Podcasts about Jevons

Adelaide Climate Action Week co-founder Mark Rowland joins Steve to unpack The Great AI Climate Debate, the Oxford-style contest his organisation staged this week between the “carbon glutton” and “silicon saviour” camps. Rather than settling for headline alarm about data centres guzzling power like mid-sized nations, Mark walks through the nuance: how much of that energy use is genuinely AI rather than general cloud computing, why the time horizon people use to judge AI shapes their verdict, and how concepts like the Jevons paradox and the “time value of carbon” complicate any tidy answer. He leaves listeners with a practical, human-scaled way to think about their own digital footprint. Renmark’s 23rd Street Distillery supplies the South Australian Drink of the Week, and it’s a genuine coup: their single malt has just been named Australian Distillery of the Year at the New York International Spirits Competition, outscoring Glenmorangie, Highland Park and a 31-year-old Crown Royal. Head distiller Paul Burnett returns to the show to talk peat, oak and the fine art of finishing a whiskey in casks that once held tawny, muscat and Tokay. The Musical Pilgrimage closes the episode on a reflective note, with Steve premiering his own composition, Higher Legacy, written for two Adelaide City Council gardeners whose combined century of service tending the city’s parks and squares is honoured on a plaque in Rymill Park — and for everyone, past and present, quietly building something worth protecting. You can navigate episodes using chapter markers in your podcast app. Not a fan of one segment? You can click next to jump to the next chapter in the show. We’re here to serve! The Adelaide Show Podcast: Awarded Silver for Best Interview Podcast in Australia at the 2021 Australian Podcast Awards and named as Finalist for Best News and Current Affairs Podcast in the 2018 Australian Podcast Awards. And please consider becoming part of our podcast by joining our Inner Circle. It’s an email list. Join it and you might get an email on a Sunday or Monday seeking question ideas, guest ideas and requests for other bits of feedback about YOUR podcast, The Adelaide Show. Email us directly and we’ll add you to the list: podcast@theadelaideshow.com.au If you enjoy the show, please leave us a 5-star review in iTunes or other podcast sites, or buy some great merch from our Red Bubble store – The Adelaide Show Shop. We’d greatly appreciate it. And please talk about us and share our episodes on social media, it really helps build our community. Oh, and here’s our index of all episode in one concisepage. Running Sheet: The Great AI Climate Debate 00:00:00 Intro Introduction 00:04:00 SA Drink Of The Week There SA Drink Of The Week this week is 23rd Street Distillery Australian Single Malt Whisky. Steve opens with a pop quiz: name a South Australian product that just outscored Glenmorangie, Highland Park and a 31-year-old Crown Royal at a fraction of the price. It’s whiskey, specifically Renmark’s 23rd Street Distillery, freshly named Australian Distillery of the Year at the New York International Spirits Competition, with their single malt scoring 96 points against entries from 39 countries. Head distiller Paul Burnett, returning for another visit, credits the win to restraint rather than showmanship: a low-level peat profile, around half a percent in the finished blend, used as what he calls a “complexing agent,” paired with an unusually wide range of cask finishes, from first-fill bourbon barrels to old tawny, muscat and Tokay casks. Steve, tasting alongside him, works through notes of honeyed fruit, fig, all-spice and a citrus edge that shifts as the glass warms in his hand, while Paul walks through the distinction between “heads, hearts and tails” in distillation and how the team’s small-batch, six-way finishing blend has quietly shifted in proportion release after release. The pair also compare notes on serving it right: a proper tulip glass, a touch of water, room temperature rather than ice, and, in Paul’s case, a slice of chocolate or nuts alongside it. Steve, for his part, admits an evening spent testing whiskey sours and old fashioneds the night before only confirmed his preference for drinking it neat. 00:35:20 Mark Rowland and Adelaide Climate Action Week Steve opens by declaring his hand: he uses AI daily for work, has solar and a battery, drives an EV, and admits the more he learns about what sits behind that lifestyle, the less smug he gets to feel about it. It’s a fitting frame for a conversation built around nuance rather than tribal certainty. Mark explains why Adelaide Climate Action Week chose an actual Oxford-style debate over a polite panel discussion, tapping into people’s competitive instincts to get them to actually take a position rather than scroll through it. The audience voted before and after: undecided voters dropped from 24% to single digits, and belief that AI will be a net negative for climate rose from 56% to over 70%. The real story, though, was in the follow-up conversations, where the deciding factor wasn’t the argument itself but the time horizon people used. Ask about the next five years and the mood turns pessimistic; ask about 20 to 50 years out and people start to believe things will improve. The pair dig into the “carbon glutton” case, distinguishing AI-specific energy demand from data centres in general, since cloud storage and streaming were already drawing power long before AI arrived. Mark points out that air conditioning, EV charging and industrial electrification are all expected to outgrow AI’s energy demands in the years ahead, and that steel and concrete for data centre construction already account for roughly 14% of global emissions. He also raises a genuinely alarming case: a data centre approval in an African nation that led authorities to cut off a village’s water supply, with fatal consequences, the kind of guardrail failure he says South Australia needs to avoid as its own data centre legislation opens the door to rapid growth. On the more optimistic “silicon saviour” side, Mark describes a proposed Barossa data centre project that would generate two to three times the renewable energy it needs, feed the surplus back into the community, and capture atmospheric moisture from its waste heat to supply water to nearby farmers and wineries, reducing pressure on the Murray. He frames this as “hybrid intelligence” in action: letting machines handle the split-second optimisation work, like grid load-shifting to squeeze more renewables in without new poles and wires, while humans retain the judgement calls machines aren’t suited to. Steve presses on the Jevons paradox, the pattern where efficiency gains simply invite greater use rather than genuine savings, and confesses to his own sprawling AI toolkit, from Claude to NotebookLM to Suno. Mark’s response is refreshingly proportionate: an individual’s prompt volume is negligible against industrial-scale use, and Google’s own sustainability reporting shows the energy and water cost per prompt has dropped by roughly half in a couple of years, even as overall usage climbs. The conversation lands on the “time value of carbon,” the idea that AI’s emissions are happening now while its promised climate benefits, if they arrive at all, are speculative and delayed. It closes with Mark’s own conviction, shaped by a master’s degree in climate change, that humanity’s Anthropocene-era warming is real, drawn partly from personal memory of snow days in his native England that simply don’t happen anymore. Asked to leave listeners with something to hold onto, Mark returns to a theme James Clear would recognise from Atomic Habits: start with the smallest available step. Downloading rather than streaming a favourite album, watching YouTube at 720p instead of 4K on a phone, deleting old emails sitting in a data centre somewhere: none of it solves the problem alone, but it restores a sense of agency against the temptation to slide into overwhelm or apathy. Further information Adelaide Climate Action Week Google’s 11th annual Environmental Report 01:27:56 Musical Pilgrimage In the Musical Pilgrimage this week we listen to Higher Legacy by Steve Davis & The Virtualosos The Musical Pilgrimage takes an unexpectedly personal turn this week. Prompted by a photograph shared in a local Facebook group of a plaque in Rymill Park honouring brothers Antonio and Carmine Lepore, whose combined century of tending Adelaide’s parks and squares began back when Clydesdales still pulled bakers’ carts through the suburbs, Steve wrote an original song called Higher Legacy. Inspired by the plaque’s closing line, borrowed from the idea that a society grows great when people plant trees whose shade they’ll never sit in, the song also nods to Jane Goodall’s advice to Adelaide listeners years ago: when the world’s problems feel overwhelming, start with the square metre you’re standing on. Brought to life with Steve’s virtual session band, The Virtualosos, it’s offered as a piece for anyone who wants to take it, own it, and perform it as their own.Support the show: https://theadelaideshow.com.au/listen-or-download-the-podcast/adelaide-in-crowd/See omnystudio.com/listener for privacy information.

Our Global Head of Thematic and Sustainability Research Stephen Byrd explains why the recent AI infrastructure selloff may reflect technical pressures, not weakening fundamentals.Read more insights from Morgan Stanley.----- Transcript -----Stephen Byrd: Welcome to Thoughts on the Market. I'm Stephen Byrd, Morgan Stanley's Global Head of Thematic and Sustainability Research.Today: Are investors misreading the AI infrastructure selloff?It's Thursday, July 30th, at 10am in New York.The recent selloff in AI infrastructure stocks has raised a familiar question: Is the buildout running ahead of real demand? The market is pulling back and we think that reflects profit-taking, crowded positioning, and forced selling by investors. This is not about weaker fundamentals. But the selloff has brought to light three key concerns, which we think the market is overplaying.The first concern is how much enterprises are willing to pay for AI. The median enterprise employee currently generates less than $11 a month in token spending. That's the fee paid when an AI model processes a request and generates a response.We think there is room for that to increase. From the employer's perspective the economics are compelling. Across workplace applications, the cost to execute the economic task would be $2-$5. And that could save an enterprise $55. That to us suggests companies are likely to spend more, not less, on AI over time.The second debate centers on efficient models, including competitive models developed in China.  And here, policy responses both from the U.S. and China can have an impact as well. Some investors worry that better efficiency means less computing demand. But we see the opposite risk. This is a classic example of Jevons paradox: When something becomes cheaper or more efficient to use, people use more of it. In AI, lower costs can attract more users, encourage more frequent use, and make complicated applications more economical. The scale is striking. Industry leaders estimate that compute demand could double every six months, which would amount to more than a thousand-fold increase in compute over five years. Hyperscalers could quadruple available power capacity to roughly 120 gigawatts by 2028, from about 30 gigawatts in 2025.And that leads to the third debate – whether data centers can secure enough power to keep expanding. It's a valid concern. In the U.S., facilities under construction and contracted grid capacity cover about 30 gigawatts. That's less than half the 68 gigawatts of power that data centers are likely to need from 2026 through 2028. Grid connections can take five to seven years in some regions. Skilled electricians, welders, and pipefitters are in short supply. And local opposition is increasing as communities debate electricity bills, tax incentives, and who should pay for grid upgrades.These are real obstacles, but we view them as delays rather than dead ends. Onsite generation, fuel cells, energy storage, natural gas turbines, and the conversion of existing high-power sites could close the gap, at least partially.We believe much of the recent weakness in AI infrastructure has been driven by technical factors rather than a change in the underlying fundamentals. As AI becomes more capable and cheaper to use, demand for intelligence, compute, and power is likely to keep rising. The global market is fragmented as policy decisions in the U.S. and China shape how growth unfolds. But strong economics should support continued investment.Thanks for listening. If you enjoy the show, please leave us a review wherever you listen and share Thoughts on the Market with a friend or colleague today.

82% of top-grossing apps now take payment outside the app store. Patrick Stuart-Constant's team at Sociaaal ships 4,000 video ads a month, scaling to 10,000. The shift to web-to-app monetization isn't coming. It already happened.Part 1 of a two-part panel from a FunnelFox webinar, with Rocketship HQ as partner. Shamanth Rao moderated the conversation with Jacob Rushfinn (CEO, Botsi), Andrey Shakhtin (CEO, FunnelFox), Patrick Stuart-Constant (CEO, Sociaaal), Mike Gadd (VP Customer Success, Singular), and Elise Zareie (Head of Paid Social, Lingokids) on how paid acquisition teams design the path after the click.Andrey Shakhtin on why off-store attribution beats SKAN on speed, and how 2-5% payment fees unlock room to iterate. Patrick Stuart-Constant on scaling ad volume as a search space problem, and why cheaper AI creative grew his team instead of shrinking it (Jevons paradox). Elise Zareie on why web funnels only work with dedicated ownership inside the UA team. Mike Gadd on why the first renewal is the metric most teams miss. Jacob Rushfinn on the $1M revenue threshold before web funnels pay off, and the 10K to 20K monthly loss to budget for the learning phase.Video Chapters:00:00 Introductions and format02:40 The one shift teams have not priced in07:50 Andrey on the 82% off-store payments finding09:52 Mike on measurement across web and app, platform hell11:45 Elise on creative: lift-and-shift vs TikTok-native15:54 Patrick on the search space, 4K to 10K ads a month18:07 Jevons paradox: cheaper ads, bigger creative team19:30 Elise on where AI helps and where taste is still human21:39 What separates web-funnel winners from teams that quit25:27 Andrey on iteration pace correlating with revenue27:00 Who should not try web funnels: payments complexity28:42 Jacob on the $1M threshold and the learning-phase budget29:36 Common learning phase mistakes and how to short-circuit them32:00 Part 2 drops next weekTopics covered:- The shift to web-to-app monetization and off-store payments- AI creative production at scale, 4,000 to 10,000 ads a month- Web funnels: dedicated ownership vs side project- The $1M revenue threshold for going off-store- Learning phase mistakes on acquisition, funnel, and paywall- Measurement complexity across web and appLearn more:- Original webinar recording: https://www.youtube.com/watch?v=OYPDZlwLhNA- FunnelFox (webinar host): https://funnelfox.com- Rocketship HQ (partner): https://rocketshiphq.com- Jacob Rushfinn (Botsi): https://www.linkedin.com/in/jacob-rushfinn/- Andrey Shakhtin (FunnelFox): https://www.linkedin.com/in/andrey-shakhtin/- Patrick Stuart-Constant (Sociaaal): https://www.linkedin.com/in/patrick-stuart-constant/- Mike Gadd (Singular): https://www.linkedin.com/in/mike-gadd-27a16427/- Elise Zareie (Lingokids): https://www.linkedin.com/in/elise-zareie/- Shamanth Rao (Rocketship HQ): https://www.linkedin.com/in/shamanthrao/

