Podcasts about Databricks

AGENDA: 00:00 – Google Loses Two AI Legends as Anthropic Wins the Talent War 14:45 – China's $50B DeepSeek Bet Changes the AI Power Balance 27:15 – AI's Memory Crisis Has Begun — Apple Warns of a '100-Year Flood' 30:00 – Wall Street Finally Asks the $725 Billion Question: Who Pays for AI? 41:00 – We Built an AI Finance VP... and It's Better Than Humans 46:30 – The Death of Moats? Why Founders Should Stop Talking About Defensibility 58:30 – Databricks, ServiceNow & the New AI Software Winners 01:07:00 – The Seat-Based SaaS Model Is Dying 01:12:00 – OpenAI's Custom Models Could Rewrite Enterprise Software 01:17:00 – OpenAI's Biggest Threat Isn't Anthropic Anymore  

SaaStr 863: The Enterprise AI Reality Check: From Dashboard Graveyards to 30-Day Migrations with Databricks' Co-Founder and SVP of Field Engineering Every Fortune 500 CEO has told their team that if they are not using AI, they are behind. So now every employee is token-maxing, spend is going up, and almost nobody can tell you what they are getting out of it. That is the reality Databricks sees from the front lines, serving more of the Fortune 500 than any other data and AI company on the planet. In this episode, Databricks Co-Founder and SVP of Field Engineering, Arsalan Tavakoli, sits down with SaaStr CEO and Founder, Jason Lemkin, to cut through the Twitter noise and talk about what enterprises are actually doing, what is still broken, and why the next 24 months will fundamentally change who wins and who loses in every major software category. You'll learn: Why the BI dashboard is dead and what replaces it - including how a car manufacturer just onboarded 70,000 non-technical users to query their own data in plain language with no analyst in the loop What "context" actually means for enterprise AI and why it is harder to solve than the data problem, using a framework that explains why agents fail even when the underlying data is clean Why no software monopoly survives the next 24 months, and how collapsing migration costs and low-end AI competitors are about to give every incumbent a pricing problem they cannot ignore How Databricks now completes enterprise-grade migrations in 30 days or less using LLMs to analyze, convert, and reconcile legacy systems that previously took years and cost more than the savings Why the murky middle is the most dangerous place to be in enterprise software right now, and how to know which side of the AI budget divide your product actually sits on

Patrick Moorhead and Daniel Newman return from a packed week of travel, covering HPE Discover 2026 and Pure Accelerate hosted by Everpure. They break down the government-forced shutdown of Anthropic's Mythos 5, the Apple-Intel foundry signal, the xAI-Cursor acquisition, and whether enterprise AI spending is actually contracting or simply concentrating. Episode 309 of The Six Five Pod covers the week's events, market moves, and the structural questions that follow. The handpicked topics for this week are: Anthropic Mythos 5 Forced Shutdown: The U.S. government issued a 90-minute compliance window and a worldwide kill switch on Anthropic's Mythos 5 and Claude Fable 5 models, forcing them offline across all geographies. Patrick and Daniel examine what this means beyond the immediate headlines: model access has entered the same geopolitical variable set as semiconductor export controls, and every enterprise CIO now has a new on-premises infrastructure argument on the table. The shutdown also surfaced an unexpected counterpoint from the cybersecurity community, which argued that Mythos 5, operating in a defensive capacity, was itself a protection layer against the use of adversarial models. Anthropic's decision to revoke access globally rather than implement citizenship-based authentication reflected both the 90-minute timeline and the practical impossibility of real-time identity verification at scale. (The Decode) HPE Discover 2026: The Agentic Infrastructure Story: Six Five Media spent multiple days at HPE Discover in Las Vegas, live-streaming coverage that drew more than 30,000 viewers across the event. Patrick and Daniel break down HPE's most complete agentic stack story to date, covering its networking-led compute approach, expanded NVIDIA and Broadcom silicon partnerships, autonomous networking through Marvis, and Juniper's integration into the AMD Helios interconnect as a path into hyperscale deals HPE previously lacked access to. (The Decode) Pure Accelerate 2026 and the Everpure Data Primacy Pitch: At Pure Accelerate, Everpure made its clearest case yet for a data intelligence layer designed to reduce token costs in enterprise AI workflows by operating across any storage vendor, any enterprise application, and without being hard-coded into the underlying array. Patrick and Daniel assess the value proposition and the proof burden separately: the concept is differentiated, particularly against Snowflake and Databricks, in that Everpure does not require its own storage hardware, but the company still needs to demonstrate ROI at scale and earn permission to compete in a market where data platform players have already established category positioning. (The Decode) Apple and Intel: The 18AP Signal and What It Sets Up for 14A: The announcement that Apple will manufacture chips with Intel sent Intel's stock up roughly 10%. The hosts parse what that deal likely looks like in practice: 18AP as a test drive for lower-risk logic-layer parts, with the more consequential milestone being a potential M7 SoC on Intel's 18AP process. The underlying driver is the TSMC capacity constraint, with Samsung logic deals picking up across the industry for the same reason. The real inflection point that Patrick notes is 14A: if Intel's backside power delivery process reaches risk production and scales to iPhone volume by 2028, the strategic weight of the Apple relationship will fully materialize. (The Decode) xAI Acquires Cursor for $60 Billion: Elon Musk's xAI acquired Cursor for $60 billion using equity inflated by SpaceX's IPO run-up, a move Patrick characterizes as buying market position in a category where xAI arrived late, having missed the window on thinking models and tool calling. Cursor brought $4 billion in ARR, 7 million monthly active users, and 50% Fortune 500 penetration into the deal. The open question remains whether xAI can convert that installed base into a durable enterprise AI stack or whether it remains primarily a GPU capacity provider selling at well above neo cloud market rates, with the Google-SpaceX deal drawing additional scrutiny as a related-party transaction preceding the IPO. (The Decode) The Flip: Is Enterprise AI Spending Contracting or Concentrating? Patrick takes the position that enterprise AI is entering a rationing phase, pointing to Accenture's bookings decline, Microsoft cutting developer access to cloud code, Uber blowing through cloud licenses, and the emergence of AI cost management as a venture category as converging proof points. Daniel argues the opposing case: dollar volume is growing even as project counts fall, hyperscaler CapEx guidance continues to accelerate across Microsoft, Google, Amazon, and Meta, and what reads as contraction is the market moving from subsidized pilots to production deployments tied to measurable P&L outcomes. Both agree the hard ROI era is arriving, and the real debate is whether that transition reads as discipline or deceleration on the way in. (The Flip) Fed Chair Kevin Warsh's First Meeting: New Fed Chair Kevin Warsh held rates steady in a unanimous decision but delivered remarks that the market viewed as hawkish, sending the S&P lower and two-year yields up 16 basis points before a partial recovery the following day. Patrick and Daniel note the structural signal beneath the reaction: Warsh is establishing the Fed's independence from political pressure while also signaling an intent to move away from survey-based data that arrives three to six months stale, in favor of more real-time economic inputs. Daniel draws a direct line to the kind of forward-looking data infrastructure that firms like Palantir, Databricks, and Snowflake are positioned to provide at the institutional level. (Bulls and Bears) Iran-Israel-U.S. Developments and Oil Below $80: A Memorandum of Understanding between Iran, Israel, and the U.S. briefly sent oil below $80 and signaled a potential opening of the Strait of Hormuz, though by the time of recording, reports were already emerging that the situation may be reversing. Patrick and Daniel keep it brief: the market has largely looked through the geopolitical noise, rallying through the period of conflict, and the oil price signal matters more to the macro environment than the diplomatic specifics. (Bulls and Bears) Accenture Earnings — The Services Layer Faces the Agentic Reckoning: Accenture beat on earnings but missed on revenue. The company reported a bookings decline of 2%, trimmed its 2026 revenue guide by 3-4%, and saw its worst single-day stock reaction in years. Patrick and Daniel use the result as a structural lens rather than a single-quarter data point: agentic AI and enterprise technology vendors are absorbing exactly the work that large professional services firms have historically owned, and the market is beginning to price that displacement ahead of the labor data catching up. Patrick flags this as the canary in the coal mine for the global services industry broadly. (Bulls and Bears) SpaceX IPO Volatility and Valuation Reality: The SpaceX IPO debuted at $135, surged above $210 on its first day of trading, and finished the week around $181. At its peak, the company briefly surpassed the market capitalizations of both Amazon and Microsoft before pulling back. Patrick and Daniel unpack the gap between the premium investors are assigning to Elon Musk and the company's underlying fundamentals. Despite generating roughly $50 billion in annual revenue, SpaceX remains unprofitable, and upcoming lock-up expirations could introduce meaningful volatility, particularly on the downside. Patrick points to long-term comparisons with Amazon and Tesla, while noting that many retail investors are still near break-even. The discussion explores how much of SpaceX's valuation is based on future potential versus current performance—and how much room remains for investor expectations to reset before fundamentals catch up. (Bulls and Bears) Watch the full video at sixfivemedia.com, and be sure to subscribe to our YouTube channel so you never miss an episode. The Decode  US Government Forces Anthropic to Disable Claude Fable 5 + Mythos 5 Worldwide — First-Ever Federal Shutdown of a Commercial Frontier AI Model; 90-Minute Compliance; EU + UK Sovereign-AI Talks Accelerate https://www.anthropic.com/news/fable-mythos-access  HPE Discover 2026 — Neri Bets the Company on Networking as the AI Control Plane; Juniper Integration Operational; Vultr Standardizes on HPE + NVIDIA https://www.crn.com/news/networking/2026/hpe-ceo-antonio-neri-five-boldest-statements-from-hpe-discover-2026 Everpure - Pure//Accelerate 2026 — First Conference Under New Name; "Data Primacy" Vision; Data Stream Built on NVIDIA AI Data Platform; Data Intelligence GA https://www.prnewswire.com/news-releases/everpure-unveils-data-primacy-architecture-for-the-ai-era-302803097.html  Apple's Chip Supply Chain Realigns in One Week — Intel 18A-P Enters Risk Production June 16; White House Confirms Apple-Intel Foundry Deal June 18 (INTC +9% to Record $135); Cook Says iPhone/Mac/iPad Price Hikes "Unavoidable" on RAM Crunch https://www.investing.com/analysis/appleintel-chip-manufacturing-deal-reshapes-foundry-race-200682398 SpaceX Buys Cursor for $60B All-Stock Four Days After IPO — Largest Developer-Tooling Acquisition Ever; Cursor at $4B ARR / 50%+ Fortune 500; Musk's xAI Loses the Code War, Buys the Winner https://www.cnbc.com/technology/ The Flip Are enterprise AI budgets contracting — is the procurement boom ending and the rationing phase beginning? FOR: Yes — Accenture cut its guide and bookings declined today; Uber blew through AI budget in months; Meta killed its leaderboard. https://www.businesswire.com/news/home/20260618029271/en/Accenture-Reports-Third-Quarter-Fiscal-2026-Results AGAINST: No — AI infrastructure capex is accelerating; enterprise demand is supply-constrained, not budget-constrained. https://ca.investing.com/news/stock-market-news/stifel-raises-jabil-stock-price-target-to-460-on-ai-growth-93CH-4698089 Bulls & Bears MACRO — FOMC Chair Kevin Warsh's Inaugural Meeting: Unanimous Hold at 3.5–3.75%, Statement Stripped of Cutting Bias; Dot Plot Flips to a 2026 HIKE at 3.8% Median; Warsh Refuses Own Dot; Worst Fed Day for a New Chair Since 1994 https://www.cnbc.com/2026/06/17/fed-meeting-today-live-updates.html  MACRO — Oil Cracks Below $80: Brent $78 (3-Month Low), WTI $75; US-Iran 14-Point MoU Signed at Versailles; Strait of Hormuz Reopening; IEA Projects 5.05 Mbpd Supply Glut in 2027 https://finance.yahoo.com/economy/policy/articles/oil-plunge-below-80-already-174253019.html Accenture (ACN) Q3 FY26 ACTUALS — EPS $3.80 Beats $3.70 (+9% YoY); Revenue $18.72B Slight Miss; Bookings DECLINE −2% to $19.3B; FY26 Guide Trimmed to 3–4% Local; Stock −13.3% Open; $9B Cybersecurity Acquisition Push https://www.businesswire.com/news/home/20260618029271/en/Accenture-Reports-Third-Quarter-Fiscal-2026-Results  SpaceX (SPCX) Post-IPO Trading Action — Melt-Up to $225.64 Tuesday Intraday Briefly Surpasses Amazon at $2.85T; Round-Trips to $192 by Wednesday Close on Fed Hawkish Pivot; Morningstar Fair Value $62 (~69% Implied Downside) https://www.cnbc.com/2026/06/15/evercore-isi-says-landmark-spacex-ipo-could-reignite-bull-market-send-sp-500-to-9000.html  

Bloomberg reported that Qualcomm is nearing a deal to acquire Modular, an AI software startup known for the Mojo programming language and an inference engine for cross hardware deployment. The reported move aligns with Qualcomm's push to expand on device AI on Snapdragon platforms, including PCs that meet Microsoft's Copilot Plus NPU requirements. Competitive pressure from Nvidia, Apple, Intel, and AMD is driving chipmakers to pair silicon with software to lower developer friction. Recent AI transactions such as Databricks' acquisition of MosaicML and investments in Anthropic show a broader consolidation of tools and compute. Regulators in the United States and Europe have increased scrutiny of AI deals, raising interoperability and licensing questions. Founders and IT buyers should evaluate portability, licensing, and performance baselines as potential ownership changes develop.Learn more on this news by visiting us at: https://greyjournal.net/news/ Hosted on Acast. See acast.com/privacy for more information.

El crecimiento exponencial de los pagos digitales y la banca en línea delinea una inclusión financiera a dos velocidades en México, donde el entorno urbano acelera su digitalización mientras el sector rural enfrenta el riesgo de quedarse rezagado en la economía del futuro.  En este episodio también encontrará:El impacto global de "Fortibleed", la masiva filtración de credenciales VPN que afectó a miles de firewalls de Fortinet,  La alianza entre Gobierno y universidades para la creación del Clúster Nacional de Supercómputo y  AIEl despliegue de la red gratuita con tecnología wifi 7 en las terminales del AICM.  Secciones: Historia Innovadora: Sistema de Tren Eléctrico Urbano.   Así lo dijo: José Antonio Peña Merino, titular de la Agencia de Transformación Digital y Telecomunicaciones (ATDT).   Breves de la semana: Las adquisiciones de SpaceX, Salesforce y Databricks.  Prompt que me cambió la vida: Walter Rosenkranz, Director General para México de Movizzon.   IT Masters Insight: Álvaro Arce, cofundador de Genuine Digital School.  #InclusionFinanciera #Ciberseguridad #Supercomputo #WiFi7 #TransformacionDigitalLe invitamos a seguir IT Masters Update, dejarnos sus comentarios aquí o a través de #ITMastersUpdate en las redes sociales y a visitar nuestro sitio oficial en IT Masters Mag.

Can a company reach 1 billion users before figuring out how to make money—and still dominate the future of AI?This week's AI news cycle delivered a fascinating mix of milestones, competitive shakeups, enterprise AI breakthroughs, security concerns, and agentic innovation. OpenAI crossed the historic 1-billion-user mark, Microsoft opened Copilot CoWork to the masses, SpaceX made a massive move with its $60 billion Cursor acquisition, and new open-source challengers emerged to challenge the industry's biggest players. For business leaders, the message is becoming increasingly clear: AI capabilities are no longer the bottleneck. Adoption, governance, employee enablement, and operational execution are now the real competitive advantages. Organizations that successfully train their teams and embed AI into daily workflows are already seeing dramatic productivity gains and measurable business outcomes. In this session, you'll discover: Why OpenAI's 1-billion-user milestone may be more complicated than the headlines suggest  How ChatGPT's market share slipped below 50% while Gemini and Claude continue gaining ground  OpenAI's new $150 million partner network and what it means for enterprise AI adoption  Why Microsoft Copilot CoWork could become a game changer for organizations already invested in Microsoft 365  The strategic implications of SpaceX acquiring Cursor for $60 billion  How new open-source coding models are challenging leading closed-source AI systems  Why AI governance and international cooperation became a major focus at the G7 Summit  The growing scrutiny facing OpenAI ahead of its anticipated IPO  New developments in agentic AI platforms from Databricks and Vercel  How leading companies are using AI agents to transform productivity and operations  What business leaders need to know about AI's growing impact on jobs, hiring, and workforce planning  Why employees who openly use AI may still face workplace stigma despite widespread adoptionAbout Leveraging AIThe Ultimate AI Course for Business People: https://multiplai.ai/ai-course/YouTube Full Episodes: https://www.youtube.com/@Multiplai_AI/Connect with Isar Meitis: https://www.linkedin.com/in/isarmeitis/ Join our Live Sessions, AI Hangouts and newsletter: https://services.multiplai.ai/eventsIf you've enjoyed or benefited from some of the insights of this episode, leave us a five-star review on your favorite podcast platform, and let us know what you learned, found helpful, or liked most about this show!

