's open-source super week (Nemotron, Cosmos 3) • MiniMax M3, Gemma 4 12B, JetBrains Mellum2, Opus 4.8 • DeepSWE benchmark: GPT-5.5 leads long-horizon coding • ITBench: Frontier models under 50% on real SRE tasks • Cloudflare + VoidZero for AI-native edge dev • Google's $920M/month SpaceX compute deal #AI #Anthropic #NVIDIA #OpenAI #AInews #TechNews #LLM     Funding rounds Anthropic formally confirmed the closure of its $65 billion Series H funding round at a post-money valuation of $965 billion. This represents a 2.5-fold increase over its $380 billion Series G valuation from February 2026, adding $585 billion in value in approximately 100 days https://www.anthropic.com/news/series-h  Frameworks AI raising at 15B valuation representing a near fourfold increase from its $4 billion Series C valuation recorded in October 2025 processing 15 trillion tokens daily for major production clients including Cursor, Notion, and Perplexity https://finance.yahoo.com/sectors/technology/articles/fireworks-ai-eyes-15-billion-174609357.html Baseten is raising 1B at 11B valuation annualized revenue, which skyrocketed from $200 million to $600 million over a single quarter https://techstartups.com/2026/05/26/ai-inference-startup-baseten-in-talks-to-raise-1-billion-at-11-billion-valuation/  OpenRouter has secured a $113 million Series B funding OpenRouter has experienced exponential traffic growth, with weekly production throughput expanding fivefold from 5 trillion to 25 trillion tokens over a six-month horizon https://www.businesswire.com/news/home/20260526953416/en/OpenRouter-Raises-%24113-Million-CapitalG-led-Series-B-as-Weekly-Volume-Explodes-to-25T-Tokens  Further up the stack: Cognition AI secured a $1 billion Series D round led by Lux Capital and 8VC https://cognition.ai/blog/series-d   Model Releases MAI models: MAI-Code-1-Flash: A 5-billion active parameter model optimized for ultra-low latency within GitHub Copilot and VS Code. MAI-Image-2.5: A high-fidelity image generation model ranking third on global image evaluation arenas, outperforming competing architectures like Nano Banana Pro. MAI-Transcribe-1.5: A multi-lingual speech processing engine offering fivefold speed improvements across 43 languages. MAI-Voice-2: Natural audio and voice generation across 15 languages, available at a highly competitive price point. Web IQ: A search-grounding API engineered to directly compete with Perplexity. https://microsoft.ai/models/    https://www.peoplematters.in/news/ai-and-emerging-tech/uber-imposes-dollar1500-monthly-ai-spending-limit-on-employees-amid-rising-costs-50073    Nvidia has executed an "Open-Source Super Week," positioning itself as a dominant software and model publisher: Nemotron 3 Ultra (best US open source open weights model but behind china): A massive 550-billion parameter MoE (55 billion active) designed with a 1-million token context window, optimized specifically for high-throughput, cyclical agent loops. It achieved peak throughput rates of 400 tokens per second on day-zero optimized clusters. Cosmos 3: A physical AI world-modeling framework comprising 16-billion Nano and 64-billion Super variants. Built on a Mixture-of-Transformers (MoT) architecture, Cosmos 3 natively binds textual, visual, auditory, and physical kinetic vectors. Nemotron 3.5 ASR: A highly compact 0.6-billion parameter streaming speech recognition model pushing sub-100 millisecond latencies across 40 language locales.   