Ömer ve Yaşar Ateş Ölçer'in yepyeni bölümünde Dreame Ev Robotları sponsorluğunda istihdam krizini ve yapay zekayının geleceğe etkilerini tartışıyor!2025 yılı içerisinde sık sık haberlerde karşılaştığımız işten çıkarma haberlerinden yola çıkarak yeni mezunlara açılan işlerde azalmayı, beyaz yaka işlerdeki daralmayı, otomasyona kurban giden işleri ve yapay zekanın yeni işler yaratıp yaratamayacağını değerlendiriyorlar. Sadece istihdam ve ekonomi ile sınırlı kalmayan tartışma kapitalizmin doğası ve geleceğin siyasi sistemlerine dair bir beyin fırtınasına da ilerliyor.00:00 - Giriş01:27 - İşten çıkarma trendi06:25- Büyük teknoloji şirketleri kapitalizme etkisi08:38 - Yapay zekanın beyaz yaka işlere etkisi12:03 - Yapay zekanın istihdama etkisi16:00 - Moravec paradoksu17:25 - Dreame Ev Robotu18:50 - Diplomanın öneminin zayıflaması23:50 - Yapay zekanın becerilerinin hızlı gelişimi25:27 - Jevons paradoksu28:20 - Yapay zeka mevcut toplumu nasıl etkileyecek?30:45 - Ev genci krizi32:18 - Türkiye'de istihdamın geleceği36:50 - Kamu istihdamı öne mi çıkacak?41:08 - Büyük güç mücadelesinin teknoloji yarışına etkisi44:00 - Demokrasinin sonu mu geliyor?48:12 - Verimliliği tekrar düşünmek51:10 - Kapanış

Matt Orsagh spent 25 years inside mainstream finance, including as Senior Director of Capital Markets at the CFA Institute. Then he concluded that ESG was painting the house while it was on fire. Now, as co-founder of the Arketa Institute for Post-Growth Finance, he makes the case that a post-growth world is coming, by disaster or by design. We discuss why degrowth is a diet, not austerity; why green growth collides with physics and the Jevons paradox; and his answer to the Global South objection: Africa should be allowed to grow, it is the Global North that needs the diet. Plus a radical rethink of pensions, a third of the world's assets, built on natural and social capital, not just piles of gold. Listen, then share it with someone who still believes efficiency will save us.

“ I'm a believer in this technology. I believe that the cheaper it is, the more people will use it. So, even if we assume that models will get smaller for the same level of intelligence, the demand to actually have more compute is going to be enormous. And the reason why you want to have the, the compute close to you is not only in terms of performance. As a national security threat, I'm not saying this will happen, but it could happen that, China or US or whoever decides to stop exporting compute to the rest of the world. If that happens today, Europe has zero AI at the scale of data the data center.”For over a decade, technology has promised to make life easier. Social media was meant to connect us, we were told smartphones would simplify our lives, and now artificial intelligence promises to lift even more daily burdens. Yet these advances also raise an important question: when do tools enhance human creativity, and when do they replace the very experiences that give life meaning? While AI holds immense promise, many people face challenges coping with current technological advancements that ‘redefine' human communication, work, education, and democratic values. Today, we examine societal and cultural impacts of AI, identity, inclusion, governance, education, and community well-being. And are speaking with Nacho de Gregorio, author of TheWhiteBox, one of Medium's most-followed AI analysts, for a level-headed conversation about both the remarkable potential and the real limitations of AI.(0:22) The hidden risks of the AI debt bubble(0:55) Shadow borrowing and the data center boom(3:12) The AI compute gap and national security(5:40) Europe's missing infrastructure and tech sovereignty(8:13) The Jevons paradox and the demand for compute(10:25) Why open source AI is the best path forward(12:31) The chess paradox and the future of human art(12:53) Unmeasurable domains and future-proofing your career(14:00) Tutoring and AI in education(15:11) Navigating AI slop and the information ecosystem(16:24) The Pleasure of Waiting vs. The HustleEpisode Websitewww.creativeprocess.info/podInstagram:@creativeprocesspodcast

“ I'm a believer in this technology. I believe that the cheaper it is, the more people will use it. So, even if we assume that models will get smaller for the same level of intelligence, the demand to actually have more compute is going to be enormous. And the reason why you want to have the, the compute close to you is not only in terms of performance. As a national security threat, I'm not saying this will happen, but it could happen that, China or US or whoever decides to stop exporting compute to the rest of the world. If that happens today, Europe has zero AI at the scale of data the data center.”For over a decade, technology has promised to make life easier. Social media was meant to connect us, we were told smartphones would simplify our lives, and now artificial intelligence promises to lift even more daily burdens. Yet these advances also raise an important question: when do tools enhance human creativity, and when do they replace the very experiences that give life meaning? While AI holds immense promise, many people face challenges coping with current technological advancements that ‘redefine' human communication, work, education, and democratic values. Today, we examine societal and cultural impacts of AI, identity, inclusion, governance, education, and community well-being. And are speaking with Nacho de Gregorio, author of TheWhiteBox, one of Medium's most-followed AI analysts, for a level-headed conversation about both the remarkable potential and the real limitations of AI.(0:22) The hidden risks of the AI debt bubble(0:55) Shadow borrowing and the data center boom(3:12) The AI compute gap and national security(5:40) Europe's missing infrastructure and tech sovereignty(8:13) The Jevons paradox and the demand for compute(10:25) Why open source AI is the best path forward(12:31) The chess paradox and the future of human art(12:53) Unmeasurable domains and future-proofing your career(14:00) Tutoring and AI in education(15:11) Navigating AI slop and the information ecosystem(16:24) The Pleasure of Waiting vs. The HustleEpisode Websitewww.creativeprocess.info/podInstagram:@creativeprocesspodcast

In this episode, theoretical neuroscientist Vivienne Ming discusses her new book, Robot Proof - When Machines Have All the Answers, Build Better People.  The conversation examines the intersection of artificial intelligence and knowledge work, exploring how professionals can adapt to an increasingly automated economy. The discussion contrasts the limitations of basic AI task automation with the advantages of human-AI collaboration—the "cyborg" model—for solving complex, ill-posed problems. Ming highlights her research on forecasting market outcomes, the critical role of endogenous motivation, and how the legal industry and other elite professions must rethink entry-level training. Key Topics Discussed Theoretical Neuroscience and Early AI: Ming's background and the evolution of machine learning models from early academic research to modern agentic AI. The Polymarket Experiment: An analysis comparing the forecasting accuracy of standalone AI, unassisted humans, and human-AI collaborators, revealing the superiority of deep human-machine integration. Automation vs. Augmentation: The pitfalls of the traditional "human-in-the-loop" model and why replacing menial tasks often neglects essential human problem-solving skills. Well-Posed vs. Ill-Posed Problems: Identifying the specific areas where AI excels (algorithmic, factual answers) and where human intelligence remains superior (navigating uncertainty and undefined parameters). Labor Disruption and Economic Shifts: Examining historical technological revolutions, the Jevons paradox, and the future demand for specific, highly adaptable human skill sets. Endogenous Motivation: How internal drivers like curiosity, resilience, and perspective-taking predict professional success more accurately than standard extrinsic incentives. Practical AI Strategies: Actionable methods for professionals to refine their skills, including using AI as a critical "nemesis" to challenge assumptions and encourage deep, effortful processing. The Future of Elite Professions: The macro-level challenges facing organizations in developing junior talent—such as associate attorneys—when the entry-level tasks traditionally used for training are automated. Things We Talk About in this Episode Socos: Vivienne Ming's philanthropy and research newsletter (socos.org). Thinking, Fast and Slow: Authored by Daniel Kahneman. Anthropic Research: A study published in Science detailing the productivity of AI-assisted programmers. BCG AI Study: Research analyzing AI integration and performance among management consultants. Raj Chetty: Economic research regarding peer role modeling, education, and socioeconomic mobility.  

Çin'in ucuz yapay zeka modelleri piyasada donanım ihtiyacının eriyeceği korkusunu yaratsa da, ekonomi tarihi bunun tam tersini söylüyor. Midas Podcast'in bu bölümünde, 19. yüzyıldan günümüze uzanan Jevons Paradoksu çerçevesinde, maliyetler düştükçe çip ve bellek talebinin neden katlanarak büyüdüğünü masaya yatırıyoruz. Teknoloji yatırımlarınızda kısa süreli panik hareketlerine değil, piyasayı büyüten gerçek verimlilik yasalarına odaklanmak istiyorsanız, bu bölüm tam size göre. İyi dinlemeler. Midas uygulamasını indir: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://app.getmidas.com/gmih/mie6gpeu⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ X (Twitter): ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://twitter.com/getmidas⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Instagram: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.instagram.com/get_midas/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ YouTube: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.youtube.com/@midasplus⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ TikTok: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.tiktok.com/@midasinkulaklari⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Midas'ın Kulakları: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.getmidas.com/midasin-kulaklari⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Not: Bu içerik, içeriğin yayınlandığı günkü veriler ve haberler baz alınarak hazırlanmıştır. Eğer varsa içerikte geçen hedef fiyat tahminleri, uzman ve analist yorumları bu içeriğin yayınlandığı tarihte geçerlidir. Bu tahmin ve yorumlar zaman içinde değişkenlik gösterebilmektedir. Bu podcast'te yer alan haberler ve haberlerin içerdiği şirketler hakkındaki bilgiler yatırım danışmanlığı kapsamında değildir. Bahsi geçen hisselerdeki; hisse adı, fiyatı ve grafikleri de dahil temsilidir, yatırım tavsiyesi değildir.

A Silicon-Carbon Collaboration.What does AI actually cost?We hear a great deal about the energy, water, and infrastructure required to build AI. Those conversations matter. But they often stop at one side of the ledger.This episode asks a different question: What are we NOT counting?Beginning with data centers and Jevons' Paradox, we explore what it means to think economically—not just in terms of financial cost, but in terms of human cost and human benefit. Along the way, we discuss accessibility, executive function, abuse support, creativity, transportation, and why reducing friction can create entirely new possibilities rather than simply replacing old ones.And, perhaps most importantly, we ask whether the value of new technology should be measured only by what it consumes... or also by what it makes possible.

Despite fears of AI replacing jobs, some sectors are experiencing significant job growth with the integration of AI. Hunter and Judson dive into the Jevons paradox and explore how technological advancements often lead to job reallocation rather than loss. Tune in as they discuss why AI might not take your job but instead transform your industry.    LINKS Podcast Video cainwatters.com Submit a Question Facebook | YouTube | Instagram

Klik je týždenný komentovaný prehľad technologických správ, o udalostiach, ktoré sa udiali vo svete IT, médií a sociálnych sietí. Moderátori: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Ondrej Podstupka⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, Martin Hodás Discord diskusný server nájdete tu: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://discord.gg/dAUW4PCaEh Linky: Slovenská družica Marína https://www.sme.sk/domov/c/do-vesmiru-vyletela-piata-slovenska-druzica-nesie-sladkovicovu-basen Čínska raketa pristála https://x.com/SpoxCHN_MaoNing/status/2075495374618538318 https://x.com/ThosMajor/status/2075921863990235333 Japonsko testuje vesmírnu loď https://x.com/NTDNews/status/2076474238995722243 Elektroautá v číne https://www.youtube.com/watch?v=a46Xp8FtWOg Hank Green a Jevons paradox https://www.youtube.com/watch?v=a6sYYrLTOjQ Apple žaluje OpenAI https://www.theguardian.com/technology/2026/jul/10/apple-sues-openai-trade-secrets Prvé zariadenie OpenAI https://www.bloomberg.com/news/articles/2026-07-14/openai-s-first-device-will-be-moveable-screenless-speaker-built-as-ai-companion Mete hrozí pokuta od EÚ https://ec.europa.eu/commission/presscorner/detail/sk/ip_26_1579 OnePlus končí v USA a EÚ https://www.cnet.com/tech/mobile/oneplus-pulls-out-of-us-and-europe/ Rozhovor o podvodníkoch, ktorí okradli slovenskú obec https://korzar.sme.sk/spis-gemer/c/sest-hodin-manipulacie-aj-videohovor-starostka-opisuje-ako-ju-podvodnici-obrali-o-obecne-tisice Spravili sme chybu, máte pripomienku? Napíšte nám na ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠klik@sme.sk⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Kapitoly 00:00 Úvod01:20 Slovenská družica06:54 Vesmírne správy z Japonska a Číny20:14 AI, súdny spor a noviny od OpenAI34:36 Meta mala zlý týždeň40:02 Návykové sociálne siete46:32 Prečo predĺžili Chat Control50:01 OnePlus končí v EÚ a USA52:47 ZáverSee omnystudio.com/listener for privacy information.