Apple WWDC, Meta AI & Data, Databricks, Themen der heutigen Sendung.

You Didn't Get SpaceX? Don't Worry, There Are Other Mega IPOs Coming You may feel like everyone got into SpaceX except you, and now you're wondering: Should I buy shares today? Is there something better coming next? The reality is that several other massive IPOs could be coming sooner than many investors realize. At the top of the list are OpenAI, with an estimated valuation of $852 billion, Anthropic, with an estimated valuation of $965 billion, Stripe, with an estimated valuation of $159 billion, and Databricks, with an estimated valuation of $134 billion. Before you get too excited about these potential offerings, or beat yourself up for missing SpaceX, consider what the historical data tells us. Research examining 1,724 U.S. IPOs between 2011 and 2024 found that the average IPO gained approximately 23% on its first day of trading. However, over the following three years, those same IPOs underperformed the broader market by an average of 25 percentage points. The study also found that since 1980, companies coming public with at least $100 million in annual sales and a price-to-sales ratio above 40 experienced an average decline of 45% from their first-day closing price. For current SpaceX shareholders, there could still be a near-term catalyst. Under Nasdaq's fast-entry rules, newly public companies can become eligible for inclusion in the Nasdaq-100 after just 15 trading days. However, both the S&P 500 and the Dow Jones indexes currently maintain a 12-month waiting period before new companies become eligible for inclusion. If your appetite for risk remains high, you'll likely have opportunities to speculate on OpenAI, Anthropic, Databricks, and other AI-related companies when they eventually go public. But an interesting question remains: When these AI giants hit the public markets, will investors who bought SpaceX at the IPO decide to sell some of their shares and rotate into the next hot AI opportunity? There are plenty of unanswered questions, which is exactly why we prefer not to invest based on hype, headlines, or fear of missing out. Instead, we focus on financial fundamentals, valuation, cash flow, and long-term business quality. Exciting stories can drive prices higher for a while, but over time, fundamentals tend to matter most.   What Can the Nifty Fifty and Tech Bubble Teach Us About Today's Market? Every market cycle has a story. In the early 1970s it was the "Nifty Fifty." In the late 1990s it was the internet and technology boom. Today it is artificial intelligence. The late 1990s we saw the technology boom where the internet was a revolutionary innovation that truly changed the world. Investors were correct about the technology but wrong about what they should pay for it. Companies with little revenue and no profits traded at astronomical valuations. The Nasdaq saw a five-fold increase between 1995 and early 2000. When the bubble burst, the fallout was severe. The Nasdaq ultimately lost almost 80% of its value. Hundreds of companies disappeared. Even industry leaders such as Cisco, Intel, and Microsoft experienced stock declines of 50% to 90%. Many investors assumed technology would continue growing forever and overlooked the simple fact that stock prices had already discounted years of future success. After peaking in March 2000, it took over 15 years for the Nasdaq to reclaim its previous high in April 2015. Often times I hear people say this time is different because unlike many internet companies in 2000, today's AI leaders are highly profitable businesses generating enormous cash flow. So, let's take a look at the Nifty Fifty as another, maybe more similar example. The Nifty Fifty era was built around the belief that a small group of dominant companies were so good that valuation no longer mattered. Investors piled into stocks such as Coca-Cola, IBM, Xerox, Polaroid, McDonald's, Sears and others. These companies were viewed as "one-decision stocks “buy them and never sell them. Investors would make excuses for the valuations because the businesses were strong. Through 1972, these firms averaged 22% annual earnings growth over the previous five-year period and had great profitability with an average return on equity over 22%. The problem was as enthusiasm grew, valuations expanded dramatically, with many trading at 40 to 60 times earnings despite an economy growing much slower. Then reality arrived. The 1973-74 bear market combined with inflation, rising interest rates, and an economic recession caused many of these stocks to fall 50% to 80%. The S&P 500 fell over 14% in 1973 and more than 26% in 1974. Most of the companies survived and remained successful businesses, but investors who paid excessive prices often waited a decade or longer to earn satisfactory returns. Today's AI boom has similarities to both periods. Like the Nifty Fifty, investors are concentrating heavily in a small number of dominant companies. Like the tech bubble, there is widespread excitement surrounding a transformational technology that is likely to reshape entire industries. However, history reminds us that even great companies can become poor investments when expectations become too optimistic. During every major market cycle, investors eventually discover the difference between a great business and a great stock. The key lesson from both the Nifty Fifty and the dot-com era is that transformative technologies often live up to their promise. What investors frequently get wrong is the price they are willing to pay for that future growth. AI may ultimately be every bit as revolutionary as investors believe. The bigger question is whether today's stock prices already reflect much of that future success. As we've learned from previous cycles, when expectations become too high, excellent results may not be enough to satisfy the market.   Private Credit Funds Are Facing High Redemption Requests Again This Quarter For the first quarter of 2026, redemption requests in several private credit funds exceeded the industry-standard 5% quarterly redemption cap. Second-quarter requests appear to be even higher. BlackRock's flagship private credit fund received redemption requests totaling 13.3% of fund assets, up from 9.3% in the first quarter. BlackRock has indicated it will continue to honor only up to 5% of redemption requests per quarter. Blackstone is facing a similar situation. Investors requested redemptions equal to roughly 10% of fund assets, and the firm also appears committed to maintaining its 5% quarterly redemption limit. Cliffwater may be facing the greatest pressure. Its $31 billion private credit fund received redemption requests totaling 17% of fund assets, far above the amount investors can currently withdraw and higher than the roughly 14% that was requested in Q1. Private credit funds have been dealing with a number of challenges, including rising loan losses, fraud concerns, and significant exposure to software companies. Many software businesses are facing pressure as investors question how artificial intelligence could impact their future growth and profitability. During BlackRock's last earnings call, CEO Larry Fink stated that institutional investors such as pension funds and insurance companies continue to allocate capital to private credit strategies. I don't want to call the man a liar, but it does seem strange that with all the problems that private credit is having I would think institutional funds would also be pulling back from investing. One would expect at least some institutional investors to become more cautious as risks increase. What concerns me most is the continued use of redemption gates. The longer funds limit withdrawals to 5% per quarter, the more investors may worry about liquidity. That concern can become self-reinforcing, leading more investors to submit redemption requests. If that happens, redemption demand could continue to rise in future quarters, creating additional pressure on the industry.   Investors Turn a Blind Eye to Fundamentals For many years, successful investing was built on analyzing company fundamentals. Today, however, there is a growing trend toward speculation and gambling. Many investors simply do not seem to care about valuation or earnings and instead believe stocks will continue to go "to the moon." Tesla is a good example. Three years ago, Wall Street analysts projected that Tesla would generate $163 billion in revenue by 2025. The actual figure came in far lower at $94.8 billion, more than 40% below expectations. Historically, missing growth expectations by such a wide margin would have been a major disappointment for investors. Yet Tesla shares have risen roughly 59% over the last three years despite falling well short of those revenue projections. There are other signs of speculation throughout the market. Thirteen years ago, there were only 39 private companies valued at more than $1 billion. Today, there are over 800. This trend highlights two important developments. First, private companies are staying private much longer, allowing early investors to capture a greater share of the value creation before public investors have an opportunity to participate. Second, investors are assigning much higher valuations to these businesses, many of which have little or no earnings and, in some cases, no positive cash flow at all. Markets can remain driven by optimism for long periods of time, but eventually fundamentals matter. The challenge for investors is determining when sentiment and speculation have pushed prices too far ahead of reality.   Headlines Say Crisis, Economic Data Says Otherwise The economy continues to show surprising resilience despite concerns surrounding higher energy prices and the conflict involving Iran. Many investors expected consumers to pull back as gasoline prices surged and headlines focused on geopolitical risks. Instead, economic data suggests the U.S. consumer remains in good shape. Retail sales in May rose 6.9% from the prior year, exceeding expectations and demonstrating that consumers are still willing to spend despite higher fuel costs. Even excluding gasoline stations, retail sales increased 5.4%, showing that spending strength was broad-based rather than simply a reflection of higher energy prices. Online sales, clothing purchases, restaurant spending, and other discretionary categories all contributed to the gains. Housing is also showing signs of stabilization. Pending home sales, which measure signed contracts on existing homes, rose 3.8% in May to the highest level in six months. The increase was well above economist expectations and marked a 4.8% improvement from a year ago. What makes these numbers particularly impressive is that they occurred while mortgage rates remained above 6% and energy prices were elevated because of Middle East tensions. Buyers and consumers appear to be adapting to a higher-rate environment rather than waiting indefinitely for lower borrowing costs. This does not mean there are no risks. Higher energy prices act like a tax on consumers, and housing affordability remains a challenge. However, the latest retail sales and housing data suggest the economy is far from rolling over. For investors, this is another reminder that economic fundamentals often matter more than headlines. While markets may focus on wars, oil prices, and geopolitical uncertainty, consumers are still spending, homes are still being purchased, and the economy continues to move forward.   The Most Important Part of the Fed Meeting Wasn't the Rate Decision The Federal Reserve's June meeting marked one of the biggest shifts in Fed communication and leadership in decades. As expected, the Fed left interest rates unchanged at 3.50%-3.75%, but the details beneath the surface were far more important. For the first time since 1951, a former Fed chair will remain on the Board after stepping down as chairman. Jerome Powell's decision to stay on as a governor creates an unusual dynamic as new Chairman Kevin Warsh begins reshaping the institution. Historically, outgoing Fed chairs have typically left the Board when their chairmanship ended. Warsh wasted little time signaling change. The Fed announced five new task forces that will review key aspects of monetary policy and Federal Reserve operations, including inflation frameworks, the Fed's balance sheet, its reliance on data sources, and productivity and jobs and the impact of artificial intelligence and other transformative technologies. The reviews are expected to produce recommendations later this year and could shape how the Fed operates for years to come. Perhaps the most noticeable change was the Fed statement itself. The policy statement was significantly shortened and went from above 300 words recorded in recent meetings to around 130 wors. It also removed much of the forward-looking language that investors had grown accustomed to under previous leadership. Language that suggested a bias toward future rate cuts was eliminated, reflecting a more data-dependent and less guidance-driven approach. The updated projections were also more hawkish than many expected. Nine of the 18 policymakers who submitted forecasts now expect at least one rate hike before year-end, while the other nine see rates remaining unchanged or moving lower. The result is a Fed that appears deeply divided on the path forward as inflation remains above target. Another major headline came from Warsh himself. Only 18 of the Fed's 19 policymakers submitted a forecast in the quarterly dot plot, with Warsh confirming that he did not provide one. As a long-time critic of forward guidance, Warsh appears to be signaling that the Fed may gradually move away from one of Wall Street's most closely watched communication tools. Half of the committee is worried inflation remains too high and believes rates may need to move higher. The other half sees little need for additional tightening. This sets the stage for Warsh's hope for a “family fight” as he believes more disagreement will lead to a better discussion so the Fed can finally deliver on price stability. While the rate decision itself was unanimous, the projections revealed a growing divide beneath the surface. The takeaway is clear: while rates didn't move, the Federal Reserve did. A shorter statement, less forward guidance, a chairman who won't publish his own rate forecast, five new policy task forces, and a committee split down the middle on the direction of rates all point to a Federal Reserve that looks very different than it did just a few months ago. The era of predictable Fed communication may be ending, and markets will have to adjust.   Financial Planning: Give More, Pay Less with Appreciated Stock One of the most tax-efficient ways to support a favorite charity or church is by donating appreciated stock instead of cash. When stock that has been held for more than one year is gifted directly to a qualified charity, the charity receives the full market value of the shares and can sell them without paying tax because it is a tax-exempt organization. The donor generally receives the same charitable income tax deduction they would have received had they donated cash, while also avoiding the realization of any capital gain. For example, if someone is considering donating either $50,000 of cash or $50,000 of appreciated stock, the charity receives the same economic benefit in either case, $50,000 that can be used to further its mission. Likewise, the donor generally receives the same $50,000 itemized charitable deduction. The difference is that if the stock was originally purchased for $20,000, donating the shares allows the donor to avoid recognizing the $30,000 capital gain. If the donor still wants to own the investment, they can use the cash that otherwise would have been donated to repurchase the shares, effectively increasing their cost basis from $20,000 to $50,000 and reducing future taxable gains.   Companies Discussed: Accenture plc (ACN)

First-year Foster MBA  (Class of '27) Tejash Bagri explains how he turned a stalled application into an interview by building a go-to-market prototype before reaching out — and what that says about standing out when every candidate has the same AI tools. A practical case study for anyone in a competitive recruiting process. Tejash was part of the core organizing team for Foster's inaugural AI Spark Day and leads the school's AI and Data Analytics Society. Before his MBA, he worked as chief of staff for a group of organizations in a startup environment, where he built AI-driven workflows for research, marketing, and hiring. He reached the final sixteen of Foster's Dempsey Startup Competition and is building a product focused on AI literacy in the classroom. What you'll learn How to use a prototype to get past a resume screen when everyone's resume looks optimized Why AI fluency only matters once it sits on top of real functional or industry expertise Why you should identify the two or three areas where you're genuinely above average — and build from there A staged model for AI maturity, and where most people stall Where to keep the human visibly in control during a live interview Resources mentioned Luma and Meetup (for finding local industry events) Lovable, Replit, Databricks, Claude Code (build/prototyping tools) Company 10-K filings as interview-prep research Ethan Mollick's "jagged edge" framing of AI capability

Hoy hablamos de la pausa de Estados Unidos antes de meter a DeepSeek, CXMT y otras empresas chinas en la lista negra; del nuevo agente empresarial de Databricks y su factura oculta; de la inversión de Nvidia en fotónica para centros de datos de IA; del frenazo de Anthropic a la facturación por tokens del Claude Agent SDK; y de una vía con ultravioleta para degradar PFAS.Puedes seguirnos en YouTube en https://youtube.com/olivernabani y puedes unirte al Discord Mashain en https://olivernabani.com/discord