https://www.minimax.io/models/text/m3  MiniMax M3: A 1-million token context model hitting 59.0% on SWE-Bench Pro and 74.2% on MCP Atlas, though noted for high token consumption due to intensive internal self-validation loops.   https://blog.google/innovation-and-ai/technology/developers-tools/introducing-gemma-4-12b/  Gemma 4 12B: Google's Apache 2.0 on-device model, which utilizes an encoder-free architecture that projects vision and audio vectors directly into the text-token space, bypassing separate CLIP-style encoders to minimize local memory footprints. https://www.jetbrains.com/mellum/  JetBrains Mellum2: A compact 12-billion parameter MoE (2.5 billion active) engineered for ultra-low latency routing and retrieval-augmented generation (RAG) sub-agents within developer IDEs. Opus 4.8 https://www.anthropic.com/news/claude-opus-4-8    https://www.cnbc.com/2026/06/05/google-to-pay-spacex-920-million-a-month-for-xai-compute-capacity.html      Benchmarks: https://deepswe.d atacurve.ai/blog https://venturebeat.com/technology/deepswe-blows-up-the-ai-coding-leaderboard-crowns-gpt-5-5-and-finds-claude-opus-exploiting-a-benchmark-loophole (GPT 5.5 the winner in long horizon tasks) a highly complex software engineering benchmark focused on original, long-horizon tasks across five distinct programming languages. Comprising 113 chaotic tasks across 91 live, production-grade repositories, DeepSWE forces agents to generate 5.5 times more code and modify an average of 7 separate files per task compared to standard evaluations. On this challenging leaderboard, GPT-5.5 leads with a score of 70%, establishing a significant 16-percentage-point lead over contemporary alternatives I think older benchmarks where models reach ~90% accuracy can be considered saturated. Few percentage points don't give us any good signal.  https://research.ibm.com/publications/developing-ai-agents-for-it-automation-tasks-with-itbench  ITBench-AA, an evaluation framework focusing on live Kubernetes incident response and Site Reliability Engineering (SRE) operations. Comprising 59 live, containerized SRE incident snapshots, the results are remarkably sobering: every frontier model scored under 50% on successful incident resolution, with Claude Opus 4.7 leading at 47% and GPT-5.5 following closely at 46%.   Edge AI announcements: https://www.cloudflare.com/press/press-releases/2026/cloudflare-acquires-voidzero-to-build-the-future-of-the-ai-native-web/  The consolidation of the AI-native developer stack has reached the runtime virtualization layer. Cloudflare recently completed the acquisition of VoidZero, the development group responsible for Vite, Vitest, Rolldown, and Oxc, backing the transaction with a $1 million open-source ecosystem fund. This acquisition is highly strategic; as autonomous agents write an increasing proportion of production software, local development environments, compilation pipelines, and bundlers must be optimized for execution speeds that match agent speeds. Cloudflare's goal is to construct a localized, full-stack edge playground. In this sandbox, AI agents can generate, test, bundle (utilizing the highly parallelized, Rust-based Oxc and Rolldown engines), and deploy entire web applications end-to-end within milliseconds. This architecture completely bypasses traditional local machine container bottlenecks, enabling high-velocity agent loops to execute in a fully sandboxed, web-scale edge runtime.