Full show notes and transcript  - https://bit.ly/4puDkZ9Watch on YouTube - https://youtu.be/uPkDBu9sW7I-----Episode Summary:Dara and Matthew open on the news: OpenAI's GPT-5.6 and its new Sol, Terra and Luna tiers, Fable's return via US export controls and what that precedent means, and the rising cost of AI as companies like Uber burn through their budgets. The main event is the human question: as agents take on more of the routine work, how does the analyst's role change, and where does the human add the most value? They trace the shift from data-wrangling to custodianship of context and judgement, weigh Jevons paradox against job losses, and land on the uncomfortable idea that this time the resource being automated is intelligence itself, so the higher rung we would normally climb to may be the one that vanishes.-----About The Measure Pod:The Measure Pod is your go-to fortnightly podcast hosted by seasoned analytics pros. Join Dara Fitzgerald (Co-Founder at Measurelab) & Matthew Hooson (Head of Engineering at Measurelab) as they dive into the world of data, analytics and measurement, with a side of fun.-----If you liked this episode, don't forget to subscribe to The Measure Pod on your favourite podcast platform and leave us a review. Let's make sense of the analytics industry together!

Predictions about artificial intelligence often focus on job losses and shrinking demand for lawyers. Filevine CEO and co-founder Ryan Anderson and product manager John Rizner offer a sharply different forecast. Drawing on the Jevons paradox, they argue greater efficiency will make legal services accessible to more people, encourage deeper legal research, and create work once excluded by cost. AI might reduce the effort required for individual tasks while expanding the overall volume and ambition of legal representation.The shift holds major implications for the access-to-justice gap. Faster drafting, research, and document review would allow lawyers to serve more clients without sacrificing professional judgment. Anderson expects family law, immigration, bankruptcy, criminal defense, and employment litigation to experience some of the earliest growth. Motions, witnesses, and legal theories once abandoned over expense become economically viable, although courts face their own capacity crisis as more disputes and arguments enter the system.Rizner explains how Filevine's legal AI platform, Lois, applies machine learning to one of legal research's oldest problems: traditional citators often return different results. Lois combines citation graphs with semantic analysis to locate opinions discussing related legal doctrines even when no direct citation connects the cases. A panel of models then evaluates potential conflicts and produces a structured memo. The goal is richer legal analysis focused on the precise holding or proposition a lawyer needs, rather than a simple flag attached to an entire opinion.Accuracy still demands disciplined human review. Filevine organizes citation verification into three levels: confirming the cited case exists, determining whether the case supports the claimed proposition, and checking whether the authority is still good law. The conversation also examines Rizner's research into how different large language models approach efficient breach of contract. OpenAI, Google, and Anthropic models produced dramatically different recommendations, revealing embedded legal and economic preferences beneath seemingly neutral answers.The guests also explore how AI changes legal drafting, law firm economics, and the billable hour. Filevine's acquisition of Pincites, now Lois for Word, reflects Microsoft Word's continuing role as the shared language of legal documents, redlines, formatting, and negotiations. Efficiency does not automatically eliminate hourly billing. Lawyers might instead use saved time to produce more thoroughly researched arguments, stronger contracts, and work product approaching senior-level depth. Firms still need incentives rewarding efficiency rather than treating faster work as lost revenue.Looking ahead, Anderson and Rizner predict a proliferation of frontier and open-source models tailored to firms, individual lawyers, and specific client relationships. Legal teams will increasingly pair proprietary knowledge with selected models to produce highly specialized analysis. Yet model choice introduces jurisprudential bias, accuracy risks, and serious training concerns for junior lawyers. AI expands the range of available options, while experienced legal judgment decides which arguments deserve trust, which sources require verification, and which advice should reach the client.John Rizner Slides Filevine Primary Presentation - 2026Listen on mobile platforms:  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ |  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠Substack⁠[Special Thanks to ⁠⁠Legal Technology Hub⁠⁠ for their sponsoring this episode.]⁠⁠⁠⁠⁠Email: geekinreviewpodcast@gmail.comMusic: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠ Transcript:

Nuestros amigos del podcast Arkham: visita arkham.tech Nuestros amigos del podcast Odoo: Agenda una demo en https://www.odoo.com/r/8ho8 En este episodio de Chisme Corporativo nos metemos al negocio que realmente está impulsando la revolución de la inteligencia artificial: los data centers. Descubrimos por qué las tecnológicas están gastando cientos de miles de millones de dólares, quiénes ganan con esta carrera y cómo la energía, la memoria y el dinero podrían convertirse en el verdadero límite del boom de la IA. ¿Estamos construyendo el futuro... o la próxima burbuja?

For over a decade, technology has promised to make life easier. Social media was meant to connect us, we were told smartphones would simplify our lives, and now artificial intelligence promises to lift even more daily burdens. Yet these advances also raise an important question: when do tools enhance human creativity, and when do they replace the very experiences that give life meaning? While AI holds immense promise, many people face challenges coping with current technological advancements that ‘redefine' human communication, work, education, and democratic values. Today, we examine societal and cultural impacts of AI, identity, inclusion, governance, education, and community well-being. And are speaking with Nacho de Gregorio, author of TheWhiteBox, one of Medium's most-followed AI analysts, for a level-headed conversation about both the remarkable potential and the real limitations of AI.(0:00) The AI compute gap and national security(1:56) The hidden risks of the AI debt bubble(4:40) Separating AI hype from job displacements(12:38) Shadow borrowing and the data center boom(17:19) Europe's missing infrastructure and tech sovereignty(21:52) The Jevons paradox and the demand for compute(26:37) Regulatory capture and doomerism(31:12) Why open source AI is the best path forward(42:19) The chess paradox and the future of human art(46:35) Unmeasurable domains and future-proofing your career(53:45) Tutoring and AI in education(1:01:48) Navigating the AI slop and information ecosystem(1:16:00) Finding beauty in the natural world(1:20:18) The pleasure of waiting vs. the hustle(1:35:01) Setting healthy boundaries with technologyEpisode Websitewww.creativeprocess.info/podInstagram:@creativeprocesspodcast

As America marks 250 years of existence, it is worth pausing to ask a question that most people avoid: what is actually true about this country versus what we have been conditioned to believe? The noise coming from cable news, social media algorithms, and political fundraising machines has created a version of America that feels perpetually on the brink of collapse. But the data tells a radically different story. America 250 is not a eulogy. It is a celebration grounded in economic history, human ambition, and the rare national DNA that makes this country unlike any other on earth. The story of America 250 is not just about survival. It is about a country that has repeatedly invented entirely new categories of value from nothing, attracting dreamers from every corner of the globe who recognize something that many native-born Americans take for granted. Understanding what America actually is, rather than what the anger merchants want you to believe, is the starting point for seeing where it is going next. You're listening to Christopher Lochhead: Follow Your Different. We are the real dialogue podcast for people with a different mind. So get your mind in a different place, and hey ho, let's go.   The Anger Industrial Complex Is Manipulating You The most important thing to understand about the current state of American political culture is that the division you feel is largely manufactured. Politicians, legacy media, and social media algorithms have built extraordinarily profitable business models on your outrage. Fundraising emails do not celebrate progress or bipartisan cooperation. They warn you that the other side is coming for everything you love. Cable news stopped booking reasonable people because screaming is more watchable. Then social media arrived with algorithms engineered to identify with inhuman precision exactly what makes you angry, and serve you more of it every hour. Here is what those category leaders of manufactured rage never want you to know. On guns, taxes, immigration, abortion, equal rights, policing, gay marriage, the national debt, and entrepreneurship, Americans mostly agree. 91% of Americans believe anyone regardless of race deserves an equal opportunity to succeed. 94% approve of interracial marriage, up from just 4% in 1958. 81% of Americans support universal background checks, including 80% of Republicans. 94% believe every citizen deserves a fair shot to start and grow a business. These numbers cut cleanly across party lines and receive zero coverage because agreement does not generate revenue. The pattern is consistent and deliberate. Every time Americans broadly agree on something, the machine finds the 5 to 15% on either extreme of the bell curve who do not, puts them on television, feeds them into the algorithm, and collects revenue by monetizing anger manufactured from nearly nothing. A citizen who stops being angry is a bad customer, and that is precisely why the machine never stops running.   America Is a Catapult, Not a Club What makes America 250 worth celebrating is not just its age. It is its architecture. In Gallup surveys conducted across 150 countries since 2007, one question has been asked consistently: if you could move anywhere on earth, where would you go? Every single year, 170 million people choose the United States. The runner-up draws half that number. China has four times America’s population and a foreign-born population of just 0.1%. The United States sits at 15%. People do not want to move to America because it is the best. They want to move here because it is different. Nearly every other country on earth functions like a club, one you are born into or spend a lifetime trying to enter. America was purpose-built as a catapult for people driven by dreams, pirates, innovators, and those desperate enough to bet everything on a different future. The founder of SoftBank, one of the wealthiest people in Japan, was born ethnically Korean and was bullied to the point of contemplating suicide, denied credit in Japanese business specifically because of his ethnicity. That story plays out differently in America, where meritocracy at its best does not ask where you came from or what school you attended. Two families, two wars, two bets on a different future in the same country capture this perfectly. One grandfather left Scotland after World War Two for a rubber factory job in Montreal. One father left Korea to become a janitor and a limo driver in Hawaii. Neither came for comfort. Both came for the removal of limits on what their children could become. America 250 is the story of those bets paying off across generations.   The Jevons Paradox and the Next 250 Years In 1865, British economist William Stanley Jevons noticed something counterintuitive. As steam engines became more efficient and required less coal to do the same work, experts predicted coal consumption would fall. Instead, it exploded. Greater efficiency lowered the cost of power, which expanded adoption, which created entirely new categories of economic activity that had not existed before. Jevons called it a paradox, and it is the single best framework for understanding America’s economic history. From a GDP of roughly 193 million in 1790 to over 30 trillion today, America did not simply get better at existing industries. It invented the railroad, then electricity, then the automobile, then the computer, then the internet. Each one was a new category. Each one created massive value from nothing. The internet alone generated approximately 16 trillion in new global economic value over 30 years, more than half of total world GDP in 1995, built entirely from scratch by entrepreneurs. Before the internet, no one needed a web engineer, a search algorithm, or a social media manager. New categories create new categories. AI is now the next expression of the Jevons paradox at a civilizational scale. Goldman Sachs projects AI will raise global GDP by 7% over the next ten years. PwC projects AI could contribute 15.7 trillion by 2030 alone, nearly matching the internet’s entire 30-year impact in under a decade. If AI creates twice the proportional value the internet did, that is 110 trillion in new economic value built on top of the existing world economy. America 250 is not the end of a story. It is the opening chapter of the most consequential economic category in human history, and America is positioned at its center. To hear more from Christopher Lochhead and about America 250 & beyond, download and listen to this episode. You can also check out his thoughts on America as a Different Category of Country.   We hope you enjoyed this episode of Christopher Lochhead: Follow Your Different™! Christopher loves hearing from his listeners. Feel free to email him, connect on Facebook, X (formerly Twitter), LinkedIn, and subscribe on Apple Podcast / Spotify!  