Today’s headline news for Canadian IT solution providers, aside from HPE Discover: OpenAI launches official partner program and investment fund: OpenAI has officially introduced its new partner program alongside a $150 million investment fund aimed at expanding its enterprise ecosystem. The partner program is designed to help service providers, system integrators, and consultancies build, deploy, and manage custom AI solutions leveraging OpenAI’s models. According to the company, the initiative will provide partners with dedicated technical support, go-to-market resources, and early access to new product features. The accompanying $150 million fund will focus on investing in early-stage startups that are developing applications on top of the platform. GTIA names two Canadian Innovate Awards finalists: GTIA announced the six finalists for its inaugural Innovate Awards today, with two Canadian companies among them: GoWest.ai (Toronto, customer-facing AI category, for its CFP Service Desk and Field Technician Assistant) and Nucleus Networks (Vancouver, internal AI category). Winners receive a $20,000 USD cash prize, announced at ChannelCon 2026 on August 5 in San Diego. For more on the awards and what GTIA is looking for, check out our In The Channel conversation with Carolyn April from April 27. And to hear Jennifer Roy of Nucleus on how they’re thinking about AI, that episode is here. Cisco research highlights Canadian AI network risks: A new study from Cisco underscores an infrastructure cliff for Canadian organizations. The research found that 71 percent of Canadian respondents expect their current network capacity to hit its limits within 36 months due to AI workloads, while 91 percent cited budget constraints as the primary barrier to the required modernization. The data provides a critical conversation point for MSPs: any serious AI strategy must now be preceded by a serious network upgrade strategy. Okta integrates with Google Cloud AI: Okta has announced it is adding a dedicated identity security layer to Google Cloud AI, while its Auth0 platform is now directly integrating with Gemini for AI agent deployment. According to the company, these integrations are designed to bring enterprise-grade identity governance into the fast-moving AI ecosystem. For Canadian solution providers helping customers experiment with AI tools, this integration provides a mechanism to secure these environments and non-human identities without slowing down developer velocity. CrowdStrike open AI gateway: CrowdStrike has announced an open gateway ecosystem making Falcon AI’s control plane available across AI infrastructure, with native integrations spanning Databricks, Google Cloud, Azure API Management, and others. Simultaneously, Grant Thornton Advisors announced it is standardizing its managed security service operations on Falcon Complete, replacing legacy MDR with what CrowdStrike is positioning as agentic MDR. Acumatica channel appointment: Acumatica has appointed Roman Bukary as senior vice president of partner strategy and programs, effective immediately. Bukary brings prior experience in SaaS channel leadership and will be responsible for the strategy and ongoing evolution of Acumatica’s partner ecosystem. Coro Global Lean IT Day: Coro has launched Global Lean IT Day as an annual observance on June 16, recognizing IT professionals who manage enterprise-level cybersecurity complexity with limited team size and resources. The announcement is tied to ISC2 data showing 59 percent of organizations report critical or significant cybersecurity skills shortages, and Coro says it will release full survey findings ahead of Black Hat USA 2026. Leaseweb Canada leadership: Leaseweb Canada has named Estelle Azemard as its new chief executive officer. Azemard, who will be based in Montreal, brings more than 16 years of cloud industry experience and will lead the company’s continued growth and strategic expansion in Canada, where data sovereignty and hybrid cloud demand are both rising. Read Full Transcript Welcome to The Buzz from ChannelBuzz.ca, I’m Robert Dutt, today is Wednesday, June 17th, and here’s what’s happening in the channel today. We’ll have a full roundup of everything going down at HPE Discover this week in your feed in about an hour from now, but in the meantime, there’s plenty going on aside from all the news at Discover. Here’s what we think is worth keeping an eye on. OpenAI has officially introduced its new partner program alongside a $150 million investment fund aimed at expanding its enterprise ecosystem. The partner program is designed to help service providers, system integrators, and consultancies build, deploy, and manage custom AI solutions leveraging OpenAI’s models. According to the company, the initiative will provide partners with dedicated technical support, go-to-market resources, and early access to new product features. The accompanying $150 million fund will focus on investing in early-stage startups that are developing applications on top of the platform. As enterprise demand for generative AI moves from the proof-of-concept phase to production deployment, solution providers are increasingly being asked to navigate the integration complexities of building AI agents and customized models. The launch represents a significant maturation of OpenAI’s channel strategy, moving beyond direct enterprise sales to embrace the third-party ecosystem. Formalizing a channel structure gives Canadian IT providers a clearer framework to monetize AI advisory and implementation services, allowing them to capture more margin as customer demand scales up. The Global Technology Industry Association – GTIA – has announced the six finalists for its inaugural Innovate Awards, and Canadians have a strong showing. Two of the six companies are Canadian: GoWest.ai, the Toronto-based AI consultancy founded by West McDonald, is a finalist for its CFP Service Desk and Field Technician Assistant in the customer-facing category, while Nucleus Networks, the Vancouver-based MSP that now operates across five Canadian cities, is a finalist in the internal AI solutions category. The remaining four finalists – J&M Solutions, Sentry Technology Solutions, Framework IT, and Thrive – are all US-based. Winners in each category receive a twenty-thousand-dollar cash prize, announced live at the ChannelCon 2026 final keynote on August 5th in San Diego. One-third of the inaugural finalist class being Canadian is significant for a channel community that too often looks south of the border for proof points on what real AI deployment looks like. If you want more context on what GTIA was building toward with these awards, and what “deployed and in production” actually means in practice, we covered exactly that earlier this year on In The Channel with Carolyn April, GTIA’s vice president of research and market intelligence. And to hear how Nucleus thinks about AI inside their own MSP operations, Jennifer Roy joined us on the show in late April. Links to both episodes are in the show notes. A new study from Cisco underscores a looming infrastructure cliff for Canadian organizations chasing AI ambitions. The research found that 71 percent of Canadian respondents expect their current network capacity to hit its limits within 36 months due to the demands of AI workloads. Even more pressing, 91 percent cited budget constraints as the primary barrier to the required modernization. This data suggests an impending reckoning where artificial intelligence software aspirations simply outpace the physical network capabilities required to move massive data sets. The strain on existing infrastructure will likely manifest in latency issues and stalled proof-of-concept projects. This presents a critical conversation point for Canadian MSPs and infrastructure partners to bring to their customers: any serious AI strategy must now be preceded by a serious network upgrade strategy. It creates a massive opportunity for the channel to re-engage clients on foundational infrastructure, turning a software conversation into a broader hardware and services engagement. In Brief:  Okta announces dedicated identity secrity layer for Google Cloud CrowdStrike announced open AI gateway ecosystem. Acumatica appoints Roman Bukary as its new senior vice president of partner strategy and programs.  Coro launches the first-ever Global Lean IT Day to recognize IT professionals managing enterprise-level cybersecurity with limited resources.   Leaseweb Canada names Estelle Azemard as its new chief executive officer.  Full details and links in the show notes or the blog post. Remember that we’ll have all The Buzz from HPE Discover in your inbox in about an hour, and shortly after that, be sure to check out today’s In The Channel, where we’ll talk to HPE’s Ben Fallon about the company’s self-driving networks strategy.  And if you haven’t heard it yet, yesterday on The Buzz we took you through all the news from HPE Partner Growth Summit at Discover 2026, and then we followed that up Tuesday with HPE North American channel chief Jeremiah Jenson, going deep on The Power of One, the announcements from the show, and his big reqquest for solution providers. Be sure you check it out if you’re working with Hewlett-Packard Enterprise. That’s how we’re seeing the headlines today. I’m Robert Dutt for ChannelBuzz.ca, thanks for listening. Have a great day.

Anthropic has pulled access to its most advanced AI models following a US government order restricting foreign access, Microsoft says Singapore's workforce is among the world's most advanced AI users, Databricks has unveiled a new AI coworker designed to automate business tasks, Meta is bringing AI-powered search to Facebook, and South Korea's courts are grappling with lawyers citing AI-generated "ghost cases." Lynlee Foo unpacks the week's biggest AI stories and examines the growing tension between AI innovation, regulation, enterprise adoption and trust.See omnystudio.com/listener for privacy information.

Send us Fan MailThis week's enterprise software developments further demonstrate how rapidly vendors are embedding agentic AI, governed automation, and composable data architectures into core enterprise workflows. Rootstock Software strengthened its manufacturing and warehouse execution strategy through the acquisition of Ascent Solutions, while Anaplan expanded its AI planning portfolio with CoModeler, Custom Analyst, and Agent Studio to accelerate enterprise planning automation. In the go-to-market space, Apollo.io acquired Pocus to build a more agentic revenue operations stack, and Zapier partnered with Rillet to connect general ledger workflows with thousands of operational applications. Meanwhile, Databricks introduced Lakewatch as an open, agentic SIEM platform built on the lakehouse architecture, and Oracle launched Fusion Agentic Applications designed to place coordinated AI agents directly inside ERP workflows. Governance and enterprise trust also emerged as central themes, with Relyance AI unveiling Lyo to monitor how AI agents interact with enterprise data, while Salesforce introduced AI Foundry to operationalize research into enterprise-ready AI models. Finally, Spade raised significant funding to transform messy transaction strings into finance-grade AI data, reinforcing how semantic normalization and governed enterprise context are becoming foundational to the next generation of AI-native enterprise systems.In today's episode, we invited a panel of industry analysts for a live discussion on LinkedIn to analyze current enterprise software stories. We covered many grounds including the direction and roadmaps of each enterprise software vendors. Finally, we analyzed future trends and how they might shape the enterprise software industry.Video: https://www.youtube.com/watch?v=hekHpEgI0zMQuestions for Panelists?

Amy Reichanadter, Chief People Officer at Databricks, joined us on The Modern People Leader to discuss her upskilling journey throughout her career, creating consumer-grade employee experiences, and leading through rapid technological change. ----  Sponsor Links:

What if the data engineering skills you have today become obsolete in five years? In this episode, host Benjamin Wagner sits down with Pranav Motarwar, a data engineer who's witnessed the industry's transformation from traditional ETL to AI-powered pipelines, to explore how AI is fundamentally reshaping data engineering roles, why you need to master both "AI for data" and "data for AI" to stay relevant, and the emerging infrastructure required to handle multimodal data at scale. Whether you're a data engineer wondering about your career longevity or a builder curious about next-gen data stacks, this conversation unpacks the skills you'll need, the tools defining 2026, and why data engineers aren't disappearing - they're just evolving faster than ever.

Patrice Chaperon est Directeur Data, Infra et Plateforme chez Leboncoin, la marketplace que tout le monde connaît. Avant ça, Patrice a passé plus de dix ans chez Doctolib et Criteo, où il était notamment Head of Analytics.On aborde :

Tired vs. Wired: $4 Trillion in IPOs Coming, $100B in M&A, and Why the SaaSpocalypse is Over The public markets spent the last twelve months telling you B2B software was finished. Stocks down 60 to 70 percent. PE firms buying nobody. For the first time in history, software trading at a discount to the S&P 500. And at the exact same moment, Anthropic is projecting $50 billion in revenue, Cursor is getting acquired for $60 billion, and SpaceX, Anthropic, OpenAI, and Databricks are about to generate more market value than every other IPO since 2000 combined. Both things are true - and which one defines your next 18 months depends entirely on one question: are you tired or are you wired? In this episode, SaaStr CEO and Founder Jason Lemkin calls the market as he sees it, names who is winning and who is pretending, and makes the case that the Cambrian explosion in B2B is just getting started. You'll learn: Why the SaaSpocalypse was never about B2B dying - it was about pre-AI software dying - and what the Palantir, Twilio, and Atlassian re-acceleration stories actually tell you The four categories every B2B company falls into right now, and why category four founders need to stop pretending the recovery is coming on its own Why vibe coding your CRM is dead as a concept, and what "putting deals on your calendar" actually means as a product strategy Why your biggest near-term competitive edge might be two days of engineering work - making your API agent-friendly before your competitors do What SaaStr's own journey from 20 humans to 3 humans and 21 agents teaches you about consistency as the only real cheat code in agents This is for you if: Your growth has slowed and you are not sure whether it is a market problem or a you problem - this session will help you figure out which You are a founder or exec who has been in the "AI is coming" conversation for a year but has not yet seen it show up in your revenue You want the unfiltered version of where B2B is headed in the next 18 months, including the parts most people are too polite to say out loud

Host Gary J. Ross and Jeremiah Gordon, General Counsel of CapitalG, discuss growth equity investing and legal issues that arise at the later stages of the venture capital lifecycle. Jeremiah tells Gary that CapitalG, Alphabet's independent growth fund, operates differently from traditional corporate venture capital. Instead of investing to serve Google or Alphabet's strategic needs, the fund partners with companies such as Databricks, Stripe and CrowdStrike to drive financial returns and transform industries. Jeremiah discusses growth-stage diligence, the role of in-house counsel, and the new challenges created by the rapid growth in AI companies. The episode concludes with a look at exit transactions, particularly the increasing prevalence of private-to-private acquisitions.

Do you really need the SpaceX IPO in your portfolio?   With SpaceX expected to become the largest IPO in history, investors everywhere are asking the same questions: Am I missing out? Should I try to buy shares immediately? What happens if I don't own it?   Today, on Financial Detox, Jason and Alex separate hype from reality and explain what most investors misunderstand about IPO investing.   The truth is that the biggest risk may not be missing the IPO, it may be chasing it for the wrong reasons.   The conversation goes beyond SpaceX and explores why companies like OpenAI, Anthropic, Stripe, and Databricks are changing the way investors think about public and private markets.   What we cover today:

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

Database branching has, for a long time, been a troublesome piece in the modern developer workflow puzzle: a good idea in principle but in practice a slow and often expensive challenge. Get it right and you can accelerate productivity and remove bottlenecks; get it wrong and you're potentially creating all sorts of trouble for yourself, from privacy risks to additional complexity. However, things are changing. Thanks to the emergence of new platforms such as Neon, Supabase and Databricks Lakebase, branching a database can become as familiar to developers as managing code branches and multiple environments with, say, Git and Terraform.  On this episode of the Technology Podcast, host Ken Mugrage is joined by his Thoughtworks colleague Cam Casher and Databricks' Kevin Hartman to discuss the work Thoughtworks and Databricks have been doing together on Lakebase. They discuss the platform, their experience using it with Spotify's Backstage and the opportunities database branching can offer software engineering teams in an increasingly AI-assisted and agentic world. Read Cam and Kevin's recent series on using Databricks Lakebase with Backstage: https://www.thoughtworks.com/insights/blog/data-engineering/backstage-lakebase-databricks

AI is moving incredibly fast across the life sciences sector, but many organizations still struggle to build systems that deliver real operational value. In this episode, tech leader Rose LaRocca-Fisch explains why strong data governance and business alignment must come before chasing software trends. Please visit our website to get more information: https://swangroup.net/ Rose shares her practical leadership experience guiding pharmaceutical companies, CDMOs, and global biotech organizations through massive growth. The discussion breaks down why high-profile tech implementations collapse and outlines the exact steps needed to prepare your infrastructure for enterprise-grade tools.Key themes covered in this conversation:Why does advanced software amplify existing operational flaws instead of fixing themThe OASIS framework for sustainable and scalable IT transformationHow data readiness directly impacts clinical trial success and manufacturing yieldsReal-world applications using platforms like Databricks to speed up patient enrollmentThe shift toward AI-assisted work and managing data integrity risksLinks from this episode:Get to know more about Steven Swan: https://www.linkedin.com/in/swangroup Get to know more about Rose LaRocca-Fisch: https://www.linkedin.com/in/rose-larocca-fisch 

Send us Fan MailThis week's enterprise software announcements further confirm that the market is rapidly converging around agentic AI, semantic intelligence, and autonomous workflow orchestration. Blue Yonder introduced new AI agents and mobile applications aimed at strengthening supply chain execution and frontline operations, while Zendesk expanded its AI customer service strategy through the acquisition of Forethought. Actian launched an AI analyst designed to convert business glossaries into a live semantic layer, highlighting the growing importance of governed enterprise context for AI-native operations. Meanwhile, ActiveCampaign and Contentsquare announced new capabilities focused on customer engagement and digital experience intelligence. On the enterprise planning side, Anaplan expanded its AI planning portfolio with CoModeler, Custom Analyst, and Agent Studio, while Oracle continued embedding coordinated AI agents directly inside Fusion ERP workflows through its new Fusion Agentic Applications initiative. In parallel, Apollo.io acquired Pocus to strengthen its agentic go-to-market stack, Databricks introduced Lakewatch as an open agentic SIEM platform built on the lakehouse architecture, and Rootstock Software acquired Ascent Solutions to deepen its manufacturing and warehouse execution capabilities.In today's episode, we invited a panel of industry analysts for a live discussion on LinkedIn to analyze current enterprise software stories. We covered many grounds including the direction and roadmaps of each enterprise software vendors. Finally, we analyzed future trends and how they might shape the enterprise software industry.Video: https://www.youtube.com/watch?v=ksS15kccXPcQuestions for Panelists?