Craig Rosenberg, Chief Platform Officer at Scale Venture Partners and co-founder of Topo, joins AJ Bruno and Asad Zaman to take on the question every founder is wrestling with: can you still build a world-class sales team when OpenAI and Anthropic are handing individual contributors $10 million equity packages? Craig argues you do not have to compete head-on, then lays out the hiring profile to chase instead, the quota-to-comp discipline that keeps packages sane, and why founder brand has become the most reliable pipeline play left as CAC keeps climbing. Topics include enterprise AE compensation, where private equity is still winning the GTM talent war, the Topo playbook for events and data-as-moat, and a bull-versus-bear debate on whether Gong goes public in the next 36 months. Plus, a Quiz Pro Quo on the real customer counts behind Salesforce, HubSpot, and ZoomInfo. Key Takeaways: - Rather than try to outbid OpenAI and Anthropic for talent, build your own farm system and develop people into the role. As Craig Rosenberg, Chief Platform Officer at Scale Venture Partners, put it: "You have to change your hiring profile to a unique profile that's unique to your business, but then you gotta coach 'em up." - A resume from a hot AI lab is not a guarantee of success at your company. As Craig Rosenberg noted, "The person that is going to do well at Anthropic may not do well at Series B," so hire for the stage and the hunger rather than the logo. - On compensation, Craig anchors the package to the role's real value: "you pay for what your wedge costs… if you feel like you have to pay $10 million, then you have a huge problem and you gotta go back to the drawing board." If the number runs away from you, the model is broken. - With CAC climbing and most channels breaking down, founder brand has become the highest-leverage pipeline play. As Craig Rosenberg said, "The value of building a founder brand, when you look at the data, it's amazing," pointing to gains in both pipeline and deal size. Connect with the Hosts & Guests: Host: AJ Bruno, CEO at QuotaPath - https://www.linkedin.com/in/ajbruno3/ Host: Asad Zaman, CEO at Sales Talent Agency - https://www.linkedin.com/in/azaman1/ Guest: Craig Rosenberg, Chief Platform Officer at Scale Venture Partners - https://www.linkedin.com/in/craigrosenberg/ Topline is more than a YouTube Channel: Subscribe to Topline Newsletter: https://toplinemedia.substack.com/ Tune into Topline Podcast, the #1 podcast for founders, operators, and investors in B2B tech: https://www.joinpavilion.com/topline-podcast Join the free Topline Slack channel to connect with 600+ revenue leaders to keep the conversation going beyond the podcast: https://www.joinpavilion.com/topline-slack Chapters: 00:00 Introducing Craig Rosenberg 02:34 Can Anyone Out-Hire The AI Labs? 04:33 Why Craig Isn't Worried 06:52 Enterprise AE Comp Is Climbing 08:21 Founders Overpay For Star CROs 10:53 Why AI Reps Struggle At Series B 14:00 Hire The Slighted CRO 14:42 Quota-To-Comp And Attainment 18:45 Can AI Labs Sustain Growth? 22:20 Where PE Still Wins GTM Talent 27:17 Major Runs Reshape GTM 32:36 The Topo GTM Playbook 37:55 Quiz Pro Quo 47:45 Founder Brand And Rising CAC 58:42 Bulls and Bears

Researchers crack Apple's M5 memory protections with a kernel exploit. An IBM Security executive emerges as a possible CISA pick. Researchers uncover four malicious npm packages.  AI-generated “slop” floods bug bounty programs. Major healthcare breaches hit the HHS tracker, 7-Eleven confirms a breach, and chained OpenClaw AI flaws could enable full host compromise. Santa Clara County sues Meta over alleged scam ads on Facebook and Instagram. Monday business breakdown. Our guest is Jason Madigan, Director of Commercial Cloud Security at Booz Allen, discussing the tension between resilience and data residency laws. A fond farewell for a security pioneer.  