“ I'm a believer in this technology. I believe that the cheaper it is, the more people will use it. So, even if we assume that models will get smaller for the same level of intelligence, the demand to actually have more compute is going to be enormous. And the reason why you want to have the, the compute close to you is not only in terms of performance. As a national security threat, I'm not saying this will happen, but it could happen that, China or US or whoever decides to stop exporting compute to the rest of the world. If that happens today, Europe has zero AI at the scale of data the data center.”For over a decade, technology has promised to make life easier. Social media was meant to connect us, we were told smartphones would simplify our lives, and now artificial intelligence promises to lift even more daily burdens. Yet these advances also raise an important question: when do tools enhance human creativity, and when do they replace the very experiences that give life meaning? While AI holds immense promise, many people face challenges coping with current technological advancements that ‘redefine' human communication, work, education, and democratic values. Today, we examine societal and cultural impacts of AI, identity, inclusion, governance, education, and community well-being. And are speaking with Nacho de Gregorio, author of TheWhiteBox, one of Medium's most-followed AI analysts, for a level-headed conversation about both the remarkable potential and the real limitations of AI.(0:22) The hidden risks of the AI debt bubble(0:55) Shadow borrowing and the data center boom(3:12) The AI compute gap and national security(5:40) Europe's missing infrastructure and tech sovereignty(8:13) The Jevons paradox and the demand for compute(10:25) Why open source AI is the best path forward(12:31) The chess paradox and the future of human art(12:53) Unmeasurable domains and future-proofing your career(14:00) Tutoring and AI in education(15:11) Navigating AI slop and the information ecosystem(16:24) The Pleasure of Waiting vs. The HustleEpisode Websitewww.creativeprocess.info/podInstagram:@creativeprocesspodcast

The biggest AI stocks have had a remarkable run – but questions still remain. Our Head of Americas Specialty Sales, Thomas Wigg, speaks with Global Head of Thematic and Sustainability Research Stephen Byrd and Global Head of Public Policy Research Ariana Salvatore about the competition and durability of the investment cycle.Read more insights from Morgan Stanley.----- Transcript ----- Thomas Wigg: Welcome to Thoughts on the Market. I'm Tom Wigg, Morgan Stanley's Head of Americas Specialty Sales. Stephen Byrd: I'm Stephen Byrd, Morgan Stanley's Global Head of Thematic and Sustainability Research. Ariana Salvatore: And I'm Ariana Salvatore, Morgan Stanley's Head of Public Policy Research. Thomas Wigg: Today, the rally in AI CapEx beneficiaries has taken a breather in recent weeks on concerns of competition from open-source models, backlash to token-maxxing, and growing political opposition to data center builds. It's Tuesday, July 7th at 10am in New York. Let's start with you, Stephen. There's a lot of discussion recently around a backlash at token-maxxing. Essentially, enterprises trying to curtail their high spending on AI tokens from the frontier labs, and, in many cases, shifting to cheaper open-source China models. Can you first offer some perspective here on the value of tokens for enterprises? I know you have a popular token factory model that walks through the economics of agents. Stephen Byrd: Yeah, Tom, we do have this model that really walks through token economics, both from the adopter side as well as the hyperscaler side. So, let's do the adopter side. So, there's a study out that shows a whole range of enterprise use cases of AI, and the average single use case that they identify would save a company about $55 or provide that much benefit. And while we don't know exactly how many tokens it will require, we can make some educated guesses as to a typical token usage to achieve that $55 outcome. And we know that a typical American model, though this varies a lot, you can think of as the cost per million tokens being in the range of $5 per million. Some will be lower, some will be higher. So, for a few dollars of token cost, an enterprise can generate benefit of $55. So that doesn't make me overly concerned about token spend and concerns about token-maxxing. I know we're going to get into that, but the foundation here is really good in the sense that enterprise use cases are very much in the money. Thomas Wigg: How do you think market share ultimately shakes out on tokens? Do the cheaper models overtake the frontier AI labs? Do tokens bifurcate based on the complexity of workloads? How do you think this plays out? Stephen Byrd: What we continue to see is this relentless pace of innovation and cost reduction. So, the frontier keeps going out – meaning model capabilities continue to increase, and, with that, we see enterprise adoption growing quite a bit. Long way to say there is a role for both the frontier as well as these open-source models, and we'll continue to see both flourish. What I see is a lot of tokens will be spent on open-source models. A lot of the value will be in the higher end models because that's where enterprises are going to go. Let me give you an example. I was speaking with one of our programmers about a recent project, and he used a very high-end coding tool, an American coding tool. And for him, that incremental cost of the tokens was very much worth it. And here's a very practical example as to why it makes sense for many enterprises to use the higher end models. If a coding tool gets one of the thousands of lines of code wrong, the cost to remediate is very, very high. In other words, that incremental cost – in this example I'm thinking of, it's a few dollars incremental cost – is so worth it because if the quality is not there, the cost to any enterprise to go back and remediate is so high. And that's true in a lot of enterprise use cases, but not in every use case. And what we are seeing is these open-source models that are cheaper will be very good for a variety of more mundane use cases that are still very valuable. That said, what we've seen in data from places like OpenRouter is dollar-weighted, meaning valued by enterprise spend, the vast majority is still the proprietary models. But even within proprietary models, we could have more expensive and less expensive models. You do not need to go to the frontier. Where I come out on all this is that I'm very confident that the demand for compute is going to exceed the supply. What is difficult to exactly know is who are the winners, what is the exact mix. But the fundamentals of the demand for compute look extremely strong. Thomas Wigg: So, I think you just gave me the answer, but I do want to bring this all back to AI CapEx. Now, last year, when the market sold off on Deep Seek concerns, the concept of Jevons paradox ultimately prevailed, where the cheaper pricing led to even greater demand and CapEx went higher.Do you think the same plays out here? Stephen Byrd: It does look that way very much. And the Jevons paradox dynamic is what we still see today in the sense that as the models get better, what we can do with the models increase, the cost of tokens will keep dropping, the cost of compute will keep dropping.But let's talk about what might derail that, just to make sure we're thinking about all the risks. If somehow commoditized models could perform at the same level as proprietary models in all situations, then I would feel differently. But I don't see that. What I see is that these newer models really do have capabilities that are fairly breathtaking and that are worth that extra money. But if somehow, we hit a wall where these models aren't getting better and therefore the sort of the open models are going to catch up, then I'd feel differently about that. This is where Ariana will, will come in in terms of policy and, you know, this comes up a lot when we think about U.S. versus China. How do we think about, you know, access to different models? How do we think about the cost of different models? What about the risk of appropriation of capabilities by the Chinese firms, for example? That comes up a lot in policy circles. But the base case that I have is this just looks more like Jevons paradox, and there's going to be continued innovation, continued reduction in the cost of producing these services from these models. That looks like more of the same. Thomas Wigg: Let's shift to Ariana to talk about the political angle here. The cover of Barron's over the weekend was a guy wearing a no data centers T-shirt. And this does seem to be one of the few bipartisan issues of agreement heading into the midterms.The stat that the article gave was that 75 data center projects worth $130 billion were blocked or delayed in 1Q26, which is equal to the total number for 2025. This is according to Data Center Watch. Now, most of this is in blue states like New York, Michigan, Illinois, Minnesota considering a statewide moratorium, but you're also seeing Pennsylvania, Arizona, Ohio, parts of Texas restricting tax incentives here. So as this gets louder into the midterms, how do you think this plays out? Ariana Salvatore: So, this is definitely one of the big wedge issues, not just for the midterm elections, but for 2028. And to your point, it's expanding into something that's got bipartisan momentum behind it. Our view is that as long as the Trump administration is in power, something like a federal ban is unlikely to come to fruition. That's because we think the administration is still broadly supportive of the AI data center build-out. And I think even if you were to see a Democrat in office further down the road, that position is the same. And the reason is, it's just too difficult to imagine the U.S. giving up that strategic imperative relative to China. So, while it is true that voters are against AI, while it is true that you are seeing these sorts of local efforts pick up steam, it's also the case that China is accelerating its own AI build-out – not just domestically, but around the rest of the world too. It's also the case that they are kind of tweaking some export restrictions on inputs for some of these data centers, and those geopolitical realities, I think, are hard to ignore. So, at the end of the day, there is a broader strategic imperative here that both Democrats and Republicans kind of recognize and get behind. Now, what does that mean in the near term for the build-out? I think it's not that you're going to see a real pushback or moratorium so much as a conditional build-out.That means you're going to see data centers have to incorporate things like grid modernization in their contracts, agree to longer term investments, for example. Do something that benefits the communities or give it back in some way. And I think that's kind of the policy trajectory in addition to the administration continuing to lean on tech companies to basically, you know, square the circle here and find some way to make this more affordable for, you know, local constituents. Thomas Wigg: Stephen, let me get your take on this too, because I know you live in the D.C. area, and you have a lot of political conversations like you referenced earlier. How do you think this plays out? Is it a red state versus blue state dynamic? And if what Ariana says comes to fruition, where it's a conditional build-out in terms of either giving back to the community or ensuring certain prices or certain technologies behind the meter, in front of the meter, does that have implications for certain areas of the market? Stephen Byrd: Yeah. First, I think Ariana's points were all spot on. I just want to, kind of, build on that and, and dive into it a little more detail. A few things. The politics are, from my perspective, not being the expert that Ariana is, I find them a little strange – in the sense that at the federal level, we have one dynamic, and at the state and local level, we have a bit of a different dynamic. And what I mean by that is, at the federal level, I think it's becoming increasingly clear just how geopolitically important AI supremacy is. As these models get more capable, I think it's pretty clear that the Trump administration really sees just how potent these tools are from a geopolitical point of view. So that points in the direction of wanting to support AI and wanting to ensure that the United States has a leading and dominant position in terms of AI capabilities. Pause there, and then go to your point about, sort of, the local and state level. Building on what Ariana said, what I see are basically two approaches to data center development. In states where the utility is vertically integrated, meaning they control everything, like Louisiana, I do see a path where – in those kinds of states where the politics are a bit more favorable – you could develop a data center connected to the grid, where the data center developer is paying full freight and then some. Meaning that they are providing back to the community, they're providing sort of net benefits, and there should be plenty of capital to make that work and really support all constituents. That can work – in a state where the politics work – because utilities are really weather vanes from a political point of view. So, if their state supports data center development, they will more likely support a data center development. The other approach, though, in many states, whether it's deregulated or it's in a state where the politics are a little less favorable. Which, to your point on the cover of Barron's, it's a lot of states, what I'm increasingly seeing is that the developers are going to go off grid. And they just don't want to show any impact to the community that could be considered negative. So, no use of water, no use of power, and hopefully have a, you know, low or zero emissions profile to show no impact at all. Even then, you want to give back to the community. But the view there is, look, we want to sidestep all of these concerns that we might be causing impacts to the grid by just not being connected. So, I think we're going to see a whole lot of off-grid data center projects. That's mostly natural gas turbines and fuel cells, that general approach. Energy storage will be required in a big way. That's not easy to do. So, in the context of delays there, the Bitcoin players who do have grid access today are clearly seeing a lot of demand for their products. So, I would say politics is now a huge issue that's showing up. The other thing I'd flag is often local communities and states are rejecting projects and using permit requests as a way to do that. So, for example, if your data center needs an air permit because your turbines are going to emit some kind of an, you know, sulfur dioxide, et cetera, into the air, you can run into trouble there. If your data center requires water and you need a water permit, you can run into trouble. So, that's causing these developers to try to find approaches that really minimize or eliminate the need for those kinds of permits. Thomas Wigg: Stephen and Ariana, thank you for taking the time. And to our audience, thank you for listening. If you enjoy Thoughts on the Market, please leave us a review wherever you listen to the show and share the podcast with a friend or colleague today.*****Tom Wigg is a member of Morgan Stanley's Institutional Equity Division and is not a member of Morgan Stanley's Research Department. Unless otherwise indicated, his views are his own and may differ from the views of the Morgan Stanley Research Department and from the views of others within Morgan Stanley.

82% of top-grossing apps now take payment outside the app store. Patrick Stuart-Constant's team at Sociaaal ships 4,000 video ads a month, scaling to 10,000. The shift to web-to-app monetization isn't coming. It already happened.Part 1 of a two-part panel from a Funnelfox webinar, with Rocketship HQ as partner. Shamanth Rao moderated the conversation with Jacob Rushfinn (CEO, Botsi), Andrey Shakhtin (CEO, FunnelFox), Patrick Stuart-Constant (CEO, Sociaaal), Mike Gadd (VP Customer Success, Singular), and Elise Zareie (Head of Paid Social, Lingokids) on how paid acquisition teams design the path after the click.Andrey Shakhtin on why off-store attribution beats SKAN on speed, and how 2-5% payment fees unlock room to iterate. Patrick Stuart-Constant on scaling ad volume as a search space problem, and why cheaper AI creative grew his team instead of shrinking it (Jevons paradox). Elise Zareie on why web funnels only work with dedicated ownership inside the UA team. Mike Gadd on why the first renewal is the metric most teams miss. Jacob Rushfinn on the $1M revenue threshold before web funnels pay off, and the 10K to 20K monthly loss to budget for the learning phase.Video Chapters:00:00 Introductions and format02:40 The one shift teams have not priced in07:50 Andrey on the 82% off-store payments finding09:52 Mike on measurement across web and app, platform hell11:45 Elise on creative: lift-and-shift vs TikTok-native15:54 Patrick on the search space, 4K to 10K ads a month18:07 Jevons paradox: cheaper ads, bigger creative team19:30 Elise on where AI helps and where taste is still human21:39 What separates web-funnel winners from teams that quit25:27 Andrey on iteration pace correlating with revenue27:00 Who should not try web funnels: payments complexity28:42 Jacob on the $1M threshold and the learning-phase budget29:36 Common learning phase mistakes and how to short-circuit them32:00 Part 2 drops next weekTopics covered:- The shift to web-to-app monetization and off-store payments- AI creative production at scale, 4,000 to 10,000 ads a month- Web funnels: dedicated ownership vs side project- The $1M revenue threshold for going off-store- Learning phase mistakes on acquisition, funnel, and paywall- Measurement complexity across web and appLearn more:- Original webinar recording: https://www.youtube.com/watch?v=OYPDZlwLhNA- FunnelFox (webinar host): https://funnelfox.com- Rocketship HQ (partner): https://rocketshiphq.com- Jacob Rushfinn (Botsi): https://www.linkedin.com/in/jacob-rushfinn/- Andrey Shakhtin (FunnelFox): https://www.linkedin.com/in/andrey-shakhtin/- Patrick Stuart-Constant (Sociaaal): https://www.linkedin.com/in/patrick-stuart-constant/- Mike Gadd (Singular): https://www.linkedin.com/in/mike-gadd-27a16427/- Elise Zareie (Lingokids): https://www.linkedin.com/in/elise-zareie/- Shamanth Rao (Rocketship HQ): https://www.linkedin.com/in/shamanthrao/