S&P futures are pointing to a higher open today. Asian markets closed higher on Tuesday, buoyed by a recovery in tech stocks and optimism surrounding China's export growth. Japan's Nikkei surged near +2%, with strong gains across semiconductor and heavy industry names. Samsung Electronics and SK Hynix drove the Kospi to close +8% higher today. European markets opened mixed.Companies Mentioned: Nuvalent, Databricks, Boeing

Take control of every AI agent, managed or not, running in your environment using Agent 365 and Microsoft Entra. Surface agents across AWS Bedrock, Google Vertex, Databricks, and Salesforce in one registry, assign Entra Agent IDs via CLI or SDK, and enforce least-privilege access through Conditional Access policies and Agent Blueprints, all without rebuilding your existing identity infrastructure. Lock down agent activity with sign-in logs that capture every authentication attempt, policy hit, and failure. Govern agents as first-class identities alongside your users, apps, and devices, and draw a hard line between managed and unmanaged AI in your organization. Vince Smith, Microsoft Entra Principal Product Manager, shares how to establish full visibility, access control, and lifecycle governance for AI agents using Microsoft Entra and Agent 365.  ► QUICK LINKS: 00:00 - Visibility and control with Agent 365 01:39 - Multi-platform registry sync 02:29 - Assign Agent ID 04:14 - Agent Blueprints 05:24 - Conditional Access for agents 06:24 - Sign-in logs audit trail 07:03 - Unblock the agent 07:54 - Wrap up ► Link References  Check out https://aka.ms/EntraforAgents ► Unfamiliar with Microsoft Mechanics? As Microsoft's official video series for IT, you can watch and share valuable content and demos of current and upcoming tech from the people who build it at Microsoft. • Subscribe to our YouTube: https://www.youtube.com/c/MicrosoftMechanicsSeries • Talk with other IT Pros, join us on the Microsoft Tech Community: https://techcommunity.microsoft.com/t5/microsoft-mechanics-blog/bg-p/MicrosoftMechanicsBlog • Watch or listen from anywhere, subscribe to our podcast: https://microsoftmechanics.libsyn.com/podcast ► Keep getting this insider knowledge, join us on social: • Follow us on Twitter: https://twitter.com/MSFTMechanics • Share knowledge on LinkedIn: https://www.linkedin.com/company/microsoft-mechanics/ • Enjoy us on Instagram: https://www.instagram.com/msftmechanics/ • Loosen up with us on TikTok: https://www.tiktok.com/@msftmechanics

What's up everyone, today we have the pleasure of sitting down with Lindsay Rothlisberger, Director of GTM Innovation at Zapier.(00:00) - Intro (01:23) - In This Episode (02:00) - Sponsor: Knak (03:08) - Sponsor: MoEngage (05:49) - How Zapier's RevOps Team Built Its AI Foundation (19:43) - Why Visibility Has to Come Before Governance in AI Adoption (24:58) - Sponsor: GrowthBench (25:58) - Sponsor: GrowthLoop (29:48) - How Zapier Fights Context Rot in Its AI Shared Brain (35:55) - How Zapier Governs Shared AI Skills from Review to Long-Term Ownership (39:27) - What Happens to RevOps When Everyone Around Them Can Build (45:05) - The Director of GTM Innovation Role and the Sharing Problem Nobody Has Solved (50:47) - What Keeps Lindsay Grounded in the Middle of All This Change (52:00) - Lindsay on Getting Buy-In and What She's Reading Summary: When a startup claimed in April 2026 that it invented the marketing engineer role and that RevOps professionals "just do tool integrations," Lindsay Rothlisberger had heart palpitations. Her team at Zapier had been building AI into GTM workflows for years before the announcement. In this episode, she walks through the 6-component AI governance model she published publicly: a golden path to Cursor, a structured shared brain in Google Drive, data policies built with the security team, a visibility layer powered by a custom Zapier agent, a context engineering strategy that fights context rot, and a red-yellow-green skills review gate. She also names the part of the model that's still broken, and it's more honest than most AI governance conversations allow. If your team is figuring out how to govern AI at scale without killing the momentum, this is the inside view from someone who's done it.About Lindsay RothlisbergerLindsay Rothlisberger is Director of GTM Innovation at Zapier, where she leads the company's AI-powered GTM transformation internally and works alongside customers navigating the same shift. She spent 4 years building Zapier's RevOps function from zero, scaling it into a cross-functional engine covering AI, systems, analytics, planning, and enablement, and growing ACV 10x in that time. Before moving into the innovation role, she led marketing operations and lifecycle programs at UNiDAYS across B2B and B2C markets. She writes on LinkedIn about what Zapier is actually shipping, what works, and what doesn't.How Zapier's RevOps Team Built Its AI FoundationMost RevOps teams doing serious AI work have been doing it longer than the current conversation suggests. The tools are newer and the terminology has changed, but building automated workflows that take unstructured data and produce structured, actionable outputs for salespeople and marketers? That's exactly what good RevOps teams were doing before anyone put a trending name on it.Lindsay's team at Zapier started experimenting with AI several years ago, when it was first becoming accessible. Zapier gave its RevOps team the tools to experiment early, and rather than waiting for a strategy to materialize, they picked a specific, annoying problem: sales handoffs. Salespeople were going into first calls without enough context about the lead. The team pulled all the relevant unstructured data, engagement records, support tickets, email threads, and used AI to generate clean, contextualized briefing materials. The result was a measurable lift in lead-to-opportunity conversion rates, and a pattern the team has used ever since: find something specific that's visibly broken, prove AI fixes it, then apply that logic somewhere else.That early foundation matters now because the landscape has shifted in a way that affects RevOps directly. Claude Code, Cursor, and similar tools have made it possible for people with no engineering background to build real things. Sales managers are writing AI skills that generate quarterly revenue strategies for reps. CS reps are building account monitoring tools. Lindsay's read on this is that the RevOps team's job isn't to slow that down. It's to give it a governance structure so it can scale without creating a mess, and to be the team that built the foundation those builds are operating on.At Zapier, that governance structure is anchored by an AI center of excellence led by a chief AI officer. The architecture is a hub-and-spoke model: the central team sets the frameworks, the guidelines, and the enablement resources; Lindsay serves as the spoke into go-to-market, with a partner who works alongside her. The 2 of them act as a feedback loop between what's happening on the ground in sales, marketing, and CS and what the central team needs to know. The center of excellence is small, just a handful of dedicated people, but it reaches into every function through the spoke structure.The first thing the center of excellence built for non-technical GTM employees was the golden path to Cursor. Cursor had already been adopted by Zapier's product and engineering teams. For GTM, the barrier wasn't the technology itself; it was the setup. Someone who's spent their career in spreadsheets and CRM doesn't automatically know how to configure a development environment. The golden path is step-by-step onboarding: from installation through a fully configured Cursor environment with the right MCP connections (Databricks, Zapier), the right rules, and the right context already loaded. The whole point is removing the 2-hour configuration overhead that otherwise kills adoption on day 1.That context is the shared brain: a structured Google Drive hierarchy with company-level, department-level, team-level, and working group-level folders. The first iteration meant converting existing documentation into markdown files and organizing them into a folder structure that agents could traverse predictably. Lindsay describes the experience of setting it up as oddly satisfying for an ops person who has spent years wishing the organization's institutional knowledge lived somewhere findable instead of scattered across a Google Drive that nobody had cleaned up in years. The goal of the initial build wasn't completeness. It was a working foundation that gave people enough context to get value from their agent setup without needing to build from scratch.The companies operating furthest ahead in AI adoption right now are the ones that treated the shared brain as infrastructure rather than a side project. Getting every GTM employee configured, context-loaded, and working from a shared knowledge base is unglamorous work, but it's the layer every other build depends on.Key takeaway: Before anyone on your GTM team builds anything with AI, create a centralized setup guide that handles environment configuration, approved MCP connections, and context loading from a structured knowledge base. Start with the tools your technical teams are already using and build a version of that golden path for non-technical employees. The 2-hour configuration friction that stops people on day 1 is a solvable problem, and solving it once prevents you from solving it individually for every person who tries to onboard.How Long It Actually Takes to Build a Shared BrainThe shared brain question that comes up in every version of this conversation is a practical one: how long does it actually take? Zapier's first rollout was a 4-week sprint, and the design of that sprint was deliberate about scope. Rather than trying to capture everything the organization knew, the team focused on what Lindsay calls the slow layer of context: things that don't change often. Company strategy documents. Ideal customer profile definitions. Lead and opportunity definitions. Basic playbooks. These documents already existed. The sprint was mostly ...

If you've been hearing the term "IPO" everywhere lately, there's a reason — and it's a big one. In this episode, Karl Eggerss breaks down everything you need to know about Initial Public Offerings in plain English, no finance degree required. With 152 IPOs already hitting the U.S. market in 2026 and companies worth a combined $3 trillion potentially going public this year — including SpaceX, OpenAI, Anthropic, Stripe, and Databricks — this may be the most important IPO conversation of the decade. In this episode, you'll learn: What an IPO actually is and how the process works Why investors get so excited when a big IPO drops The hidden risks most headlines skip over — including the lockup cliff Why the average company going public in 2025 was 12 years old — and what that means for growth potential What 2026's IPO boom could mean for the broader stock market How midterm year volatility could collide with a historic wave of new listings Whether you're a first-time investor or a seasoned one, this episode will help you cut through the hype and make smarter decisions when the next big name goes public.

Agentic AI is being misread as a series of separate battles - e.g. Snowflake vs. Databricks, copilots vs. agents, model makers vs. app vendors, etc. We think the real story is that the biggest opportunity in software is converging around who owns the new intelligent client and the AI back end that makes it useful. The new client is the agent-based system of engagement - Snowflake's CoWork & CoCo, Databricks Genie, Microsoft Copilot, Google Gemini Enterprise, ChatGPT/Codex, Claude/Cowork and others. But that client cannot deliver business outcomes without a new back end - what we call a System of Intelligence - that represents a model of the enterprise in terms of its business rules and tacit knowledge. You can't build one without the other. We frame this premise using Clay Christensen's integrated innovation and Jensen's extreme co-design as applied to enterprise software.That is why Snowflake is the focal point for this Breaking Analysis, but not the whole story. Snowflake is not just competing with Databricks anymore. It is now in the same strategic arena as Microsoft, Google, OpenAI, Anthropic, Salesforce, SAP, ServiceNow, Celonis and others - all trying to define where business users, builders and agents get work done, and where the enterprise context that powers that work gets built.

Follow me @samirkaji for my thoughts on the venture market, with a focus on the continued evolution of the VC landscape.Welcome back to another episode of Venture Unlocked, the podcast that takes you behind the scenes of the business of venture capital.In this episode, I'm joined by three deep tech investors and friends of the show, Nate Williams, Sunil Nagaraj, and Guy Perelmuter, for a roundtable on the state of deep tech and the changing venture landscape. We dig into what deep tech really means today, why it's suddenly attracting so much capital, and how economics, government tailwinds, and AI as a “killer app” have pulled these once niche technologies into the mainstream. We also explore the growing concentration of capital in a handful of hyperscale winners, the tension between consensus vs. non-consensus investing, and what all of this means for emerging managers, LPs, and founders operating at the zero-to-one stage.Thanks for listening to another episode of Venture Unlocked. I hope you enjoyed this conversation with Nate, Sunil, and Guy. If you'd like to get Venture Unlocked content straight to your inbox, go to ventureunlocked.substack.com and sign up, or head over to Apple Podcasts or Spotify and subscribe. Thanks again for listening.Nate Williams is the Founder and Managing Partner of DeepTech seed firm UNION (Union Labs, Union Peak VC funds) and formerly served as an Entrepreneur-in-Residence (EIR) at Kleiner Perkins focusing on vertical “Physical AI” opportunities across Climate/Resilience, PropTech, and Mobility. Nate has made over 40 early-stage investments, including Urban Sky, Butlr, Antimatter (acquired by Databricks), Proxy (acquired by Oura), Ruby Robotics (acquired by Intuitive Surgical) and Klue (acquired by Medtronic). Before transitioning to full-time VC, Nate built a track record as a hands-on operator with senior leadership roles across startup, growth, and turnaround stages, culminating in successful exits for 4Home (to Motorola, 2010), Motorola Mobility (to Google, 2012), Motorola Home (to ARRIS, 2013), and August Home (to Assa Abloy, 2017). Earlier in his career, Nate was an Analyst in the Digital Home Group at Intel Corp. Nate holds an MBA from UCLA Anderson School of Management and a Bachelor's degree in Comms from the University of Connecticut.Sunil Nagaraj is the Founder and Managing Partner of Ubiquity Ventures, a seed-stage venture firm investing in “software beyond the screen,” including robotics, AI, industrial automation, and frontier technologies. Prior to founding Ubiquity, Sunil spent over a decade at Bessemer Venture Partners, where he invested in companies across cloud computing, developer tools, and emerging technologies. He is widely recognized for his early conviction in deep tech and infrastructure-driven innovation before it became mainstream in venture capital.Guy Perelmuter is the Founder and Managing Partner of GRIDS Capital, a venture firm focused on deep tech, AI, and advanced industrial technologies. With a background spanning engineering, technology, and investing, Guy has built his career around backing highly technical founders tackling complex global problems. He is known for his insights into the convergence of AI, infrastructure, and industrial transformation, as well as his emphasis on technical depth and long-term value creation in venture investing.Timestamps:Topics in this conversation include:* Definition of Deep Tech by Technical Prowess and Advanced Engineering (2:51)* Hardcore Technology, Difficulty to Build, and Hardware Misconceptions (3:51)* Drivers Of Deep Tech Tailwinds: Maturing Technologies and Government Push (6:12)* Excess Investor Interest After SpaceX and Other Breakout Successes (9:18)* Historical Analogy to Electrification and AI as New Infrastructure Layer (14:43)* Need For Specialized Deep Tech Expertise and New VC Org Structures (19:36)* Schizophrenic Risk-on Behavior and King-making of Consensus Winners (22:08)* Why Normal M and A and IPO Outcomes Still Matter For Smaller Funds (26:53)* Fund Proliferation, New Managers, and What Will Prove Transient (28:49)* Access Capital, Hollywood-ization of Venture, and Coming Bust Risks (33:34)* Consensus Growth Obsession, 10x Expectations, and Metric Distortions (38:02)* How Seed Managers Adapt and Curate Downstream Capital for Portfolios (41:01)* Founder-led Investor Selection and Power Shifting To Specialist Seed GPs (44:53)* Myths About VC Impact, Trend Surfing, and Overstated GP Influence (48:18)* Final Thoughts and Takeaways (53:11)Follow me @SamirKaji and give me your insights and questions with the hashtag #ventureunlocked. If you'd like to be considered as a guest or have someone you'd like to hear from (GP or LP), drop me a direct message on X. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit ventureunlocked.substack.com

Por que reunir informações para decidir virou um processo tão complexo na sua empresa? Neste conteúdo, recebemos Guilherme Flora Garcia, Cientista de Dados na dti digital. Ele compartilha como uma empresa de saúde e nutrição animal superou a fragmentação de dados e conseguiu implementar soluções de inteligência artificial que geram alertas precoces, evitando perdas financeiras significativas. Ficou curioso? Então, dê o play!Assuntos abordados:Dados fragmentados;Data Lakehouse;Databricks;Data Mesh;Governança centralizada;Infraestrutura automatizada.Links importantes:NewsletterDúvidas? Nos mande pelo LinkedinContato:  osagilistas@dtidigital.com.brOs Agilistas é uma iniciativa da dti digital, uma empresa WPP #iaaplicada

Mega-IPOs are returning as potential market catalysts, with companies like Stripe, Databricks, and Shein drawing investor attention. Stripe's secondary transactions in 2023 implied a $50 billion valuation versus a 2021 peak near $95 billion, while Databricks last raised at around $43 billion. The structure of offerings, including free float, lockups, and index eligibility after S&P's 2023 rule change, will drive liquidity. Recent listings such as Arm at roughly $4.9 billion, plus Instacart and Klaviyo at about $660 million and $576 million, show investors prioritize profitability and unit economics. Higher interest rates and tighter SEC disclosure rules add pressure on pricing and readiness. Founders weighing IPOs, direct listings, or sales are focusing on audit quality, revenue metrics, and a clear use of proceeds to improve outcomes.Learn more on this news by visiting us at: https://greyjournal.net/news/ Hosted on Acast. See acast.com/privacy for more information.

SpaceX filed on May 20 for an initial public offering of stock that could value the Elon Musk-led company at more than $2 trillion. Other IPOs, Anthropic, OpenAI, DataBricks, and Stripe, could be coming soon. Barron's Senior Managing Editor Lauren R. Rublin talks with Associate Editor Al Root and Reporter Nate Wolf about the SpaceX deal, the IPO outlook, and what a flood of new issues could mean for the broader stock market. Learn more about your ad choices. Visit megaphone.fm/adchoices

Snowflake reported a first quarter fiscal 2027 earnings beat and announced a new $6 billion multi-year agreement with Amazon Web Services, according to Quartz. The quarter covered the period ending April 30, 2026. The deal points to deeper alignment with AWS through potential minimum spend, co-selling, engineering collaboration, and marketplace incentives. Customers should assess impacts on pricing, discounts, and lock-in while benchmarking against Microsoft Azure and Google Cloud. Competitive pressure from Databricks, Microsoft, and Google continues as AI workloads expand. Founders can use this development to revisit contracts, optimize workload placement, and plan budgets and runway for the next year.Learn more on this news by visiting us at: https://greyjournal.net/news/ Hosted on Acast. See acast.com/privacy for more information.