Remember to leave us a 5-star rating and review in your favorite podcast app. Miss an episode? Sign-up for our daily intelligence roundup, Daily Briefing, and you'll never miss a beat. And be sure to follow CyberWire Daily on LinkedIn. CyberWire Guest On today's Industry Voices segment we are joined by Jason Madigan, Director of Commercial Cloud Security at Booz Allen, discussing the tension between resilience and data residency laws. If you enjoyed this conversation, check out the full interview here. Selected Reading First public macOS kernel memory corruption exploit on Apple M5 (Calif) IBM executive floated for CISA director as concerns persist for agency (SC Media) Former CISA nominee Sean Plankey named US CEO of defense startup (CyberScoop) New Actors Deploy Shai-Hulud Clones: TeamPCP Copycats Are Here (OX Security) ‘Never-ending' AI slop strains corporate hacking reward schemes (Financial Times) Millions Impacted Across Several US Healthcare Data Breaches (SecurityWeek) 7-Eleven Data Breach Confirmed After ShinyHunters Ransom Demand (SecurityWeek) 'Claw Chain' OpenClaw Flaws Allow Sandbox Escape, Backdoor Delivery (SecurityWeek) Santa Clara County sues Meta over alleged scam ads (San José Spotlight) Exaforce raises $125 million in Series B funding. (N2K Pro Business Briefing) Peter G. Neumann, Who Warned of Computer Security Risks, Dies at 93 (The New York Times) Share your feedback. What do you think about CyberWire Daily? Please take a few minutes to share your thoughts with us by completing our brief listener survey. Thank you for helping us continue to improve our show. Want to hear your company in the show? N2K CyberWire helps you reach the industry's most influential leaders and operators, while building visibility, authority, and connectivity across the cybersecurity community. Learn more at sponsor.thecyberwire.com. The CyberWire is a production of N2K Networks, your source for strategic workforce intelligence. © N2K Networks, Inc. Learn more about your ad choices. Visit megaphone.fm/adchoices

John Graunt was a shopkeeper in 17th-century London who followed his own curiosity to a rather grand result. His work gave rise to the fields of demography and epidemiology. Research: Berke, Olaf, et al. “Celebration day: 400th birthday of John Graunt, citizen scientist of London.” Environmental Health Review. 63(3): 67-69. 2020. https://doi.org/10.5864/d2020-018 Britannica Editors. "John Graunt". Encyclopedia Britannica, 20 Apr. 2025, https://www.britannica.com/biography/John-Graunt Britannica, The Editors of Encyclopaedia. "Sir William Petty." Encyclopedia Britannica, 11 Apr. 2026, https://www.britannica.com/money/William-Petty Clark, Andrew. “Aubrey’s ‘Brief Lives.’” Oxford. Clarendon Press. 1898. https://dn790003.ca.archive.org/0/items/briefliveschiefl01aubruoft/briefliveschiefl01aubruoft.pdf Connor, Henry. “John Graunt F.R.S. (1620-74): The founding father of human demography, epidemiology and vital statistics.” Journal of medical biography 32,1 (2024): 57-69. doi:10.1177/09677720221079826 Eschner, Kat. “People Have Been Using Big Data Since the 1600s.” Smithsonian. April 24, 2017. https://www.smithsonianmag.com/smart-news/people-have-been-using-big-data-1600s-180962949/ Glass, D.V., et al. “John Graunt and His Natural and Political Observations [and Discussion].” Proceedings of the Royal Society of London. Series B, Biological Sciences, Vol. 159, No. 974, A Discussion on Demography (Dec. 10, 1963), pp. 2-37 Published by: The Royal Society Stable URL: https://www.jstor.org/stable/90480 Graunt, John. “Natural and political observations mentioned in a following index, and made upon the Bills of mortality.” Oxford : Printed by William Hall, for John Martyn, and James Allestry, printers to the Royal Society MDCLXV [1665]. http://resource.nlm.nih.gov/2356017R KARGON, ROBERT. “John Graunt, Francis Bacon, and the Royal Society: The Reception of Statistics.” Journal of the History of Medicine and Allied Sciences, vol. 18, no. 4, 1963, pp. 337–48. JSTOR, http://www.jstor.org/stable/24621352 Kelsey, Holly. “Sovereign and the Sick City in 1603.” Shakespeare Birthplace Trust. Aug. 23, 2016. https://www.shakespeare.org.uk/explore-shakespeare/blogs/sovereign-and-sick-city-1603/ Lewin, C. G. "Graunt, John (1620–1674), statistician." Oxford Dictionary of National Biography. August 08, 2024. Oxford University Press. https://www.oxforddnb.com/view/10.1093/ref:odnb/9780198614128.001.0001/odnb-9780198614128-e-11306 Pepys, Samuel. “The Diary of Samuel Pepys.” GEORGE BELL & SONS. London. 1893. Accessed online: https://www.gutenberg.org/cache/epub/4200/pg4200.txt Smith, R.M. (2008). “Graunt, John (1620–1674).” The New Palgrave Dictionary of Economics. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-349-95121-5_758-2 See omnystudio.com/listener for privacy information.