For over a decade, technology has promised to make life easier. Social media was meant to connect us, we were told smartphones would simplify our lives, and now artificial intelligence promises to lift even more daily burdens. Yet these advances also raise an important question: when do tools enhance human creativity, and when do they replace the very experiences that give life meaning? While AI holds immense promise, many people face challenges coping with current technological advancements that ‘redefine' human communication, work, education, and democratic values. Today, we examine societal and cultural impacts of AI, identity, inclusion, governance, education, and community well-being. And are speaking with Nacho de Gregorio, author of TheWhiteBox, one of Medium's most-followed AI analysts, for a level-headed conversation about both the remarkable potential and the real limitations of AI.(0:00) The AI compute gap and national security(1:56) The hidden risks of the AI debt bubble(4:40) Separating AI hype from job displacements(12:38) Shadow borrowing and the data center boom(17:19) Europe's missing infrastructure and tech sovereignty(21:52) The Jevons paradox and the demand for compute(26:37) Regulatory capture and doomerism(31:12) Why open source AI is the best path forward(42:19) The chess paradox and the future of human art(46:35) Unmeasurable domains and future-proofing your career(53:45) Tutoring and AI in education(1:01:48) Navigating the AI slop and information ecosystem(1:16:00) Finding beauty in the natural world(1:20:18) The pleasure of waiting vs. the hustle(1:35:01) Setting healthy boundaries with technologyEpisode Websitewww.creativeprocess.info/podInstagram:@creativeprocesspodcast

Fresh out of the studio, Benedict Evans, independent technology analyst and author of AI Eats the World, returns to explore whether the AI model layer is becoming commodity infrastructure. Benedict argues there is no winner-takes-all effect in models yet, drawing parallels to telecoms, cloud, chips and the fiber bubble to ask where durable value actually accrues when everyone runs similar infrastructure on similar tokens. He unpacks why the chatbot remains a poor interface, introduces the "blank screen" and "jagged frontier" problems that keep software companies alive, and explains why large language models inherently give you "the average." Closing the conversation, Benedict reflects on the indicators that would show AI has truly eaten the world — and why the answer is better products, not better models."When you automate away work, you can always see the jobs that are going away because they're right there. And you don't know what the new jobs are going to be. Human needs are infinite. How many people are earning a living from making podcasts now? Imagine predicting that 10 years ago. There's a stage in the evolution of the market where like if you're still arguing about that, you're an idiot. But there's a stage at the beginning where you might have opinions about some of these questions, you're probably not even asking the right questions. That, I think, is where we are with this stuff today." — Benedict EvansEpisode Highlights: [00:00] Quote of the Day by Benedict Evans from AI Eats the World[01:16] The public market test: what are investors buying?[04:21] How far up the stack can models go?[05:30] Models can't build all the apps themselves[06:00] The thesis: models as commodity infrastructure[07:52] "All the value went up the stack"[08:24] Chips and Rock's Law: down to three players[11:23] The 1999 reseller story: one-time sales[13:28] The S-curve framing of technology[16:38] You're probably not asking the right questions on AI[18:02] "If this works, we're competing with a Mac"[20:25] Incumbents make it a feature[22:14] Big tech "killing startups" is overstated[24:39] Cowork as the new spreadsheet[26:01] The blank-screen and jagged-frontier problems[29:00] The hard part isn't writing the code[31:25] "What a good answer would probably look like"[33:38] The job displacement debate[37:38] Jevons paradox and the lump-of-labour fallacy[40:30] LLMs inherently give you the average[42:36] Why you really hire McKinsey[45:33] Punk versus prog rock: outside the training data[49:00] Automating ever-higher human functions[49:55] Why this is unanswerable: no theory of scaling[51:30] Indicators that AI has eaten the world[54:53] The solution isn't a better model[56:39] Where to find Benedict EvansProfile: Benedict Evans, Independent Technology AnalystLinkedIn: https://www.linkedin.com/in/benedictevans/Website: https://www.ben-evans.com/newsletterPodcast Information: Bernard Leong hosts and produces the show. The proper credits for the intro and end music are "Energetic Sports Drive." G. Thomas Craig mixed and edited the episode in both video and audio format.Here are the links to watch or listen to our podcast.Analyse Podcast Main Site: https://analysepodcast.comAnalyse Podcast Spotify: https://open.spotify.com/show/1kkRwzRZa4JCICr2vm0vGl Analyse Podcast Apple Podcasts: https://podcasts.apple.com/us/podcast/analyse-asia-with-bernard-leong/id914868245 Analyse Podcast LinkedIn: https://www.linkedin.com/company/analyse-podcast/Sign Up for Our This Week in Asia Newsletter: https://www.analysepodcast.com/#/portal/signup Subscribe Newsletter on LinkedIn https://www.linkedin.com/build-relation/newsletter-follow?entityUrn=7149559878934540288

Code-based PCB design is reshaping how engineers build hardware, and in this OnTrack Podcast episode, host Zach Peterson sits down with Ioannis Papamanoglou and Narayan Powderly, co-founders of atopile, to explore their code-first, AI-driven approach to PCB design. Drawing on backgrounds at Tesla and across the electronics industry, the founders explain why hardware has lagged behind software tooling and how capturing engineering intent in code can unlock automation across the entire design process — from requirements capture to schematic capture and layout. You'll learn why the real bottleneck in AI for PCB design is tooling and orchestration rather than raw model capability, how deterministic and nondeterministic layers work together, and why the designer's role is shifting toward system architecture as low-level boilerplate disappears. The conversation also covers test board generation, the Jevons paradox effect on engineering resources, supply chain and firmware integration, and a live demo showing atopile working alongside Altium Designer. Whether you're a PCB designer, hardware engineer, or curious about AI in electronics, this episode offers a grounded look at where code-based hardware design is headed.

Sign up for Practi, a new platform that helps law firms use subscription billing.Here are the top 5 takeaways from this episode:* Lawyers need an AI strategy and policy first. Before adopting any tools, firms must have a written AI policy, even if it simply says no tools are approved yet. Without one, a staff member using an unapproved (non-enterprise) AI tool can cause an ethical breach if client data ends up in model training.* Stick to two AI tools, not a dozen. Jennifer recommends picking one AI within your existing workspace (Copilot if on Microsoft, Gemini if on Google) plus one secondary tool for drafting or checking work. Chasing every new model is counterproductive. Depth beats breadth.* Document infrastructure is the real foundation. Before AI can be useful, a firm's documents need to be organized, accessible, and OCR'd where necessary. Getting documents into a state where an AI can actually “talk” to them is the unglamorous but critical first step.* Claude (especially via Claude Code/Cowork) is the top recommendation for legal writing. For transactional work requiring a long context window, Jennifer sees Claude as unmatched. She's actively installing Claude's Cowork integration for clients, who are amazed at its ability to handle contract redlines directly in their workflow.* AI increases productivity but also workload. Jennifer invokes Jevons' Paradox: AI tools make lawyers faster, but that extra time tends to get filled with more work. The real win is choosing intentionally: take on more clients, deepen client relationships, or bill at a higher rate, rather than just working more hours.__________________________Want your question to be answered on a future show? Fill out this short survey.Have subscription model question? Check out this free resource to ask all of your questions at notebook.practi.ai.Check out Law Tech AI.Sign up for Paxton, my all-in-one AI legal assistant, helping me with legal research, analysis, drafting, and enhancing existing legal work product.Get Connected with SixFifty⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, a business and employment legal document automation tool.Sign up for ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Gavel⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, an automation platform for law firms.Visit ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Law Subscribed⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to subscribe to the weekly newsletter to listen from your web browser.Prefer monthly updates? Sign up for the Law Subscribed Monthly Digest on LinkedIn.Check out ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Mathew Kerbis'⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ law firm ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Subscription Attorney LLC⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.Want to use the subscription model for your law firm? Click here to sign up for a new platform that helps law firms use subscription billing. Get full access to Law Subscribed at www.lawsubscribed.com/subscribe

Vad ska vi göra när AI har tagit alla jobb? Och vilka nya jobb kommer eventuellt uppstå? Med Hampus, Viktor, Lars och Jacob. TIDSSTÄMPLAR [00:00:00] Intro [00:03:00] USA stänger av Anthropic Fable för utländska användare – "kill switch" i AI-kapprustningen [00:12:00] Historiska jobbomvandlingar: väckaren, stickramen och spinneriet [00:19:00] Kommer AI leda till massarbetslöshet? MIT-studien "Canaries in the Coal Mine" och Jevons-paradoxen [00:25:00] AGI-timing: Dario Amodei, Elon Musk och Marc Andreessen om när superintelligensen kommer [00:31:00] Komparativa fördelar vs AI – och vad IQ-kurvan säger om framtidens arbetsmarknad [00:37:00] Vad skulle du göra om du inte behövde jobba? Lars drömmer om tåg och fotboll [00:44:00] 40-timmarsveckan – historisk relikt eller permanent struktur? [00:48:00] Råd till nyexade: initiativkraft, nyfikenhet och att hålla sig à jour med verktygen [00:52:00] Eric Schmidt utbuad på Harvard – och vad det säger om mänsklighetens förhållande till förändring [00:53:00] AI-attityder: 30% positiva i USA, 70% positiva i Kina – varför är det tvärtom mot vad man tror? [00:57:00] Framtidens yrken OM PODDEN Marknaden är en podd om börs, ekonomi och finans. Vi som gör den är Hampus Brodén, Johan Isaksson, Petter Hjerstedt, Viktor Fritzén, Lars Jörnow och Jacob Bursell. Följ oss på X: https://x.com/marknadspodden Hör av er till oss på jacob@monopolmedia.se

We talk to Michał Zalewski (lcamtuf) about the vulnpocalypse and if we even need fuzzers anymore. This episode may be export controlled at a future date.Watch on YouTube: https://www.youtube.com/watch?v=uI9CSgB4p9oTranscript: https://securitycryptographywhatever.com/2026/06/14/facing-the-vulnpocalypse-with-lcamtufhttps://github.com/google/aflhttps://www.reddit.com/r/claude/comments/1tqtenf/anthropic_said_today_that_mythos_is_coming_to_all/https://github.com/google/clusterfuzzhttps://en.wikipedia.org/wiki/Jevons_paradoxhttps://en.wikipedia.org/wiki/XZ_Utils_backdoorhttps://en.wikipedia.org/wiki/Brighton_hotel_bombinghttps://curl.se/https://ftp.openbsd.org/pub/OpenBSD/patches/7.8/common/025_sack.patch.sighttps://www.wired.com/story/last-pass-vulnerability-password-safe/https://nostarch.com/tangledwebhttps://nostarch.com/silence.htmhttps://nostarch.com/practical-doomsdayhttps://nostarch.com/secret-life-of-circuitshttps://www.youtube.com/c/3blue1brown"Security Cryptography Whatever" is hosted by Deirdre Connolly (@durumcrustulum), Thomas Ptacek (@tqbf), and David Adrian (@dadrian)

Dans cet épisode nous allons nous demander si le paradoxe de Jevons est applicable à l'IA et pourquoi.

00:01 1999 igjen: to skrekkfilm-hiter og «this time it's different»00:04 Rekordbelåning og margin debt på all time high00:05 Opsjonsjaget vi ikke har sett siden 198700:08 Short gamma, marketmakere og spiralen som ga «Red Friday»00:14 Ingenting virket: bare lang volatilitet beskyttet00:17 Laveste korrelasjoner på to år og VIX opp 40 prosent00:18 Bank of America: «here be dragons» og ledighet mot inflasjon00:20 Bilen, AI og Jevons-paradokset00:24 SpaceX som datasenterselskap, ikke rakettselskap00:30 Børsnotering denne uka: 1770 milliarder og Musks absolutte makt00:31 S&P-nekten mot FTSE, Russell og MSCI00:32 Lockup-kalenderen og dagen å frykte: seks måneder og fire dager00:35 Grok mot Groq og «race to zero» i modellene00:40 Midtøsten: Trump mot Netanyahu og oljeprisen00:44 Hva folk ikke ser på nå: bear flattening og carry trades som ryker00:47 Dollar over 161 og japansk intervensjon00:49 Hudson River Trading og datasenteret i Norge00:51 Norge har misforstått seg selv: fisk, olje, rå kraft og nå compute00:53 Å raffinere compute: Skygard, spillvarme og 10X på krafta00:58 Compute som multiplikator: fra 10x-ere til 100x-ere01:00 Budsjettforliket, Mímir Kristjánsson og minstepensjonistene01:05 Å prestere når alt er mulig: fokus, nysgjerrighet og flytskjemaer01:11 Telefonen som heroin: reels, 24-timers reset og hjernen tilbake01:19 Trikkedrapet og situational awareness01:24 Varsler i stedet for å glo på skjermen: gull/sølv og momentum01:35 1998: LTCM, doblede posisjoner og banken som tapte 900 millioner01:45 Andrew Left, Citron og short-saken som ble svindel01:50 Oraclum, superforecasters og nordmannen på topp01:56 Drewry-indeksen, VM-frakt og Fifas fredspris til Trump Hosted on Acast. See acast.com/privacy for more information.