Immad Akhund, Co-Founder and CEO of Mercury, talks with TITV Host Akash Pasricha about his fintech's multi-product evolution and $200M raise. We also talk with The Information's Kevin McLaughlin about Microsoft's contentious decision to cut off a key Databricks integration, and we get into the realistic market math behind SpaceX's $1.75 trillion valuation with our editors Martin Peers and Meredith Mazzilli.Articles discussed on this episode: https://www.theinformation.com/newsletters/the-briefing/spacex-worth-700-billion-1-75-billionhttps://www.theinformation.com/articles/microsoft-opens-new-front-fight-data-ai-agentsSubscribe: YouTube: https://www.youtube.com/@theinformation The Information: https://www.theinformation.com/subscribe_hSign up for the AI Agenda newsletter: https://www.theinformation.com/features/ai-agendaTITV airs weekdays on YouTube, X and LinkedIn at 10AM PT / 1PM ET. Or check us out wherever you get your podcasts.Follow us:X: https://x.com/theinformationIG: https://www.instagram.com/theinformation/TikTok: https://www.tiktok.com/@titv.theinformationLinkedIn: https://www.linkedin.com/company/theinformation/Chapters:00:00 - Introduction01:13 - OpenAI Hits $5.7B Q1 Revenue02:39 - Microsoft Triggers New Corporate Data War12:43 - The Right Way to Value SpaceX25:46 - Mercury CEO on Fintech Growth and AI Tools

Hosted by David Cowen | Careers and the Business of Law Everyone's talking about Harvey, Legora, Spellbook, and Ivo. Nobody's talking about what they ride on top of. Tom Baldwin - founder and CEO of Entegrata, former CIO at Foley, Sheppard Mullin, Reed Smith, and Cadwalader - argues the real story is data infrastructure. Without a single source of truth, every AI tool in your firm is working from a partial picture. WHY THIS MATTERS? If your firm is buying AI tools without auditing the data underneath them, this is your warning shot. Tom's framing: toaster ovens need an electrical grid. KEY TAKEAWAYS AI tools work on narrow tasks, not whole-firm intelligence. 50 asset purchase agreements? Great. 200 million documents? No. Pulling documents out of your DMS strips away the metadata that makes them valuable - judge, opposing counsel, area of law, industry. That context is what AI actually needs. Business-of-law use cases (lateral prediction, cross-sell, client attrition, FP&A) are wide open. Practice of law got all the attention. A data lakehouse unifies data across 20-40 systems. Snowflake popularized it; Azure/Databricks/Fabric are the modern stacks.  Cost is roughly the same at 200 lawyers or 2,000 - six figures, ongoing. Compute and storage are cheap; talent is the investment. Firms move from "nice to have" to "must have" after a near-miss. Tom's example: a firm almost fired an associate because their FTE calc didn't account for maternity leave. The chief data officer is becoming a real C-suite role. Sidley's among the early movers. Watch the forward-deployed legal engineer trend. Harvey is hiring practitioners for these roles. PEOPLE MENTIONED David Cowen - Host Tom Baldwin - Entegrata founder & CEO Andrew Sieja- Founder of kCura/Relativity; Entegrata's first angel investor Renee Morris, Katrina Dittmer, Glenn LaForce - Data leaders Tom mentioned COMPANIES AND TOOLS MENTIONED Entegrata - Turnkey data lakehouse in Azure Snowflake, Azure, Databricks, Microsoft Fabric - Data platform stacks Harvey, Legora, Spellbook, Ivo - Practice-of-law AI tools Sidley Austin - Early adopter of the chief data officer role

In this episode, Ben and I walk through the exact conversations we're having with employees at companies preparing to go public. We talk about how to reduce taxes, create liquidity without selling everything, protect against downside risk, and avoid the mistakes we see people make after sudden wealth events.Whether you work at SpaceX, Anthropic, OpenAI, Databricks, Plaid, or another fast-growing private company, this is the framework I'd want you to hear before making any big decisions.-------✅ Financial planning for 30-50 year old entrepreneurs: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.allstreetwealth.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠✅ My personal blog & newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.thomaskopelman.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Disclaimer: None of this should be seen as financial advice. It is just for informational purposes.

Are AI agents silently draining your cloud data budget? With the rise of consumption-based pricing and autonomous AI queries, data teams are facing a perfect storm of skyrocketing costs and operational chaos. In this episode, I sit down with Sanjay Agrawal, CEO and Co-founder of Revefi, to discuss the intersection of data engineering, cloud warehouse optimization, and FinOps in the age of AI.We chat about how legacy on-prem habits are bankrupting modern data platforms, why query optimization is more about ROI than just speed, and how AI agents are changing the landscape of data consumption. Sanjay shares his deep expertise from building world-class databases at Microsoft and ThoughtSpot, revealing how to automate cost management and performance tuning for Snowflake, Databricks, and BigQuery.Key Topics:The evolution of cloud data warehouse pricing and why it breaks traditional budgets.How AI agents are causing massive, unpredictable spikes in compute spend.Real-world horror stories of ""lift and shift"" cloud migrations.Why database benchmarks focus on speed but ignore the actual ROI of data.The future of open table formats (Iceberg) and multi-engine routing.

Ben Miller is the co-founder and CEO of Fundrise, an alternative asset management platform that gives individual investors access to private real estate, private credit, and venture capital. In March 2026, he listed the Fundrise Innovation Fund on the NYSE under the ticker VCX, one of the first publicly traded venture capital funds. VCX gives retail investors direct exposure to private companies like Anthropic, OpenAI, Databricks, and SpaceX. The fund manages over $650 million and has over 100,000 individual investors. Ben is a returning Summation guest.In this episode of Summation, Ben and Auren discuss:Why VCX traded up 700% on day one while Bill Ackman's fund traded down the next weekWhy ETFs fall apart for private markets and closed-end funds are the right structureHow AI will reshape real estate by 2031 and which markets get hit hardestThe hidden truth that SoHo, Wynwood, and Miami Beach were all built by the same personYou can find Auren Hoffman on X at @auren and Ben Miller on X at @benmillerise

In this episode, Ben Lorica talks with Richard Garris and Barry Dauber from Databricks, about what enterprises are actually struggling with as they move AI from demo to deployment. Subscribe to the Gradient Flow Newsletter

Welcome to "To the Point Cybersecurity Podcast." This week, hosts Rachael Lyon and Jonathan Knepher dive deep into the evolving challenges of security operations with special guest Monzy Merza, CEO and co-founder of Crogl. With a career spanning government research, security innovation at Splunk, and go-to-market leadership at Databricks, Monzy Merza brings a unique perspective on why the security operations problem remains unsolved and why industry solutions often fall short for SOC teams. In this episode, you'll hear about the realities faced by security analysts: data sprawled across countless tools, rising alert volumes, and unrealistic expectations for operator expertise. Monzy Merza shares his eye-opening experience stepping away from executive roles to work directly on SOC teams, the complexity of today's threat landscape, and how AI is changing—sometimes complicating—the security equation. He also discusses pitfalls of centralizing data, the importance of auditable and transparent AI, and how Crogl strives to capture institutional knowledge and empower security teams. For links and resources discussed in this episode, please visit our show notes at https://www.forcepoint.com/govpodcast/e379

Mike & Tommy weigh in on whether Databricks and Microsoft Fabric are converging into direct competitors, exploring how Databricks' push into BI with Genie and AI capabilities is closing the gap on Power BI's presentation layer. They question whether "end-to-end" platforms are the future or just feature bloat, discuss where semantic models should live in a modern data stack, and help teams decide when to bet on one platform versus embracing the dual-stack reality.Get in touch:Send in your questions or topics you want us to discuss by tweeting to @PowerBITips with the hashtag #empMailbag or submit on the PowerBI.tips Podcast Page.Visit PowerBI.tips: https://powerbi.tips/Watch the episodes live every Tuesday and Thursday morning at 730am CST on YouTube: https://www.youtube.com/powerbitipsSubscribe on Spotify: https://open.spotify.com/show/230fp78XmHHRXTiYICRLVvSubscribe on Apple: https://podcasts.apple.com/us/podcast/explicit-measures-podcast/id1568944083‎Check Out Community Jam: https://jam.powerbi.tipsFollow Mike: https://www.linkedin.com/in/michaelcarlo/Follow Tommy: https://www.linkedin.com/in/tommypuglia/

Arcee is a tiny 26-person U.S. startup that built a high-performing, massive, open source LLM. And it's gaining popularity with OpenClaw users. Also, Matei Zaharia has won the top honor from the Association for Computing Machinery. Now he's working on AI for research and says AGI is simply misunderstood. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Send us Fan MailModern data platforms are evolving—and speed, scale, and efficiency are becoming non‑negotiable.In this episode of Exchanges with Hitachi Solutions, host Matt Volke sits down with Evan Sotos, Engineering Manager for the Empower Data Platform, fresh off his return from NVIDIA GTC. Together, they explore how GPU acceleration is moving beyond AI and machine learning—and into the core of data engineering.The conversation dives into what Evan heard from engineers, partners, and vendors at GTC, why NVIDIA is positioning itself as an algorithms company, and how technologies like NVIDIA RAPIDS are being used to dramatically accelerate analytics and data pipelines without rewriting existing code. What You'll Learn·       Why GPU acceleration is becoming a core capability for modern data platforms, not just AI workloads·       What NVIDIA RAPIDS is and how it enables existing CPU‑based workloads to run on GPUs·       How GPU acceleration can significantly reduce processing time and overall compute costs·       Why “zero code changes” is such a critical advantage for real‑world data teams·       Which types of data workloads benefit most from GPU‑accelerated pipelines From AI Buzz to Real‑World Data Engineering ImpactWhile NVIDIA GTC is often associated with AI and large language models, this conversation highlights a broader shift: GPUs are increasingly being applied to traditional data engineering and analytics workloads.Evan shares how NVIDIA RAPIDS acts as a mapping layer that allows existing Spark and Databricks workloads to take advantage of GPU compute. Rather than forcing teams to refactor complex, production‑grade code, GPU acceleration can be enabled through configuration—making it practical for teams to test, validate, and adopt without disruption.  The result? Faster pipelines, improved cost efficiency, and a shorter path from raw data to actionable insight—especially for large, time‑sensitive workloads. What This Means for Data TeamsFor organizations running large‑scale analytics, predictive models, or operational reporting, time truly is money. Evan explains how accelerating data pipelines can directly impact downstream use cases—from predictive maintenance to real‑time decision‑making—by reducing the lag between data ingestion and insight.Most importantly, this episode emphasizes practicality: GPU acceleration isn't about chasing hype. It's about giving data teams another tool they can turn on, test, and adopt when it makes sense—without introducing risk, rework, or operational complexity. global.hitachi-solutions.com

"Companies designing for agents, not humans, are going to get a lot of lift."ClickHouse started as an internal tool at Yandex. Today it's the database Anthropic, OpenAI, Meta and Tesla all run on.In this episode, CEO Aaron Katz joins Lukas Biewald to talk about how he turned an open source project into a $15B company, why he acquired LangFuse knowing it could cost him customers, and what he's actually building for the agent era.Snowflake, Datadog and Databricks all come up. He doesn't shy away.Connect with us here:Aaron Katz: https://www.linkedin.com/in/aaron-katz-5762094ClickHouse: https://www.linkedin.com/company/clickhouseinc/Lukas Biewald: https://www.linkedin.com/in/lbiewald/Weights and Biases: https://www.linkedin.com/company/wandb/00:00 Trailer00:57 The Origin Story: From Yandex to ClickHouse Inc.04:43 Building ClickHouse Cloud & Raising $300M10:36 Growing Up Around Xerox PARC12:51 Salesforce, Mark Benioff & the Dot-Com Bust15:32 Cloud Skeptics vs. AI Skeptics | History Repeating18:05 Building a Modern Go-To-Market Playbook21:57 The SaaS Crash, Agents & the Future of Infrastructure27:09 The Datadog Love-Hate Story35:21 Hardest Moments: Russia, SVB & Sleepless Nights43:16 Outro

Databricks Roundtable episode: Operationalizing AI Agents: From Experimentation to Production. Join the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterMLOps GPU Guide: https://go.mlops.community/gpuguideBig shout-out to Databricks for the collaboration!// AbstractThis panel discusses the real-world challenges of deploying AI agents at scale. The conversation explores technical and operational barriers that slow production adoption, including reliability, cost, governance, and security.The panelists also examine how LLMOps, AIOps, and AgentOps differ from traditional MLOps, and why new approaches are required for generative and agent-based systems. Finally, experts define success criteria for GenAI frameworks, with a focus on robust evaluation, observability, and continuous monitoring across development and staging environments.// BioSamraj MoorjaniSamraj is a software engineer working on the Agent Quality team. Previously, Samraj worked at Meta on ads/product classification research and AppLovin on MLOps. Samraj graduated with a BS+MS in Computer Science from UIUC, advised by Professor Hari Sundaram, where he worked on controllable natural language generation to produce appealing, interpretable science to combat the spread of misinformation. He also worked with Professor Wen-mei Hwu on accelerating LLM inference through extreme sparsification.Apurva MisraApurva is an AI Consultant at Sentick, focusing on assisting startups with their AI strategy and building solutions. She leverages her extensive experience in machine learning and a Master's degree from the University of Waterloo, where her research bridged driving and machine learning, to offer valuable insights. Apurva's keen interest in the startup world fuels her passion for helping emerging companies incorporate AI effectively. In her free time, she is learning Spanish, and she also enjoys exploring hidden gem eateries, always eager to hear about new favourite spots!Ben EpsteinBen was the machine learning lead for Splice Machine, leading the development of their MLOps platform and Feature Store. He is now the Co-founder and CTO at GrottoAI, focused on supercharging multifamily teams and reducing vacancy loss with AI-powered guidance for leasing and renewals. Ben also works as an adjunct professor at Washington University in St. Louis, teaching concepts in cloud computing and big data analytics.Hosted by Adam Becker// Related LinksWebsite: https://www.databricks.com/https://mlflow.org/~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Samraj on LinkedIn: /samrajmoorjani/Connect with Apurva on LinkedIn: /apurva-misra/Connect with Ben on LinkedIn: /ben-epstein/Connect with Adam on LinkedIn: /adamissimo/Timestamps:[00:00] Introduction[02:30] AI Agents in Operations[04:36] AI Strategy Consulting[05:30] Agent Quality Focus[06:17] AI Agent Expectations[11:44] AI Use Cases Evolution[15:25] Agent Expectations Adjustment[17:41] Agent Quality Monitoring[23:22] Trust in GenAI Systems[33:33] Data Prep vs Product Thinking[40:27] Quality Systems Distinction[44:54] Q & A[1:00:57] Wrap up

Pradeep Mannakkara (CIO) and Ben Mayrides (CISO) of Cvent explain how they govern AI agents at scale across their 5,500-person organization, which now has over 6,000 agents in production. In this fireside chat recorded at a Glean event in NYC, they walk through the AWARE framework developed by Glean's Work AI Institute with Databricks and Palo Alto Networks, and describe the practical tradeoffs of moving fast while managing risk. The conversation covers agent identity, observability, cultural adoption, CIO/CISO dynamics, and what enterprise-grade AI governance looks like in practice.You'll discover:✅ Why traditional IAM and observability controls fail in agentic architectures where agents reason, delegate, and act autonomously✅ How Cvent deliberately encouraged 6,000 agent creations to build AI fluency before layering in moderation and metrics✅ The AWARE framework's five pillars: identity, context, guardrails, risk scoring, and ecosystem observability✅ Why "risk is too high" is never the final answer, only "risk is too high for now"✅ How Cvent filters AI demand through ROI gates before projects reach security review✅ Why replacing gut-feel security objections with shared criteria moves the CISO from gatekeeper to business partner✅ The sandbox-first approach that separates experimentation from production deployment✅ Why SOC 2 control criteria for AI agents are likely within 18 to 24 months⏱️ TIMESTAMPS0:00 Introduction and the AWARE framework0:34 Core challenges of agent governance2:43 What agents do for us and to us4:36 Applying the AWARE framework in practice7:09 Choosing platforms with built-in controls9:25 Making governance a cultural shift11:51 Earning trust through deliberate risk decisions13:49 Replacing gut reactions with shared criteria15:20 Managing the CIO/CISO tension18:54 Shared language for hard tradeoffs22:01 Go/no-go decisions are never one and done24:48 Advice for putting AWARE into practice26:38 Scaling to 6,000 agents

Carlyle's Jeff Currie says even if the Iran war stops now, surging oil prices will impact the economy for months. Databricks CEO Ali Ghodsi on his new cybersecurity product and AI disruption within the space. Plus, Anthropic and the Department of Defense head to court. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The SaaS multiples run was long, but it had to come to an end. Or Had it? Navigation: Intro Setting The Scene The Roots — This Didn’t Happen Overnight The Structural Thesis — Why This Isn’t Just A Sell-Off The Private Market Fallout The Bull Case — Is The Market Wrong? Separating The Wheat From The Chaff — Who Survives? Wrap-Up & Key Takeaways Conclusion Our co-hosts: Bertrand Schmitt, Entrepreneur in Residence at Red River West, co-founder of App Annie / Data.ai, business angel, advisor to startups and VC funds, @bschmitt Nuno Goncalves Pedro, Investor, Managing Partner, Founder at Chamaeleon, @ngpedro Our show: Tech DECIPHERED brings you the Entrepreneur and Investor views on Big Tech, VC and Start-up news, opinion pieces and research. We decipher their meaning, and add inside knowledge and context. Being nerds, we also discuss the latest gadgets and pop culture news Subscribe To Our Podcast Introduction Nuno Goncalves PedroWelcome to Episode 75 of Tech DECIPHERED, the SaaS Apocalypse: Why AI Breaks or has Broken or Broke the Software Business Model. In today’s episode, we will talk about what’s been going on in SaaS. SaaS, also known as Software as a Service, as a sector, has just had its worst month since the 2008 financial crisis. Give or take, around 1 trillion in software stock market cap has evaporated this year, and it was triggered in many ways by the rise of a lot of the things we’re seeing, in particular, agentic AI. We’ll talk about it later.One of the key triggers seems to have been the launch of Claude or Claude Cowork. There’s a lot of fears that the model that is taken as SaaS to be the darling of investors, both VCs, private equity funds, and also retail investors, has now evaporated. The sweetheart industry no longer works. Bertrand, what happened to SaaS? What’s happening? Bertrand SchmittSetting The SceneWe are in the middle of what some are calling the SaaSpocalypse. I think that was a coined term early this year. It’s pretty bad. We are recording that March 13th. Definitely January, February of this year, 2026, were really terrible. There is no question about it. Strangely enough, since the start of the war with Iran, there has been a small rebound, so we will see how it goes. But also to give some context, we are still not worse than what happened in 2022. We are still in a better place so far. I would say the difference, there is clearly a focus in terms of SaaS versus tech in general for that down term. Nuno Goncalves PedroWe’ve seen obviously a lot of things happening, right? A lot of announcements. The iShares expanded Tech-Software ETF down 25% year-to-date. Everyone seems to be running into panic, JPMorgan, Goldman Sachs. Basically, Jefferies, I think, as you said, originally termed this the SaaSpocalypse. But definitely, it seems like everyone’s trying to sell stock and saying, “Hey, SaaS is going to die.” We’ve seen a lot of interesting elements to this, we’ll talk about it later, around AI eats software. Software eats the world. AI now eats software. I guess AI eats the world.But the reality is, we’ll discuss it later in the episode, it might be just a lot of stuff that’s reacting to what’s actually happening in the market, that there was a couple of misses in terms of numbers, that the growth of some of the key SaaS players that are driving a lot of the public stock wasn’t that great recently. That adding to some launches like we mentioned, the Claude Cowork launch, et cetera, has led people to say, “Hey, maybe some entire spaces of SaaS don’t make much sense going forward.” Bertrand SchmittActually, I don’t know if you noticed, but I think it was yesterday, it was announced that the CEO of Adobe just resigned. I was shocked how bad they managed the transition to AI. I guess it’s one of the first victims of what has been happening. From my perspective, and I will go deeper, but there is a bit of an overreaction. Claude is amazing as a tool, but the launch of Claude Cowork, a few plugins decimating the market, I think that’s an overreaction in the sense that many of these SaaS companies will be able to actually benefit from AI as well. Or some of the new AI tools really, really depend on the existence of an underlying SaaS layer that’s controlling some processes, some data. So I think we have to be careful about the extremes.At the same time, what is true, the growth rate has been going down for SaaS. If you look in the 2021 to these days, we move maybe from 30-11%, 12% average growth rate. It’s a dramatic difference in growth rate, and you cannot keep the same valuation when your growth rate has been divided by three. I mean, that’s just not possible.I think that there might be some overreaction about what company like Claude can truly achieve. At the same time, the reality is there that while SaaS companies are usually relatively strong companies, the growth rate has diminished, and as a result, so should the valuation.The Roots — This Didn’t Happen OvernightBut maybe we can move deeper about what happened the past 2 years about SaaS. Nuno Goncalves PedroIndeed. Some things going back as much as 2024 when Salesforce had its worst trading day. By then, in 2 decades, and went down by 20% on a rare revenue miss. So some early people, a lot of analysts, see this as an early warning of what was to come. Late last year, a huge shift as the different labs of a bunch of different players started launching agentic solutions, which in some ways started eating into a lot of the functionality, not just of vertical SaaS, but also of horizontal SaaS. As a distinction for some of our listeners who are not familiar with that distinction, vertical SaaS is normally SaaS that’s very specific to a specific industry or sub-industry or specific arena, whereas horizontal SaaS is normally SaaS that doesn’t require much adaptation to work across industries. A good example of that might be HR management systems.But basically, because of some of the early developments in those labs and a lot of the solutions that we started seeing around agentic tools, the market started being less positive on SaaS players and trying to readjust it. Those are the historic moments, 2024, 2025. Then all of a sudden, we see the growth rates of SaaS companies coming down, because obviously this doesn’t only have manifestations in the public equity markets. This has manifestations in clients.People, at this moment in time, we’ll talk about it later, are reconsidering their options. They’re like, “Why should I have a SaaS tool? Should I buy it from another player? Should I have a more holistic solution or an integration with Claude, for example? Should I develop in-house?” We’ll talk at length on what’s in customers’ minds, but customers started changing their views and stop buying some solutions that were out there from the large players that are public equities today. Bertrand SchmittYeah, it’s clear that there has been also just overall industry-wide tendency to try to cut on the SaaS subscriptions. Maybe there was too much interest buying too many software solutions, not rationalizing enough, not being careful about the spend. It makes sense that this has hurt overall SaaS growth rate. At the same time, there has been a transfer from IT spending from SaaS tools to AI, so we create a smaller budget for buying SaaS software.But going back, when you look at the change in revenue multiples, it’s crazy. In 2021, we were close to 20X EV, enterprise value to revenues. Now we are talking about 6-7X entering 2026, and we will see later on it does crunch even more. Right now, we are at 4X revenues. So from 20 to 6 to 4, and that’s the lowest in terms of multiples since 2016. That’s 10 years ago. P/E multiple for what multiples also comprise from close to 40 to close to 20.Talking about Adobe, Adobe trades at 5-year average of 30X, now at 12X. No wonder the CEO resigned. I don’t want to be mean, but I think it’s clear some CEO were very strong leading their companies into a SaaS paradigm, but were not as strong leading their company to a new AI paradigm. I think the markets are going to be brutal. If you are good at showing that you can transition to AI, you’re an important piece of the puzzle for AI, that’s one thing. But if the markets believe your products have not kept up, then it’s truly big trouble.I mean, they are not the only one. Intuit 34% decline in a month. Atlassian, minus 35 in a week. ServiceNow also down a third. They are not the only one, but definitely companies have to show some proof of either the lack of vulnerability in an AI world or their capacity to really move strong to a brand-new AI world. Nuno Goncalves PedroThe Structural Thesis — Why This Isn’t Just A Sell-OffWhat are the structural issues? Why wasn’t this just a sell-off? Why is this structurally a problem? The first thing is really around monetization and business model. SaaS 1.0 or 2.0, however we want to call it, was based on seat-based licensing. Seat-based licensing was the notion that with more employees and more users on the platform, there would be more revenue for the SaaS company. Very simple, very clear, very lucrative.Now, obviously, AI agents don’t occupy seats. An agent can do the work of 10 people, can do the work of 20 people, 30 people, 100 people, whatever it is. Therefore, if I’m a company, and I’m using agents, and not necessarily a human user, I’m not going to buy 10 licenses for the work of 10. I have one license, and it’s used by an agent that basically has access to that tool. That’s the first issue. The first issue is that the seat-based pricing, assuming humans, assuming a certain degree of productivity, et cetera, all of a sudden is under stress. Bertrand SchmittMaybe to highlight some point, not every SaaS company was focused on per-seat pricing. Me, when I led App Annie, we didn’t have a per-seat licensing or pricing at all, so we were focused on value-based pricing. But that’s true that around us, we have seen that quite a lot of your typical SaaS business was run on a per-seat pricing. Anytime there is a market downturn, you pay a dear price for your per-seat pricing. On top of it, these days, as you said, we have AI. In an AI world, the per-seat pricing model breaks down. Nuno Goncalves PedroIndeed. Now people are asking for other kinds of pricing schema, right? Either flat pricing based on certain usage patterns or, for example, outcome-based pricing. So depending on the outcome of what I’m trying to achieve, is it a booking of a sales call, is it something else? Whatever it is, I pay for that. But I do not pay for seats because that doesn’t work anymore.There have been a lot of movements around these licensing agreements and these basic elements. Some have actually now tried to create agentic licensing agreements. It’s like, “Okay, I have licensing agreements now for your agents, not for your end users.” It used to be end user licensing agreements. It’s now agentic licensing agreements. Obviously, there’s a shift.Part of the shift is, I believe people want to be in a measurement scale that is different. They don’t want just to pay for a seat. They want to pay for either specific outcomes that are very clearly measurable or have flat fees across the board on a variety of things. I think we’ll see the emergence of a couple of these business models and these monetization models more significantly. I do think we’re still to see some innovation around some of these monetization models, which will occur over the next probably few years as people are getting used to it. Okay, now it makes more sense for me to pay by this rather than by that.Again, because it’s a disruption, we’re still getting and nailing down what effectively the new monetization models and business models will look like for some of these players, but it still will be served as a service. We’ll come back to that later as well. Agents can do a lot of stuff and whatever, but it’s like agents and AI are software. AI is software, whatever you want to call it. AI is software at its base and its profound meaning and what it does, et cetera. Bertrand SchmittSeat-based pricing, usage-based pricing, yes, it’s too simple. Yes, it has its flaw. But at the same time, when the industry started, it made a lot of sense. That’s easy to manage, easy to control, at least from the SaaS company perspective. But definitely now that the industry is maturing, I can see that rise and the benefit and value of moving to an outcome-based pricing or to a value-based pricing. What I like with that also, it’s more truly win-win for both sides, for the SaaS companies as well as for the customer of the SaaS company. If you are more win-win, more aligned, I think it’s a better situation, more frictionless. I think it would be a big change.Another interesting piece of the puzzle, obviously, of all the changes we’re seeing is that one of the best assumptions in SaaS was you have 80% to 90% gross margin. If you are below 80%, there were serious questions coming your way in terms of what’s wrong with your business model as a SaaS business. Below 80% was blinking yellow light, below 70, blinking red lights. But now, it’s very different because AI-native companies, you’re expecting more a 50-60% gross margin.Obviously, if you’re SaaS companies, you better move fast to more AI-native tools and services. That will impact your margin. When you decrease so much your margins, of course, it will impact your valuation. There is no other way around that. You cannot value the same way a 90% gross margin business and a 50% gross margin business. That’s simply not reasonable. I think that one is part of the change and part of a different way to value companies. It’s very reasonable. Nuno Goncalves PedroThe first two structural issues is, one, obviously the per-seat pricing piece is potentially dying or at least becoming less pervasive in the market, added to these emerging pricing and monetization models that we just discussed, value-based, outcome-based, some usage-based pricing, some hybrid models that are also out there with some base subscriptions and then other kinds of things and tiers on top of it, either usage or outcome-based.The third big structural shift that we are seeing is, and I already alluded to it earlier, this notion of build-versus-buy. In the past, I think the market went fully into buy. In some ways, even beyond the, “I will buy one” solution that solves all the problems, we went into best in class. We went to unbundled buying: I’ll buy the best solutions for what I need in my corporation and enterprise needs.Now we’re getting a shift back into building: I’ll build my own stuff. I think a lot of it is relating to two things. One, there’s coding agents out there like Claude Code, Codex from OpenAI, and a bunch of other coding agents that have emerged. There’s a lot of solutions out there, like we mentioned already, Claude Cowork, that really managed to have agentic solutions into workflows that are deeply embedded into some of the enterprises.At the end of the day, I think there’s a lot more of this notion of, I have all my data in-house. I want to really leverage all the data I have. I don’t want to just use a third-party solution that has generic data. I want to use my data set, I want to use my stuff, and I want to basically fit that into ongoing improvements in terms of workflow.The other piece, I think, what’s happening with IT departments in some large corporations that’s leading to this build mindset rather than this buy mindset is also the notion of maybe we have too many people. How do we really express our productivity if we don’t have solutions that are at the core of our processes? If we have solutions at the core of the processes that we develop ourselves or that we develop in partnership with integrators, et cetera, but using some of these new AI platforms, we also have more visibility on the people that we can let go.Now, I know this is quite negative, but I think this has also been leading to all the layoffs that we’ve been seeing across industries recently, where people are like, “Well, I can just extract productivity.” We’ve seen some of those very visible ones. We were talking about Amazon and what’s happening at Amazon with the layoffs recently. A significant amount of layoffs recently announced.Then some other issues on the other side where apparently the junior engineers that were still working on stuff using Claude and other tools that they were using internally started breaking platforms and breaking systems. Anyway, definitely there’s a lot of that going into this build mindset. I want to have control. I want to make sure I understand where the productivity enhancements are, and that will give me more visibility on the people that I need to keep and the people that I need to let go. Bertrand SchmittI’m not so convinced about this part of the puzzle. I think that for many, AI is a convenient demand, but I’m more thinking that some companies, Amazon included, Microsoft, truly, truly over-hired in 2020, 2021. Yes, they scaled back a bit, 2022, 2023. But I don’t think they ever scaled back to what was reasonable given their needs. So it’s quite convenient to say, “No, it’s not management mistake of efficiency, it’s something new AI, and we have to adjust to that.”What I believe is true, however, is that you cannot fund both at the same time in the sense of you cannot finance an over-bloated workforce, and two, significant extremely large AI investment. At some point, these companies were faced with a choice, and they took a reasonable decision on this to be more efficient with their workforce.But personally, I think that actually the ability to do so much more with AI will make more companies think more about their teams and building things because when suddenly your engineers can be way more efficient, can build way more, the value increases. So you could argue that there is an opportunity for companies to deliver more, and as a result, I can see if you’re a good engineer, then there will be opportunities to build more value, potentially across more companies.So we might see a shift where you have more growth in software-related jobs outside the core top 10 bigger software companies, but growing more widely across your typical S&P 500 and even SMBs who could never afford to really deliver value with typical software engineering. But now suddenly, software engineering equipped with AI can be more dramatic in terms of value for them. Nuno Goncalves PedroI agree this is a scapegoat. I agreed that there’s a lot of posturing as well. If someone can lay off a significant percentage of their… It’s almost like the percentage of people you can lay off becomes your new pattern as a CEO, your new, “Basically, I’m saying right now to the market, I can cut…” I mean, Block, I think, cut off 40% of their workforce.At this point in time, seems a bit dehumanized. I think the tech companies are the worst cases, in particular because AI also does disrupt them a lot in their own processes internally. But it feels to me right now, it’s a little bit this one-upmanship of, “Okay, I can lay off more people than you can, kind of thing.” It’s precisely all the fears that a lot of people have around AI. It’s like you’re dehumanizing work. It’s like at the end of the day, people are still needed to work, et cetera. Bertrand SchmittBut I think Block might be one of these companies that completely over-hired over the past few years and never took the pill to reoptimize the business. Nuno Goncalves PedroI think we mentioned it at a previous episode that there was an estimate at some point in time that… For example, even Google had more than double the number of engineers they needed at any given point in time. So obviously, they did hoard engineering resources in other capacities. But at this point in time, it feels a little bit like up to you since being a software engineer right now is a kiss of death kind of thing. Which is weird because at the same time, we are seeing tremendous reallocation of capital overall in the industry towards infrastructure and platforms, where hyperscalers are at 660-690 billion in infrastructure CapEx for this year alone, and 75% of that being AI, where we are seeing a lot of movements around how do I budget accordingly if I’m a corporation.To your point, I think you made that point earlier, Bertrand, how if I’m the CIO of a company, do I allocate my resources more clearly, in particular, if I’m taking into account that I need to spend more money on AI and AI tooling and AI platforms. Obviously, at the end of the day, the CFOs are still there, and the CFOs are basically saying, “Hey, guys, we went into an unbundled world. We had all these agreements with all these people. I want more concentration.” At the same time, the CEO is telling me we need AI, “So whatever it is, you guys tell me what it is, but we can’t increase our budget for this stuff. We need to decrease it, and there needs to be AI in it.” Obviously, there’s a lot of reallocation also at a micro level within the corporate world. Bertrand SchmittYes, you cannot say it will be more built versus buy. At the same time, we are going to need less engineers to do the build. You see what I mean? Even with AI helping you, building which still cost you more, require more software engineering than just a buy decision. For me, what’s interesting is that not so many of these stories can be true at the same time. You require a next workforce, but at the same time, you’re going to rebuild your whole software stack from zero just because of the AI God that you just brought in from cloud. This is not reasonable, simply not reasonable. Nuno Goncalves PedroI think the thesis is that your top engineer is I think, in particular, the more senior engineers, can now do the job of 10. Therefore, what I am switching in terms of cost, I’m not saying I’m agreeing with the thesis, but the thesis is that. What I’m reallocating in terms of budget is, I’m reallocating towards spend at infrastructure platform level, on tokens, et cetera. That’s basically, I think, the thesis of what we’re seeing happening right now. Bertrand SchmittYes, but if you were just, quote, unquote, buying software, you’re not building software. You didn’t need software engineering to just buy software. Your software engineer that becomes as valuable as 10, yeah, but you had zero if you were just buying software. You see what I mean? Nuno Goncalves PedroNo, IT departments have always had engineers, the larger corporations. Yeah, for sure. Bertrand SchmittIt’s a very different game if you are moving from buying to building. It’s my point, I guess. Nuno Goncalves PedroIt is. Just to be clear, Bertrand, this whole build-versus-buy, the build is going to be done with a lot of use of outsourcing and a lot of use of service providers and a lot of use of integrators, et cetera. This whole bullshit of build-versus-buy, in effect, it’s a misnomer because at the same time, you’re going to have to hire, to your point, you’re going to have to hire companies, et cetera, to help you do this. It’s not magically that you can do it off the existing IT departments that you have. Bertrand SchmittExactly. The question will also be, is your first priority of business to rebuild Salesforce from scratch so that it better fits your internal need as a corporation because you have rebuilt from scratch with AI? I don’t think so. That for me is total overhyped bullshit. Klarna was big on that, this is total BS, quite frankly. Not only it didn’t work, but it makes zero business sense. Zero business sense. You’re not going to rebuild a CRM just for the fun of it while your software engineering could be focused on your core value proposition as a business. If you’re a company just starting, you have processes from scratch, you still don’t have solution, yeah, maybe you could consider that.But even then, is it really your priority versus building your core value proposition? For me, that’s a big question. But what I would expect, however, is that this overall trend mindset and stuff is going to keep the pressure on two software companies in terms of reducing tiers of cost, in terms of delivering more value, in terms of being more aligned to the business, and in terms of overall growth rates that are simply not the same as they used to be. Nuno Goncalves PedroBefore maybe we move to another topic, I think it’s clear, we’ll come back to that later, that there are a lot of overblown elements in this. You can never disregard a couple of very, very core elements. A lot of these software companies have very deep tooling into significant enterprise customers. You can’t just rebuild it from scratch yourself to your point. Not only does it make sense, but you can’t. It would take you years to do it. Good luck to you.Secondly, they have also distribution. They are pervasive in the market. They have sales forces. They have people that are selling out there. They have go-to-market teams. Again, we’ll talk about that in maybe one of our penultimate sections today. But maybe to move forward, we talked a lot about the public equity markets and how there’s been a reckoning by institutional and retail investors, et cetera.The Private Market FalloutBut also there’s been a private market fallout. The first one is very obvious to understand. Private equity firms loaded themselves with SaaS. Some even went after roll-up strategies in SaaS, like bringing a bunch of companies together and trying to attack a market and really getting a significant part of that. Software accounts for roughly 25% of the private credit market, which is incredible. Just that’s private credit alone, significant again. They’re loaded with a bunch of companies that have nowhere to go. They can’t IPO, nobody else is interested in buying them unless it’s for a huge write-off or write-down. That’s the first problem right now that we’re seeing in this fallout, which is the private equity market itself. Not only the buyout market, but also we saw a lot of growth funds loading themselves with private equity stock, with a rather SaaS stock, private SaaS stock.Right now, there’s nowhere for that to go. They’re stuck between rock and a hard place with a lot of solutions that are not growing at the rates they were growing before, with a public market that’s not really interesting right now to IPO in, because as we were mentioning earlier, the multiples have gone downhill dramatically, so it’s not interesting. Basically, it’s a chicken-and-egg issue. I would love to sell this now, but I can’t because I have awful market. I can’t IPO it either, so what do I do with all these assets? That’s the first issue here. Bertrand SchmittIt’s clear that you have to be pretty delusional to think that what’s happening in the software public markets is not impacting the private markets. We don’t know why it will be in six months. In six months, it could keep getting worse in the public markets. Six months, at some point, maybe there is a recognition it went too far in terms of adjustment. It’s always tough. But at the same time, you have to be prudent. For sure, what it means is that if I’m a private equity investor in a SaaS business, you have to be a very, very, very special SaaS company to get more financing these days at good terms.Sometimes it’s a very simple math. If you fundraise at 20X, even 10X, how do you go to get to another round of financing if now your multiples are at 4X? That simply makes absolutely no sense whatsoever. Or you need to have grown into your valuation enough that it’s not crazy anymore. If you raise at 20X, and now you’re in 4X multiple, then you need to have grown 5X in your revenues so that you simply stay at the same valuation, or maybe you have to accept a different valuation. But again, quite frankly, the tough part would be convincing investors that it make any sense to put money in a SaaS business. Nuno Goncalves PedroJust to rub it in, just to make it even worse, the secondary market, which was a great market for exits or partial liquidations, et cetera, is demanding now huge discounts. There’s no way I’m going to buy into a stock if it’s not growing at the same pace. I’m like, “I’m sorry.” I will buy your stock at a significant discount. In some cases, it might be what would be a lesser price per share than your last round or your last two rounds. Not just, I want a discount on what you think you’re worth, but it’s like, I want a discount on your last round.Because there’s liquidity issues also in some parts of the market, we were talking just about the private equity firms, some of these deals will go through. If all of this wasn’t quite enough, we have what’s happening in venture capital, which is very close to my heart, of course, because that’s where I play. If you come to me, it’s like I’m a SaaS player immediately off the game. I’m like, “Really? You’re a SaaS, tell me more.” I was just talking to a player recently, SaaS play, there was nothing around AI in their pitch.It’s not just because you have AI in your pitch that I’m going to give you money, clear, but if you’re doing a SaaS play and there’s no AI in your pitch, I’m like, “Am I missing something?” If it looks very classic, I’m like, “Oh.” There’s been a huge, huge reduction in confidence in the VC space in investing in