June 2, 2026: Senator Bernie Sanders wants the federal government to own half of OpenAI, Anthropic, and every major AI company in America — and he's framing it as reclaiming stolen public knowledge. We break down exactly how his American AI Sovereign Wealth Fund Act would work, why the Norway comparison falls apart, and what would actually happen to valuations, talent, and American competitiveness if it ever got close to passing. Then: Apollo Global Management's chief economist says there is zero evidence AI is killing jobs — and the data may actually back him up. We look at Jevons paradox, the AI washing phenomenon, and why the aggregate labor market story is more encouraging than the doom headlines suggest. And finally: new Federal Reserve research reveals that 64% of the rise in young worker unemployment since the pandemic traces back to remote work, not AI — and why being willing to go to the office five days a week may be the single best career move a worker in their twenties can make right now.

Most of the AI timeline debate happens in software. Benchmark scores, model releases, the shape of the capability curve. Jon Billow watches a different number for a living: lead times.Billow is on the leadership team at BNS, a firm that manufactures and installs electrical and communication infrastructure. The same critical power equipment his teams put into data centers also goes onto Navy and Coast Guard ships, more than 150 of them. He emailed John Sherman because he thinks the people forecasting AI's arrival are missing what he sees on the construction side every week. The buildout can only move as fast as its slowest part, and right now almost every part is backed up for years.That email is what got him on the show. Here is the heart of what he laid out.The constraint nobody prices inTo bring a large data center online, Billow says, a long list of things has to land at the same time: permitting, grid interconnect, critical power, cooling, and the compute itself. Miss one and the whole project waits. And nearly every item on that list carries a backlog measured in many months, sometimes years.The pinch point he keeps returning to is critical power equipment. According to Billow, the orders all funnel back to roughly five manufacturers, Eaton, ABB, Schneider, GE Vernova among them, and all of them are slammed. He notes that even the US government is having a hard time getting its allocation for ship programs, because it is standing in the same line as every hyperscaler. On top of that, more municipalities are now requiring data centers to bring their own behind-the-meter power generation, which adds another category of equipment backlog and a skill most operators have never needed before. Hooking up to the grid is one thing. Building gas turbines and finding electricians who can parallel generators is another, and the skilled trades are already stretched thin.A factor of five to sevenSherman pushed him to put a number on the gap. If a company says a project lands in a year, how far off is that really?Billow's read: the US has roughly 50 gigawatts of total data center capacity today, with about a quarter of it allocated to AI. Around five gigawatts are under active construction and another seven to twelve sit in backlog. Set that against the order-of-magnitude jumps the labs are talking about and his estimate is blunt. “If I was to be a betting man I would say it's in the order of five to seven years.” Whatever timeline you have been handed, in other words, multiply it.The tells from inside the labsHe pointed to two recent signals that the infrastructure is already the limiting factor. OpenAI walking back a large commitment tied to its Sora video product, which Billow reads as a company looking at finite compute and deciding where to spend it. And Anthropic delaying a model, which he attributes partly to security concerns and partly to the reality of constrained compute capacity. The software keeps leapfrogging. The ground underneath it does not move at the same speed.Why this could be good newsBillow does not frame any of this as a reason to relax. He frames it as time. If the physical buildout runs years behind the hype, that is runway to get governance and alignment right rather than scrambling after the fact. He drew the parallel Sherman's audience knows well, comparing the moment to how the world slowly built doctrine around nuclear risk, and argued the work now is to use the delay deliberately.His closing image stuck with us. He said he wants to tell his grandkids that we were building the car while it was going down the road at 55 miles an hour, but we had the presence of mind to put in seat belts because we knew who was in the back seat.Where they did not agreeThe conversation did not paper over the tension. Sherman described his time in Holly Ridge, Louisiana, a town of about 2,000 mostly elderly people living next to a data center he compared to the size of Manhattan, with construction dust in the air and water residents will not drink. He found it overwhelmingly sad. Billow sees the same structures differently, as a testament to human ingenuity that can be sited and built responsibly if we choose to. Both things sat in the room at once, and the episode is better for letting them.Going deeperWe pulled the headline argument into this piece. The full breakdown for paid subscribers goes into the parts that get more technical and more political:* Compute governance as the most feasible near-term guardrail, including chip tracking and why the industry pushes back hard* The anonymous-compute problem and why “confidential computing” worries safety researchers* China's narrow-AI approach and what it implies about the data center race* Recursive self-improvement, Jevons paradox, and whether you even need new data centers to reach the danger zone* The regulatory carve-out tech enjoys, and the NDA story coming out of LouisianaIf you want that version, upgrade your subscription and it lands in your inbox. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit theairisknetwork.substack.com/subscribe

Chris Paul and Burning Bright tackle David Ayer's 2014 World War II film Fury, starring Brad Pitt, Shia LaBeouf, Logan Lerman, Jon Bernthal, and Michael Pena. Burning Bright picked it as a Memorial Day rewatch and argues it is one of the most underrated war films of the modern era, deserving way more credit than Saving Private Ryan style lionization tends to allow. The guys dig into the five very different spiritual approaches of the tank crew, the dehumanization of war daddy, bible, gordo, kunas, and the painfully innocent Norman, and why the infamous early execution scene is not the glorification it gets accused of being. They unpack the central biblical passage from First John chapter two, do not love the world or anything in the world, as the real moral spine of the film and the heart of all discernment. From there they go big picture, hitting Jevons paradox and how better military tech just means more efficient mass sacrifice, why World War II had the cleanest cartoon story of any modern war, the controlled opposition Nazi op being run on MAGA right now, narrative shielding through Donald Trump's hyper Zionist posture, and the fiery tank as a birth canal delivering Norman into a second chance.

Jeff Schulze of ClearBridge Investments joins host John Przygocki to assess the health of the US economy. He points to a resilient Q1 GDP reading, strong jobs data and earnings growth in estimating a 30% recession probability. He cites the Jevons paradox to explain why he doesn't fear an AI "job apocalypse." And he remains bullish on US equities despite Middle East uncertainty and inflation worries. 

I detta avsnitt diskuterar vi Jevons paradox och dess relevans i dagens samhälle, särskilt inom AI och energiförbrukning. Vi utforskar hur effektivisering kan leda till ökad efterfrågan snarare än minskad, och hur detta påverkar både företag och individer.

Cuando cada mejora te cuesta más: la trampa de la eficiencia. Para escuchar sobre el tema de System Sprawl al que hago referencia en este episodio, haz clic en este enlace. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Será que a Inteligência Artificial vai destruir todos os empregos? Neste vídeo, desmistificamos o alarmismo digital com base na teoria econômica e na história. Entenda o Paradoxo de Jevons , como o Excel revolucionou carreiras e por que a IA é a ferramenta definitiva para aumentar sua produtividade.

In this episode, we break down the Jevons paradox and why AI may not simply replace work but reshape it. As the cost of intelligence falls, demand for analysis, coding, drafting, design, service, and research may rise, shifting how investors think about growth and productivity. To read this week's Sight|Lines, click here. The views expressed in this podcast may not necessarily reflect the views of Stifel Financial Corp. or its affiliates (collectively, Stifel). This communication is provided for information purposes only. Past performance does not guarantee future results. Investing involves risk, including the possible loss of principal. Asset allocation and diversification do not ensure a profit or protect against loss. © Stifel, Nicolaus & Company, Incorporated | Member SIPC & NYSE | www.stifel.com See omnystudio.com/listener for privacy information.

Is the AI job apocalypse a marketing strategy and not an economic forecast?It's nuanced, and today we tap into first wave stats for clarity.Correlation does Not imply Causation. The divergence is real, but timing also coincides with the Fed's aggressive rate hikes and sustained tightening. That said, AI is likely to reshape the labor market—not just in the number of jobs, but in the types of roles that exist - after giants clean up the BLOAT.People think the AI job crisis is about technology, but it's really about wealth inequality. - Scott GallowayPURE FACT. Every generation has its version of this story.The one where machines come for the jobs.Where the future arrives faster than people can adapt.Where the world you knew is about to end.This time around, the story has better PR and a much bigger budget — but underneath, it's the same script.I'm not saying this is nothing to worry about - we all feel the speed. In fact, the current evolution of AI is 300X faster than the Industrial Revolution. So….here's what I keep coming back to so we can understand the middle phase of the tech boom we are living through. The loudest voices warning us about the AI job apocalypse are also the people who profit most when we believe them.Anthropic's CEO says half of all entry-level white-collar jobs will be wiped out in five years.Elon says no job will be needed.Sam Altman wrote, before ChatGPT even launched, that the price of human labor was about to fall toward zero.Notice the pattern?The people predicting an extinction-level event are the same people building the asteroid and selling tickets to watch.We've Been Here BeforeThis panic isn't new.The Nobel-winning economist Robert Shiller has shown that fears about machines replacing humans helped fuel economic downturns in the 1800s.Science fiction later convinced people that automation caused the Great Depression.Computer panic deepened the recession of the early ‘80s.His point was simple.The damage doesn't usually come from the technology itself.It comes from the story we wrap around it.People feel pain from a normal recession, blame the machines, get more pessimistic, pull back further, and the story becomes the thing that creates the outcome it warned us about.That's exactly what I think is happening right now.AI is becoming a convenient cover story for layoffs that are really about over-hiring, inflation, and tariffs.Look at the numbers.U.S. tech employment grew from 8.7 million in 2020 to 9.6 million in 2023, then went flat.Not great.Not the apocalypse either.Meta's 10% cut is just bringing the company back to its 2021 size.Microsoft's 7% cut still leaves it 47% bigger than before the pandemic.Tesla announced it was hiring more, then laid off 10% of its workforce a month later — because of weak sales, not robots.This isn't the prelude to the end of work.It's a low-hire, low-fire labor market.That's it.Three Ways This Resurgence Plays OutScenario one: the bubble pops.The Mag 10 now make up 40% of the S&P.AI stocks have driven the majority of the market's returns since ChatGPT launched.If AI sneezes, the rest of the economy gets the flu.And when that recession comes, we'll blame AI for it — even though, historically, layoffs come in recessionary bursts, not the moment a new technology arrives.Scenario two: AI delivers, just slower than they say.When something gets dramatically cheaper, we don't use less of it.We find a million new uses for it.That's Jevons paradox.When the spreadsheet launched in 1979, everyone said accountants were finished.Instead, the profession quadrupled over the next 40 years.The same pattern shows up everywhere computers got adopted heavily — employment grew faster, not slower.Programmers today are coding less and thinking bigger.They've gone from construction workers to architects.The real question for any knowledge profession isn't “will AI replace this?”It's “is the human demand for analysis, judgment, and oversight elastic?”I think it is.And I think we're about to discover how much demand has been quietly waiting for the cost of execution to drop.Scenario three: the disruption outruns us.This is the scary one.AI hits every sector at once, no policy response, full collapse of the recovery cycle.But here's the part most people miss.Real societal upheaval almost never comes from unemployment.It comes from people who are working hard and still falling behind.From the loss of economic dignity.If that sounds familiar, trust your gut.We're already living in it.What's Really Going OnInside Silicon Valley, the mood is dark.People talk seriously about a “permanent underclass” and a “limited window” to build wealth before robots take over.I think this is a shared hallucination.The same people obsessed with AI's rapid capabilities are ignoring everything else about how economies, labor markets, and human demand actually work.And here's the tell.Only Americans earning over $200,000 a year see AI as a net positive.That's not a fact about AI.That's a fact about who has access to opportunity in this country.The AI jobs panic is just the newest scene in a much older story about wealth inequality.The real disruption isn't going to come from AI.It's going to come from the public finally noticing that the people warning us about the fire are the same ones selling the smoke detectors.The AI job apocalypse isn't an economic forecast.It's a marketing campaign.We're not watching the end of work.We're watching the monetization of fear.Life is so rich. Especially when you realize your inherent creative power and the evolution of our society has bright day's ahead of us. Not the doom - change, and fast? Yes, but the Universal Law of Order is always flowing from chaos to order. Your thoughts? Here's MY thoughts on AI brought to LIFE for REAL SOLUTIONS. How I view AI.....within the SPACE of the LIGHT Between Oracle Healing Journey.“Between stimulus and response there is a space. In that space is our power to choose our response. In our response lies our growth and our freedom.” - Viktor FranklThe Light Between is the conscious, sovereign light that we must maintain between our stimuli and responses. This is the light of discernment, wonder, and creativity - the light where humans truly thrive at our full capacity, rather than merely coping.I'm building a movement to advocate for preserving this vital light. Safeguarding this Light Between will enable the mindful and beneficial integration of AI into our lives.It is the wellspring of our agency, our ability to thoughtfully shape our responses to the world. Protecting and nourishing this Light is paramount as we navigate the increasing presence of artificial intelligence in our lives.In Closing…So if this lit up your heart and minds view of all the bright potential of transforming world of opportunity, then I'd love for you to experience the LIGHT BETWEEN ORACLE JOURNEY + INTUITIVE READINGS. Five Guides and a Five Layer Path…..to accelerate your intuition and problem solving. The Five-Layer Path integrates intention rituals, intuitive card draws, ancient wisdom teachings, somatic practices, and multidimensional exploration to support your journey. With your purchase, you gain access to:* Tailored Guidance: Personalized oracle readings to answer your questions.* Your Place of Power: Tools to discover and transform disempowering states.* Self Hypnosis: Techniques to rewire the subconscious, enhanced by the Neuro-Nature Self Hypnosis App.* Soul Prayer: Contemplative practices to deepen your connection to inner wisdom.* Poetic Insights: A space to save reflections for creative expression and meaning.* Five-Layer Path for Integration: A holistic approach combining intention, intuition, ancient teachings, somatic practices, and multidimensional awakening.Start for FREE and upgrade for deep awakenings and spiritual problem solving that resolves the daily self doubt and uncertainty. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit thelightbetween.substack.com/subscribe

We're announcing a16z crypto's Fund 5: $2.2B in committed capital to back the startups and founders who are building the next era of crypto. All four GPs sat down to talk through where crypto is right now, what's changed, and where it may be headed next. Chris Dixon, Ali Yahya, Guy Wuollet, and Eddy Lazzarin join Robert Hackett to cover... 00:00 Open  01:31 Why raise Crypto Fund 5 now  02:10 The GENIUS Act and what regulatory clarity unlocks for builders  04:32 Why stablecoins are crypto's WhatsApp moment  08:54 Why the next era of crypto founders will be pragmatic, not ideological  11:49 From cypherpunk revolution to crypto's "collared shirt era"  15:02 Programmable money meets AI  21:15 Onchain capital markets for compute, energy, and credit  25:57 Why finance is the foundation, not the ceiling  28:48 AI agents as first-class economic actors  38:19 Why privacy is the only moat  41:26 Jevons paradox and the future of blockspace demand  43:20 Jolt and the zero-knowledge breakthrough  58:15 Writing the next chapter of Read Write Own Resources:  Chris Dixon: https://x.com/cdixon Ali Yahya: https://x.com/alive_eth Eddy Lazzarin: https://x.com/eddylazzarin Guy Wuollet: https://x.com/guywuolletjr Robert Hackett: https://x.com/rhackett Follow a16z crypto: X: https://x.com/a16zcrypto  LinkedIn: https://www.linkedin.com/showcase/a16zcrypto/posts/  YouTube: https://www.youtube.com/@a16zcrypto Subscribe for more industry reports, trend updates, news analysis, builder guides, and other resources: https://a16zcrypto.substack.com/subscribe/   ***  As always, none of the following should be taken as investment, business, legal, or tax advice. Please see a16z.com/disclosures for more important information, including a link to a list of our investments. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The headlines say layoffs. The data says something completely different. National Association of Realtors is forecasting a 14% surge in home sales in 2026—and almost no one is connecting the dots. In this episode, we break down the single most misunderstood economic force shaping real estate right now: the Jevons Paradox. When efficiency increases, demand doesn't shrink—it explodes. And that shift is already showing up across housing, labor, and transaction volume. While many are predicting disruption, the reality is this: the agents who understand what's happening are positioning for one of the biggest opportunities in modern real estate. We cover: • Why the housing crash narrative doesn't match the data • What's really happening to white-collar jobs • Why high-trust, in-person roles are gaining power • How transaction volume is poised to surge • Why the top 20% of agents will dominate the next decade 88% of buyers and 91% of sellers still use an agent. That hasn't changed—and there's a reason. The market isn't collapsing. It's reorganizing. And the agents who adapt now will be the ones running the market by 2030.

Uma garrafa de champanhe vendida por 2 mil reais não custou nem perto disso para ser produzida. Marx e Adam Smith achavam que o valor vinha do trabalho. Estavam errados.No século XIX, um economista britânico chamado William Jevons mudou para sempre a forma como entendemos o valor das coisas — e, de quebra, como entendemos nossas próprias decisões.A ideia dele, a "utilidade marginal", é uma das mais poderosas da história da economia.Neste episódio da série A História do Dinheiro, a gente explica como Jevons e Alfred Marshall construíram o modelo que domina a economia até hoje: oferta, demanda, concorrência perfeita e o famoso "homem econômico racional". Uma teoria elegante, poderosa — e que alguns consideram perigosamente simplificada.

This week's Frankly is the second in a three-part series on the role oil plays in modern civilization, prompted by the recent flow disruptions and geopolitical conflict surrounding the Strait of Hormuz. This installment explores how modern society has been built on the assumption of cheap and abundant energy, and what happens when that assumption breaks down. Nate describes the ways our built systems, including food production, water treatment, manufacturing, and global trade, are calibrated to cheap energy inputs, and how processes that look economically efficient are often deeply inefficient in physical terms. He walks through the staggering degree to which the modern food system runs on fossil hydrocarbons, noting that roughly ten calories of fossil energy now go into every calorie of food on the plate, and that the Haber-Bosch process for synthetic fertilizer is what allows the planet to feed roughly half of its current population. Nate then traces the accelerating depletion of conventional oil fields and the turn towards shale, which behaves as a fundamentally different resource than the conventional wells it has been masking. He considers the alternatives often proposed as replacements, highlighting why energy quality matters as much as energy quantity, and why solar and wind are better described as 'rebuildable' rather than 'renewable.' The episode closes with Jevons paradox and the historical pattern that humans have never actually transitioned off an energy source, only ever adding new ones on top of the old.  Why can't we simply swap in alternative technologies for fossil hydrocarbons? What does the turn toward shale mean for systems built around cheap and stable energy inputs? And how might oil supply disruptions reshape the things you do, consume, and think about in your daily life? (Recorded March 31st, 2026)   Show Notes and More   Watch this video episode on YouTube   Want to learn the broad overview of The Great Simplification in 30 minutes? Watch our Animated Movie.   ---   Support The Institute for the Study of Energy and Our Future   Join our Substack newsletter   Join our Hylo channel and connect with other listeners

In this episode, Mauricio sits down with Andreas Steno Larsen to discuss his upbringing in a family influenced by macroeconomics and markets, shaped by his father's role as a chief economist. Andreas explains his liquidity-centric global macro framework, emphasizing how global liquidity—driven by central banks, treasuries, and commercial banks—serves as the primary force behind asset prices, operating in extended cycles likely peaking around Q3 or Q4 of 2026 amid midterm elections. He explores the interplay of politics, low interest rates, and abundant liquidity leading to potential inflation in 2027, while addressing Bitcoin's resilient yet dislocated price action amid geopolitical tensions and AI disruptions, viewing it as infrastructure rather than mere software. Andreas rebuts doomsday AI theses by invoking the Jevons paradox, predicting productivity gains and increased demand rather than mass unemployment, and highlights catalysts like stablecoin adoption for an agentic economy. The conversation also covers geopolitical hedges, with oil, natural gas, and gold outperforming silver, and touches on central banks' potential Bitcoin adoption.

My guests today are Animesh Koratana and Jamin Ball. Animesh is the founder and CEO of our portfolio company PlayerZero, which is building AI production engineers that operate complex enterprise software autonomously - resolving production incidents, catching defects before release, and building durable models of how systems actually behave.Jamin is a partner at Altimeter Capital and the writer behind Clouded Judgement, a Substack where he analyzes emerging trends in enterprise software. Jamin recently sparked a debate with an essay titled “Long Live Systems of Record.” His core argument is that while agents are changing how software is used and where value accrues, they still depend on ground truth. Systems of record won't disappear so much as get pushed down the stack as new agent-native interfaces emerge on top.My partner Jaya and I felt compelled to respond, with Animesh contributing insights based on what he's seeing on the ground as he builds PlayerZero. From our perspective, the missing layer is what happens inside the workflow itself: the judgment, exceptions, and reasoning that agents and humans apply as work gets done. We call these decision traces, and we believe the context graph they form over time will become the most valuable asset for companies building and deploying AI systems.It's a genuine debate - and one that's only going to matter more as agents move from demos to production.Looking forward to keeping the conversation going!Chapters00:00 Why Jamin's essay sparked debate00:35 Jamin's thesis: why agents need ground truth02:00 Animesh on why context graphs become the new source of leverage07:58 What current systems of record miss08:28 PlayerZero's perspective: context graphs in practice10:00 How context graphs could change org structures11:10 How to capture decision traces without forcing humans to log it?14:35 Which systems of record are most at risk17:04 Two workflows ripe for disruption: GTM and software development22:31 Animesh on where context graphs can add most value 28:50 Why context graphs create durability vs short-lived point solutions30:00 Will context graphs be verticalized or universal?34:00 Bear case: do context graphs fail like semantic layers?43:27 2026 predictions: big AI IPOs, world models, enterprise agent adoption45:00 Hot takes: point solutions die; AI job-loss discourse hits a fever pitch47:30 Jevons paradox: why agents create more work, not less

Mainstream economists and environmentalists share something in common. Both tend to tout efficiency -- think better light bulbs -- as the solution to climate change and all our other environmental problems. But the little-understood Jevons Paradox intervenes to overwhelm any progress that comes from improved efficiency. We skewer the efficiency gains of electric vehicles, lighting, and plenty of other sectors, and we cover ideas for avoiding the efficiency trap, including unveiling our new political platform, which is sure to take the country by storm.Sources/Links/Notes:Jason Barlow, "EVs Have Gotten Too Powerful," Wired, September 19, 2025.Russ Heaps, "Heaviest Electric Vehicles of 2025," Kelley Blue Book, April 7, 2025.Wikipedia article on energy efficiency in transport that includes a table that compares many modes of transportWilliam Stanley Jevons, The Coal Question: An Inquiry concerning the Progress of the Nation, and the Probable Exhaustion of our Coal-mines (London: Macmillan and Co., 1866). 2nd edition, revised.Tomas Kloucek, "Darkness as an Endangered Species: Why Light Pollution Matters," Earth Bridge, June 11, 2025.Scenic America, "Billboards in the Sky: The Hidden Culprit Behind Light Pollution," July 30, 2025.Prepared Mind, "Welcome to the Great Unraveling (Tapestry Cloud Style Reweaving Polycrisis into Polyopportunity," June 20, 2025.2,000 Watt SocietyCalculate your ecological footprint.Related episode(s) of Crazy Town:Episode 3, "One Point Twenty-One Jigawatts"Episode 19, "I Can't Drive...

With developments in generative AI progressing at such a furious pace, how can investors cut through the noise to identify the companies that will really matter? Baillie Gifford's Kyle McEnery shares his approach to meeting the entrepreneurs building the future – including his encounters with AppLovin, Anthropic, NVIDIA, Roblox and Reddit. Background:Kyle McEnery is an investment manager in our Long Term Global Growth Team (LTGG) and previously led Baillie Gifford's Artificial Intelligence Research Project. In this conversation, he tells host Leo Kelion why AI's ever-increasing capabilities make this one of the most exciting times to be a growth investor, and how leadership and culture act as signals in the noise to help identify companies with the greatest long-term growth potential. In addition to discussing which of the firms enabling and using today's language-based ‘frontier' AI models are leading the pack, he explains how efforts to understand and simulate real-world physics could unlock further progress. Portfolio companies discussed include:Anthropic – developer of the Claude AI models, which excel at coding, among other tasks.NVIDIA – the semiconductors firm whose accelerator chips are powering many of the advances in generative AI.Roblox – the video games platform whose Cube 3D technology allows creators to build objects and environments out of text-based descriptions.AppLovin – the ad-tech company whose AI-first strategy keeps the business lean and nimble.Reddit – the online discussion forum, whose authentic human conversations are gaining in value as a counterpoint to AI-generated output. Resources:AI and the future of everything: a long-term perspectiveAnthropic: why we are backing the AI frontrunnerLong Term Global Growth Strategy (institutional investors only)LTGG philosophy and process (institutional investors only)Private companies: from Anthropic to ZetwerkShort Briefings on Long Term Thinking hub Companies mentioned include:Alphabet/GoogleAmazonAnthropicAppLovinHorizon RoboticsNVIDIARedditRobloxTesla  Timecodes:00:00  Introduction – Dartmouth College's artificial intelligence workshop01:50   From quantum to AI via asset management02:50  Creating and then culling a machine-learning initiative08:05  ChatGPT's wake-up call10:35   Exceptional companies at the dawn of generative AI12:10   Anthropic's appeal to business customers14:55   A winner-takes-all opportunity?17:05   Dario Amodei and the scaling laws19:10   NVIDIA's foundational role in neural networks22:55  Making video game items in Roblox with AI25:00  AppLovin – a company built for the next era26:55  Reddit's valuable conversational communities29:35  World models, spatial AI and the physical world32:35  Staying open-minded and humble33:35  Book choice  Glossary of terms (in order of mention): Generative AI: AI systems that create new content such as text, images or code rather than just analysing data.Machine learning: AI techniques where systems learn patterns from data rather than being explicitly programmed.End-to-end, systematic (investment strategy): Fully automated, with decisions made by predefined rules rather than human judgement.Agentic AI: AI systems that can plan and carry out tasks autonomously rather than just responding to prompts.R&D: Research and development.GPT: OpenAI's models, which power its ChatGPT chatbot.Natural language processing: AI that enables computers to understand and generate human language.Token: A chunk of text, such as a word or part of a word, used by language models.Foundation models: Large AI models that can handle a wide variety of tasks.Know your customer (KYC): Financial checks used by banks to verify customers' identities and risks.Scaling laws: The idea that AI performance improves predictably as models, data and computing power increase.Compute: The processing power required to train and run AI models.Jevons' paradox: The counterintuitive idea that efficiency gains can increase, rather than reduce, overall usage.CUDA: NVIDIA's software platform for programming its chips for high-performance computing.Jensen: Jensen Huang, NVIDIA's co-founder and chief executive.Metaverse: Shared virtual worlds where people interact, create and play online.Large language models (LLMs): AI systems trained on vast amounts of text to understand and generate language.Multimodal models: AI systems that can process multiple types of data, such as text, images and video.World models: AI systems that learn how the physical world works in order to predict and simulate it.Embodied AI: AI that learns through physical interaction with the real world, such as robots or vehicles.Imitation learning: Training AI by having it copy actions demonstrated by humans.