SaaS. There’s a tremendous hyper focus on AI, and in AI investing, AI apps, platforms, infrastructure by most VC firms at this moment in time. And so at this point in time, if you’re a non-AI SaaS player trying to raise money, where’s your AI play? I think that’s the question you’re going to get. It’s going to be very difficult to raise, very difficult to raise. Bertrand SchmittI agree with you. Myself, I saw that SaaS startups with absolutely no AI in their deck, and I was so shocked. I was like, “Guys, where are you living? Are you living in a parallel universe? Are you living under a rock? What’s going on?” Then they are like, “Yeah, but we’re preparing something like that, I come back and prepare.”But even then, as you say, it’s not just leaving AI in your deck. It’s what are your proof points? What have you delivered? How do you make sure that it’s truly differentiator? And how does it make sense versus a pure AI native companies? How are you going to find the new cloud tools that are going to get out in a few weeks and more or ChatGPT or whatever? You have to have a very different proof point. There is nothing new in the past. It’s how are you going to survive against Google? How are you going to survive against Salesforce? How are you going to survive against Microsoft? So nothing is new.Software universe is changing. There’s always that big guys that can destroy you in a matter of weeks. So the question is more, how are you going to be smart enough not to be killed too easily and to find your way in a space that’s probably moving faster than ever? That is probably the difference is that it’s weeks after weeks, you have big change. I’m pretty sure it didn’t happen in that space before because I’ve seen there, I’ve seen that, and it’s moving faster than ever. But it’s nothing new that there is this big company potentially destroying your business. You have to be smart.I feel in some ways, maybe it’s the 2020s, but people stopped being smart, quite frankly. They just raised easy at very large valuation and think that you just do something sometimes pretty basic in terms of software development and that’s good enough. Your GTM is traditional, and you think you made it, and you deserve some investment. I think you must have seen some of this. I have seen a lot of this. In some ways, it’s good. The market is becoming more discerning. Nuno Goncalves PedroThe Bull Case — Is The Market Wrong?But is the market wrong? Maybe shifting to that, at least my perspective is it’s wrong. It’s not fully wrong, but it’s wrong. There’s a right sizing of multiples, but maybe 4X is not the right multiple either. This whole 20X on actuals and 40X on forward stuff didn’t make any sense. There is an argumentation to say that the market is oversold. All the banks have come forward. Goldman Sachs, JPMorgan, Jeffries, Morgan Stanley. Everyone’s come forward and said there’s been definitely, Bank of America, whatever, there’s been an overselling of stock, a dramatic overselling of stock. There’s been a panic that wasn’t warranted. The price has gone down too dramatically for some of these key players.I think part of it, in some ways, is what we were alluding to earlier, the fact that some of these players have built really important stacks that are fitting their customers in a significant on core processes. You can’t just rip it off and put something new. Magically, it will work. It will be around building things around it rather than building things that replace it. Will there be over the long term potential disruption of some of these players around CRM and other solutions? For sure, we’ll see it.But definitely, some of the existing players, public companies that are large, are here to stay, and they themselves will buy into these markets. They’ll acquire positions into other service providers into toolmakers, into other platforms that allow them to be fully AI-enabled and to make their platforms more AI-enabled. I do think there was a huge amount of overselling. The second thing we already alluded to as well as go-to-market. If I’m selling something to someone, there’s a salesperson involved or there are a couple of salespeople involved, they’re not going anywhere. So in some ways, that relationship building with CIOs, with their teams, with procurement teams, all of that is still there.And a lot of the large SaaS players have been doing this for decades. So they have the surface of attack and go-to-market that will take a long time to build for even some of these startups that are disrupting, so to speak, the market. My view is there has been too much panic and the modes of the large players that are already public, in some cases, haven’t been considered at all. Bertrand SchmittThere’s definitely some truth in that. Another piece of the puzzle is that if SaaS is not growing as fast as it used to be, it’s still growing. Many companies are still very good cash generation machines. Many of these companies are moving to AI full speed, improving their tools, changing how you can search their data, how you can leverage their data. They are very close to the data, so they know best how to deliver value on this data. They can integrate existing AI tools. There are a lot of ways for them to capture part of the value that native AI companies are claiming they will get. I think it’s definitely going to, and we’ll talk more later on. I think there will be a question around how do you differentiate the best SaaS companies from the worst SaaS companies in that context.But maybe I just felt we moved a bit quickly on one big event that’s shaping the software industry, it’s the current crash in private credit. Do you have some thoughts about that? Because what’s happening there is pretty crazy, to be frank. Nuno Goncalves PedroYeah, we’ve seen a lot of these players like KKR and Apollo getting slaughtered. Basically, Blue Owl, TPG, Ares, KKR all fell double this in one day on private credit exposure fears. Overall, Apollo has fell 7% as the date of as we were recording BlackRock, 5%. These guys were walking on water and all of a sudden, there was like, “What happened?” And what happened was private credit exposure. A lot of the concerns in the market is private credit is super sexy, and for those who don’t understand what it means is I’m giving credit to a private company in exchange for something, either warrants in the company or revenue sharing in the future, or I’ll get your revenues in advance from you, or I’ll take, whatever it is. There’s over exposure.There’s this potential logic that all these guys are scaling, all the companies that they give private credit to are scaling. And now there are concerns that there might be some dramatic credit in the market, that some of these companies are actually going to die, they’re going to implode, or they’re not going to really fulfill their covenants in their private credit agreements. Bertrand SchmittIt was hidden in plain sight, but that some of these private credit funds at 25, 35% exposure to software, IT, and SaaS, so a huge chunk in an industry where you bet on the long term revenues and cash flow to pay back your loans, while at the same time there is a discovery that this business may be at risk in the next three, five years or even one year because of AI.I think that was the first big chink in the armor that suddenly the creditworthiness of these companies might not have been evaluated properly. But two, it looks like there is also fraud that has been happening. I was reading stories how three, four people, accounting companies, were valuing and estimating loans for hundreds of SaaS business. Good luck, this is crazy. It looks like there is another layer to that story. Nuno Goncalves PedroWhen there are industries building a lot of wealth or apparent wealth that’s coming a little bit from out of nowhere, the likelihood that there’s fraud and things that were not properly done is, it sadly increases dramatically or exponentially. I think we’re seeing just maybe the first effects of that. Bertrand SchmittI was reading, for instance, that one of these big funds was no haircut across the portfolio, ever seen value that was 100%, whatever. One quarter after that, one of their clients going out of business and they lost everything. In three months, you move from no haircut to 100% haircut, decent enough part of your portfolio. This is crazy for a credit business. Nuno Goncalves PedroIt’s ostrich syndrome. You just put your head under the ground, and you’re like, “Hey, whatever.” I don’t know. Bertrand SchmittYeah, it’s zero mark-to-market in an industry that should be relatively conservative. This is private credit. This is not VC, this is not startup, this is not equity, this is credit, so pretty scary. Another piece was like, some of them were supposedly senior on the debt, but they were not so senior after all, this is insane. You claim seniority, but you don’t have it.My point, I think what’s happening in private credit is maybe it all started with that what’s going on, a lot of software exposure. It’s risky because of AI, but the more investor dig into it, that’s when they started to realize that maybe there is more than just that software issue. I guess, all of this is going to be an issue for software business because if suddenly you cannot get loans anymore or the loans you add, you have to pay them back or when it’s time to pay them off, you cannot renew the loan. There is nobody else to turn yourself to get another loan to replace it. That’s not going to be fun and that’s going to impact your growth rates. That could potentially also even be worse than that, be dramatic for your own business survival. Nuno Goncalves PedroMaybe now switching back to the positive part for the bull case. We think the market’s wrong, not fully, but wrong. The other side is still things move on. We’ve also had the same issues in credits in several industries in the past when markets imploded and credit came back. In some cases, it took a while. In other cases, it came back relatively quickly. One great analogy on making a bull case on why all of this stock that was sold was oversold, there’s too much stock being sold on SaaS and at prices that don’t make any sense is an analogy, precisely, for example, with retail. Amazon was going to destroy everyone their mother in 2010, and it did not. It was going to destroy Walmart. Walmart passed the $1 trillion market cap. Bertrand SchmittNot too bad. Nuno Goncalves PedroSo what happened? They adapted. They had huge advantages. They had huge advantages in terms of their customer base, presence, relationship with their suppliers, with the offerings they had, et cetera. They had huge advantages of economies of scale, and they leverage those advantages. And those advantages ultimately materialized in tremendous increase in revenue, tremendous increase in market capital as well.Amazon has done really well as well. It’s not like Amazon didn’t do well. Again, I think this notion, people sometimes have this difficulty in separating the notion of disruption from the notion of replacement. Disruption doesn’t mean necessarily full replacement. You can disrupt industries, disrupt players in that industry, and still those players will exist 10, 20 years later, and they’ll be much bigger because they adapted. The ones that don’t adapt may be killed.But the disruption doesn’t necessarily mean replacement or killing. It means just that effectively the rules of the game, the business model, which we already talked about, monetization models, the way that capital flows in that industry, et cetera, all of that shifts. It doesn’t mean that necessarily the existing players are not going to exist tomorrow. In some cases, they will exist and they’ll be even stronger tomorrow. Bertrand SchmittI think what’s happening is truly a disruption of the SaaS business model, of the SaaS valuations, of the SaaS analysis, because now you need a new prism to analyze it. What are the markets doing in the meantime? They are just dumping it, waiting for, “Okay, how do we look at it in a different way? Who are going to be the winners and the losers?” For now, we don’t care, they’re all losers. But I think that the next piece of the puzzle for us in this episode, but for the market is, how are we going to separate the wheat from the chaff? Who is going to survive? Who is going to more than just survive? Who is going to thrive in that new industry. Nuno Goncalves PedroThere I feel the ones that survive, there’s a couple of obvious ones we can go into. Two that immediately come to my mind are data infrastructure, the Snowflakes, Databricks of the world, because this is the underpinning of everything that’s happening around AI. I don’t see the data infrastructure fundamentally shifting right now. It might in the future, but right now I don’t see it fundamentally shift. Those guys have, if anything, tailwinds rather than headwinds.Then the other one that’s very obvious to me is cybersecurity, where I think AI is very additive to it rather than just necessarily replacing everything that exists. In some ways, that already been used for a while, certainly by the top players. Definitely, those are two immediate categories and areas that come to mind that have maybe more headwinds and tailwinds where really AI is adding rather than subtracting to it. Bertrand SchmittNo, I totally agree with you concerning data infrastructure, cybersecurity. You could argue if you take cybersecurity, that with the rise of AI attacks, with AI making it easier than ever to generate attacks, you better build up your security. Nuno Goncalves PedroWith AI? No, but you have to have AI on your side defending as well. The only way to defend AI is AI. Bertrand SchmittThat’s my point. Your cybersecurity vendors will become AI-enabled, will leverage AI at scale in order to defend you, else they won’t be able to defend you, just quite frankly. Nuno Goncalves PedroCorrect. Bertrand SchmittThat’s part of the game. Data infrastructure, no questions. Again, I don’t think you want to redo your infrastructure with brand-new tools, brand-new stuff is the current tools are working great and doing the job. Maybe another piece of the puzzle is that vertical SaaS, domain-specific tools, healthcare, manufacturing, if you have proprietary data, regulatory modes, it will be much harder for AI to disrupt quickly. If you are not disrupted quickly, you have more time to readjust your business model, to adjust your business model, to leverage AI to improve your business model.Again, of course, some companies, we have seen with Adobe, for instance, have not proven great skills at adjusting to AI. Not everyone is going to get out as a winner. I think some categories have better chance to actually not just survive, but potentially thrive. Another piece are systems of record. If you are holding proprietary non-scrapable data that AI needs to function, that you have deep switching costs protecting you, you are not going to disappear right away. I think you will probably survive. If you are smart enough, you might be able to even adjust and leverage AI.But I can see some might just stick to their revenues and hold companies hostage and might not innovate a lot. I guess we’ll do well on the short run, but on the medium to long I would definitely more worried. Nuno Goncalves PedroOne point I would like to make is at the end of the day, there’s more than that. The algorithmic methodologies you should use for specific industries, for specific verticals, for specific use cases could vary. We’re still very early in a lot of the application of some of these AI methodologies. We’re not early in the development of the research around them. They’ve been around for decades, but the application of them is still relatively early. I think that’s one of the advantages why vertical SaaS companies and vertical SaaS solutions right now might have an advantage, because the domain in which you’re operating, even algorithmically, is actually different, and you need to really right purpose it for those environments and for those domains.For me, that’s an important point to make. It’s not just any vertical SaaS. I think vertical SaaS, where there’s algorithmic distinctiveness, definitely has a shot at it. Other might not. We just saw a lot of discussions around legal tech and how legal tech got slaughtered with the launch of Claude Cowork, for example. Definitely, it will depend a little bit on the verticals. Bertrand SchmittTake the legal side. There has been some interesting decision recently where basically, if you use AI for legal advice, then this data, this discussion is not privileged. You are at big risk of discovery. There is a lot of issues that if you are working with real lawyers, will not be there. Your data is not discoverable, your discussion stay private, so it cannot be used against you. I think companies have to be very careful and very worried about how some of these tools are being used because it’s creating new risk. Some of these tools are not going to get privileged in the coming few months, I don’t think so.You could argue most of these companies in the first place claim a right to access your data and leverage it. I think that even in legal, it would be interesting to see how it evolved. AI will be able to claim some privilege at some point? Maybe, I don’t know. But on the short run, I can imagine how the legal profession, for instance, will not let it happen too quickly, and how you have to be very careful. It’s great to move fast, but you have to be careful with what is it that you are getting into. Nuno Goncalves PedroLet me guess, the last company you’re going to say or the last type of companies that you’re going to say are like the survive, thrive are AI-first or AI-native companies. Is that correct? Bertrand SchmittYeah, I guess. Yes. They are going to be less disrupted by AI, given that they’re already AI native. Nuno Goncalves PedroThey are AI. Bertrand SchmittWe are going into another territory. Even if you are AI-native, are you going to still get killed by Claude because you don’t have enough technology or ChatGPT because you don’t have enough technology? You are just that basic rapper around another AI tools. Here my perspective and what I share more and more with some entrepreneurs is you have to be careful if you are just an AI native company, but ultimately you are a very AI light in the sense that, yes, you are a native, but you are just reusing other LLMs and stuff, and you have not built any proprietary tech or moat with your data or in your industry. That’s going to be trouble. That’s going to be trouble.I’m not sure the market discriminated well enough at this stage, but I think there will be quickly some premium around, have you built a real technology mode? Are you really in such a situation that you are not going to get killed by a Claude or ChatGPT in a few weeks? I think there will be some discrimination that’s going to happen. Ai native won’t be enough to save you, basically. Nuno Goncalves PedroI think there’s one thing. One is what you’re saying. Is there fundamental technology differentiation and/or product differentiation that will sustain itself as a moat? The second thing is, even if it’s an AI app at a higher level, the reality is the guys that are in the market today, the OpenAIs, the Googles, the Anthropics, etc., they’re not going to address all use cases. There are places where some use cases will still exist. We saw that in the mobile app economy.In some of these use cases, you’d be like, why hasn’t, for example, Apple addressed the need for this kind of solution, whatever, and maybe it took them a decade to do it. Then, when they did it, they almost killed the market. But you have some of these AI apps that I think will still be in the market that will emerge and will address use cases that for some time, for some reason, OpenAI, Anthropic, etc., won’t go after. To Bertrand’s point, and I think importantly, if you’re an entrepreneur, if you’re writing on a very specific use case, and there’s seemingly a high likelihood that any of these players are going to address at some point, you’re not in a sustainable place. You’re not going to be around very long. Bertrand SchmittOr you have to take that initial leadership position and transform it into a deeper technology mode, a business mode. You have to leverage that first mover advantage, maybe, to something deeper than that, something more defensible. Maybe you pivot also in term of industry. You started in industry A, but you realize industry B is really the good one. You have to really optimize your way and not take anything for granted. Nuno Goncalves PedroBertrand, do you remember when it’s like every release of iOS and whatever, we were like, what industry is Apple going to kill now? What are they integrating? There was a period of time where it was literally like every big release, every major release, the yearly one, you’d be like, what industry are they going to kill now? Bertrand SchmittTotally. Totally. I think the same is happening. Definitely, we say AI, but I think some players have been smart enough to zigzag around that onslaught from Apple, from Google. But some will stay put. We think it’s not going to happen to them. Yes, they got into trouble pretty quickly. I think also what we have seen is that a lot of value could be from players who are simply more neutral and independent vis-à-vis a platform. If you need someone in the middle, your three or four mobile platform, or now your three or four LLMs or AI platforms, there might be value you can extract because companies are not… That’s another piece of the puzzle.You don’t want to just depend on Claude. You don’t know in three months, ChatGPT has a better model. You will want to make sure that whatever you are running can adjust to a change of LLM providers, for instance, or tool providers. I think, for instance, one position could be that mutual player, the one gives you the ability to adjust quickly to different technical AI development. We will see. But I think there are different strategies you can go through to make sure you end up not being killed, and that will require smart entrepreneurs. Nuno Goncalves PedroSeparating The Wheat From The Chaff — Who Survives?We talked about who survives, who doesn’t survive. Let me start with one. Or where I think will be categories that will be incredibly under attack, so a lot of players, I think, will disappear or will become very, very small. One obvious for me is anything that relates to the small, medium business markets, so very SMB-focused SaaS, a lot of regional SaaS stuff that has emerged, copycatting in certain markets because the larger players didn’t want to expand in some of those markets.I think a lot of that stuff gets just replaced because a lot of the SMB markets are price sensitive. A lot of these markets are also best effort-driven. It’s like it doesn’t need to be perfect, it just needs to do the basic stuff. Therefore, I see that market as a market that’s going to get, in all honesty, over the next 3-5 years, slaughtered. It’s not going to be rapid death, but some of them are just going to be totally replaced. Bertrand SchmittI agree with you. If you don’t have a big enough moat, if it’s very shallow, if your clients are moving quickly, you can easily switch based on a small price difference. That’s definitely trouble. Nuno Goncalves PedroI’ll let an anecdote just so people I don’t understand. Because people say, but these regional SaaS solutions normally because of their specificities to the markets and stuff like that, whatever. I literally drafted the other day an agreement, a semi-agreement relating to Portuguese law on Claude in Portuguese, from Portugal, not Brazil and Portuguese. It drafted an agreement from scratch based on my prompting, and it took into account specificities of the Portuguese legal system and taxation. Guys, it’s like, this is a freaking consumer tool. Localization of what? The tax regime and whatever? Who gives a shit? It’s like, again, I think that’s the market that definitely will get a pretty significant beating. Bertrand SchmittAnother market for me, we talk about Adobe, but content creation tools. Here, I think there is a dramatic shift in how you use them. Before you use another Photoshop to replace something in a picture, change a slightly picture stuff. Now, you just say, hey, remove this guy from the picture. Hey, replace. Hey, create that picture from scratch. I have five photo IDs, put these guys in context, put them in your meeting room, and go for it. This is such transformational versus how you used to work before that I think some of this industry is getting destroyed.There will be simply no point of using these tools anymore because something else is just 10X better. That is not even a question. You could argue there is still a niche of professionals doing stuff in an always because it guarantees a bit more higher quality or this or that. Sure. But overall, this is getting disrupted big time and the much bigger business might be totally new and totally AI native. Nuno Goncalves PedroI will do a parochial comment. We have two investments in the content creation space, one more on the marketing side and the other one more on the hardcore content creation side. They’re both AI from inception, so they’re both AI native. One of them is called LetsEnhance, the other one is called blaze.ai. I feel it’s true that there’s going to be a lot of replacement of some of the content creation tools in certain markets like consumer and prosumer, driven by the Nano Bananas of the world and all that stuff.But on the top end and in enterprise and all that stuff, we feel that AI native content creation tools are there to be. It’s actually one of the areas of what I would call use cases or AI apps/platforms where I feel being AI native will give you an advantage. Just being a cross-cut play around the market being Anthropic or OpenAI, whatever, actually won’t solve the problem for some of the markets that need to be served in. Bertrand SchmittMakes sense. I agree with you. Maybe more quickly, some point solutions, relatively high risk. If you have a single function tool, then could be easily replaced potentially by an AI agent. We already talk about it. If you are too SMB-focused, that’s not the best segment of the market, typically. Maybe you can have a single test to check if that company is at risk. If you were to replace that tool, can a $20 a month AI agent do this task? If switch it cost are low, then maybe that’s not a good business opportunity. Maybe you should not invest, or you should sell the stock.Again, maybe you have to focus more on regulated niches, hardware dependent, critical private data, solutions where there is already outcome or value-based pricing in place. You have to put some rules and analysis to help you understand, is this business at risk of significant disruption or not? Not all business are the same. As an investor, that might mean that there would be some good opportunities. SaaS businesses that are going to emerge even stronger right now are at a cheap discount. Nuno Goncalves PedroAbsolutely. I think at the end of the day, certain basic workflow tools that are out there to simplify CRM, some very basic ERP modules, anything that’s very, very simple in terms of if this then that, all those tools are also going to be slaughtered relatively soon, sadly. If you’re in that space, maybe time, as Bertrand was saying earlier, to pivot, to go after some fundamental differentiation, or to do something else. You want to conclude, Bertrand? Bertrand SchmittConclusionSure. I guess we could see that from a trade perspective, from an investor perspective. I think it’s creating quite genuinely some opportunities. Some stocks are in the bargain, some of those are value traps, so you better get your investment skills in order. PE, private credit, definitely a lot of risk, not just from AI, I think from basic fraud as well.Secondary market, as you just say, it’s not an easy one. It’s a canary in the coal mine. I think you will agree, but this is before getting between AI native versus everything else these days, especially if you are more early stage. A more established business, it’s a different thing. But right now, just starting a regular SaaS company, that’s a tough one. From an investor perspective, you need to pivot as fast as you can from seed-based pricing, hybrid, outcome-based, value-based pricing. You have to do the move quickly. You don’t want to be pushed when it’s too late.Build-versus-buy is real, and that will only accelerate as coding agents mature. Vertical specialization, proprietary data are strong moat. They were before as well, so it’s nothing new. But I think the importance of having a true moat is more critical than ever. Lots of companies have received investment with not enough moat, and that’s the one getting destroyed in the private and public market. If you have strong matrix, there is a question of when is a good time to exit? I don’t know if the relations will ever come back. I think it truly depends as well on your business, a strategic fit with acquisition opportunities.Anecdotally, I have seen some businesses who look at exit opportunities and now are finding attractive options. It’s not all that dark, I would say. Maybe to answer to the question, do we have a SaaS apocalypse? Yes and no. Some companies are going to end badly, some companies are going to emerge stronger. I think that’s it for today. Thank you, Nino. Nuno Goncalves PedroThank you, Bertrand.