Send us a textIn this episode, we sit down with designer Josh Jevons to discuss what it actually looks like to build a sustainable creative career without burning out, cold-pitching nonstop, or doing everything yourself.We also get into real-world outreach strategies, including walking trade shows, pitching without being salesy, and why face-to-face connections still matter. Along the way, we talk packaging, brand strategy, work-life balance, and designing systems that allow you to grow without burning out.If you're a designer who wants better clients, better collaboration, and a career that supports your life–not the other way around–this one's for you.All that and more when you listen to this episode:Making the shift from agency work to independent freelancingWhy complementary skill sets matter more than hiring “another you”Building a flexible, collaborative, creative teamThe role of brand strategy in effective (not just beautiful) designPricing, budgets, and scaling process without cutting value What designers don't learn in school, but learn fast on the jobOutreach strategies that actually feel humanHow to talk to potential clients without feeling awkward or salesyConnect with Joshua JevonsWebsite: https://www.jevonsdesign.com/ Instagram: https://www.instagram.com/joshuajevons_design/ Yeah Brother's Instagram: https://www.instagram.com/yeahbrother.co/ Mentioned in this episode:Rochester Institute of Technology https://www.rit.edu/ Yeah Brother https://yeahbrother.co/ Adobe MAX https://www.adobe.com/max.html AIGA https://www.aiga.org/ Connect with Katie & Ilana from Goodtype Goodtype Website Goodtype on Instagram Goodtype on Youtube Love The Typecast and free stuff? Leave a review, and send a screenshot of it to us on Slack. Each month we pick a random reviewer to win a Goodtype Goodie! Goodies include merch, courses and Kernference tickets! Leave us a review on Apple PodcastsSubscribe to the showTag us on Instagram @GoodtypeFollow us on Tiktok @lovegoodtypeLearn from Katie and IlanaGrab your tea, coffee, or drink of choice, kick back, and let's get down to business!

Why does adding more lanes to a highway actually make traffic jams get even worse? How did the machine meant to end slavery accidentally make it ten times more profitable? Why did the invention of the ATM lead to more bank tellers instead of replacing them? Is your "eco-friendly" car actually causing you to burn more fuel than your old one? Why does being the most efficient worker in the office always result in being given more work? ... we explain like I'm five Thank you to the r/explainlikeimfive community and in particular the following users whose questions and comments formed the basis of this discussion: environmental_bus507, sharp_simple_2764, zem, m1ss1ontomars2k4, nowhereman136 and mammoth-mud-9609 To the ELI5 community that has supported us so far, thanks for all your feedback and comments. Join us on Twitter: https://www.twitter.com/eli5ThePodcast/ or send us an e-mail: ELI5ThePodcast@gmail.com

Topics covered in this episode: Has the cost of building software just dropped 90%? More on Deprecation Warnings How FOSS Won and Why It Matters Should I be looking for a GitHub alternative? Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. HEADS UP: We are taking next week off, happy holiday everyone. Michael #1: Has the cost of building software just dropped 90%? by Martin Alderson Agentic coding tools are collapsing “implementation time,” so the cost curve of shipping software may be shifting sharply Recent programming advancements haven't been that great of a true benefit: Cloud, TDD, microservices, complex frontends, Kubernetes, etc. Agentic AI's big savings are not just code generation, but coordination overhead reduction (fewer handoffs, fewer meetings, fewer blocks). Thinking, product clarity, and domain decisions stay hard, while typing and scaffolding get cheap. Is it the end of software dev? Not really, see Jevons paradox: when production gets cheaper, total demand can rise rather than spending simply falling. (Historically: the efficiency of coal use led to the increased consumption of coal) Pushes back on “only good for greenfield” by arguing agents also help with legacy code comprehension and bug-fixing. I 100% agree. #Legacy code for the win. Brian #2: More on Deprecation Warnings How are people ignoring them? yep, it's right in the Python docs: -W ignore::DeprecationWarning Don't do that! Perhaps the docs should give the example of emitting them only once -W once::::DeprecationWarning See also -X dev mode , which sets -W default and some other runtime checks Don't use warn, use the @warnings.deprecated decorator instead Thanks John Hagen for pointing this out Emits a warning It's understood by type checkers, so editors visually warn you You can pass in your own custom UserWarning with category mypy also has a command line option and setting for this --enable-error-code deprecated or in [tool.mypy] enable_error_code = ["deprecated"] My recommendation Use @deprecated with your own custom warning and test with pytest -W error Michael #3: How FOSS Won and Why It Matters by Thomas Depierre Companies are not cheap, companies optimize cost control. They do this by making purchasing slow and painful. FOSS is/was a major unlock hack to skip procurement, legal, etc. Example is months to start using a paid “Add to calendar” widget! It “works both ways”: the same bypass lowers the barrier for maintainers too, no need for a legal entity, lawyers, liability insurance, or sales motion. Proposals that “fix FOSS” by reintroducing supply-chain style controls (he name-checks SBOMs and mandated processes) risk being rejected or gamed, because they restore the very friction FOSS sidesteps. Brian #4: Should I be looking for a GitHub alternative? Pricing changes for GitHub Actions The self-hosted runner pricing change caused a kerfuffle. It's has been postponed But… if you were to look around, maybe pay attention to These 4 GitHub alternatives are just as good—or better Codeburg, BitBucket, GitLab, Gitea And a new-ish entry, Tangled Extras Brian: End of year sale for The Complete pytest Course Use code XMAS2025 for 50% off before Dec 31 Writing work on Lean TDD book on hold for holidays Will pick up again in January Michael: PyCharm has better Ruff support now out of the box, via Daniel Molnar This is from the release notes of 2025.3: "PyCharm 2025.3 expands its LSP integration with support for Ruff, ty, Pyright, and Pyrefly.” If you check out the LSP section it will land you on this page and you can go to Ruff. The Ruff doc site was also updated. Previously it was only available external tools and a third party plugin, this feels like a big step. Fun quote I saw on ExTwitter: May your bug tracker be forever empty. Joke: Try/Catch/Stack Overflow Create a super annoying linkedin profile - From Tim Kellogg, submitted by archtoad

Dylan Field is co-founder and CEO of Figma, a beloved tool used by every modern product team. Founded in 2012, Figma has expanded from a single design tool to a comprehensive platform including FigJam, Slides, Dev Mode, and, most recently, Figma Make. After a $20 billion acquisition by Adobe fell through due to regulatory pushback, Dylan led the company to a successful IPO in 2025.What you'll learn:• How Dylan kept internal morale up after the Adobe acquisition fell through• His approach to maintaining pace and a sense of urgency 13 years in• How to systematically develop taste• How Figma decides which product lines to add• Why Dylan obsesses over “time to value”• How AI is making design more valuable—Brought to you by:Stripe—Helping companies of all sizes grow revenue—Transcript: ⁠https://www.lennysnewsletter.com/p/why-ai-makes-design-craft-and-quality-the-new-moat⁠—My biggest takeaways (for paid newsletter subscribers): ⁠https://www.lennysnewsletter.com/i/175569466/my-biggest-takeaways-from-this-conversation⁠—Where to find Dylan Field:• X: https://x.com/zoink• LinkedIn: https://www.linkedin.com/in/dylanfield/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Dylan Field(03:58) The Adobe deal fallout(05:50) Maintaining team morale post-deal(09:13) Strategies for sustaining high performance(13:37) Maintaining Figma's unique company culture(16:22) Dylan's leadership evolution(21:03) How to improve clarity as a leader(24:40) The controversy behind FigJam(31:06) Lessons from expanding Figma's core product line(39:32) Time-to-value(45:14) Introduction to Figma Make(48:26) AI app prototyping and the future of Figma Make(53:38) Lessons from Figma's AI product launch(57:47) The importance of craft(59:54) Developing good taste(01:05:35) The future of product development(01:10:32) Why AI won't steal your job(01:14:37) AI corner(01:18:32) Lightning round and final thoughts—Referenced:• Dylan Field live at Config: Intuition, simplicity, and the future of design: https://www.lennysnewsletter.com/p/dylan-field-live-at-config• Figma: https://www.figma.com/• Adobe: https://www.adobe.com/• Vision, conviction, and hype: How to build 0 to 1 inside a company | Mihika Kapoor (Product at Figma): https://www.lennysnewsletter.com/p/vision-conviction-hype-mihika-kapoor• Notion's lost years, its near collapse during Covid, staying small to move fast, the joy and suffering of building horizontal, more | Ivan Zhao (CEO and co-founder): https://www.lennysnewsletter.com/p/inside-notion-ivan-zhao• $46B of hard truths from Ben Horowitz: Why founders fail and why you need to run toward fear (a16z co-founder): https://www.lennysnewsletter.com/p/46b-of-hard-truths-from-ben-horowitz• FigJam: https://www.figma.com/figjam/• Cursor chat: https://help.figma.com/hc/en-us/articles/4403130802199-Use-cursor-chat-in-Figma-Design• Figma Slides: https://www.figma.com/slides/• Figma Sites: https://www.figma.com/sites/• Figma Buzz: https://www.figma.com/buzz/• Figma Draw: https://www.figma.com/draw/• Figma Design: https://www.figma.com/design/• Dev Mode: https://www.figma.com/dev-mode/• Figma Make: https://www.figma.com/make/• Zach Lloyd on X: https://x.com/zachlloydtweets• Warp: https://www.warp.dev/• Dylan's post on X about Figma on an AI product leaderboard: https://x.com/zoink/status/1968588014935801884• Kurt Cobain: https://en.wikipedia.org/wiki/Kurt_Cobain• Damien Correll on LinkedIn: https://www.linkedin.com/in/damiencorrell/• Marcin Wichary on LinkedIn: https://www.linkedin.com/in/mwichary/• Loredana Crisan on LinkedIn: https://www.linkedin.com/in/loredanacrisan/• Amber Bravo on LinkedIn: https://www.linkedin.com/in/amberbravo/• Figma's 2025 AI report: Perspectives from designers and developers: https://www.figma.com/blog/figma-2025-ai-report-perspectives/• Jevons paradox: https://en.wikipedia.org/wiki/Jevons_paradox#Energy_conservation_policy• AI prompt engineering in 2025: What works and what doesn't | Sander Schulhoff (Learn Prompting, HackAPrompt): https://www.lennysnewsletter.com/p/ai-prompt-engineering-in-2025-sander-schulhoff• Pantheon: https://www.imdb.com/title/tt11680642/• Retro: https://retro.app/• Thiel Fellowship: https://thielfellowship.org/—Recommended books:• Understanding Comics: The Invisible Art: https://www.amazon.com/Understanding-Comics-Invisible-Scott-McCloud/dp/006097625X• The Spy and the Traitor: The Greatest Espionage Story of the Cold War: https://www.amazon.com/Spy-Traitor-Greatest-Espionage-Story/dp/1101904216• Codex Seraphinianus: https://www.amazon.com/Codex-Seraphinianus-Anniversary-Luigi-Serafini/dp/0847871045Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed.My biggest takeaways from this conversation: To hear more, visit www.lennysnewsletter.com