Ep. 332For decades, the stock market meant public companies. Apple, Microsoft, Amazon — the giants everyone invests in.But something big has changed.More companies are staying private longer, and some of the most valuable businesses in the world — SpaceX, OpenAI, Anthropic, Databricks, Stripe — are not publicly traded.So the question becomes:Are private markets where the real growth is happening now?In this episode, Gabriel Shahin breaks down the shift from public markets to private investing, why billion-dollar companies avoid going public, and what investors need to understand before jumping into private stock opportunities.In this video, we discuss:-Why fewer companies are listed on public exchanges today-Why major companies choose to stay private longer-How SPVs (Special Purpose Vehicles) allow investors to buy private shares-The fees, carry structures, and costs behind private investments-Why governments sometimes push companies to go public-The pros and cons of private markets vs public markets-The importance of operators and leadership in early-stage companies-The risks of hype investments (like NFTs and speculative trends)-Private investments can offer incredible upside — but they also come with less transparency, limited liquidity, and higher risk.As always, the key question remains:Is it a good company solving a real problem — or just a hot trend?

What does it really take to build a successful cybersecurity career in today's fast-changing world? In this episode of Life of a CISO, Dr. Eric Cole sits down with Jesse Scott, a cybersecurity leader whose career spans NATO, Ernst & Young, CrowdStrike, Barclays, Amazon, Databricks, and startup leadership. Together, they break down what aspiring CISOs need to know about navigating big companies, fast-moving startups, and even launching a company of your own. Jesse shares lessons from working across seven countries, leading in both enterprise and startup environments, and staying ahead in a world being reshaped by AI, cyber risk, identity security, automation, privacy, ransomware, and nation-state threats. This conversation also dives into how AI is changing security operations, why CISOs must think more like business leaders, and what it means to take control of your own career in cybersecurity. If you are a CISO, cybersecurity leader, security architect, founder, or aspiring executive, this episode is packed with real-world insight on leadership, innovation, risk, and the future of cyber defense. In this episode, you'll learn: How startup experience can accelerate your path to CISO Why every cybersecurity leader should understand business and revenue How AI agents are transforming security teams and attack surfaces What CISOs should know about privacy, automation, and data poisoning Why betting on yourself may be the smartest move in cybersecurity