That Was The Week

This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.thatwastheweek.com/subscribe

This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.thatwastheweek.com/subscribe

This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.thatwastheweek.com/subscribe

And an AI Generated Linear PresentationFull Google Notebook here: https://notebooklm.google.com/notebook/9c841415-1305-4cd3-82cb-2ae2ff54596c This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.thatwastheweek.com/subscribe

This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.thatwastheweek.com/subscribe

This week’s headline: When Your AI Breaks Your Heart.GPT-5 arrived “better” by every metric—yet users begged for GPT-4o back. It wasn’t about accuracy. It was about personality. People felt like they lost a friend. OpenAI listened, backtracked, and gave them their companion back.But should it have? Progress is messy, and heartbreak may be the price of change.The Pain of ChangeUsers bond with AI like colleagues or partners—and revolt when those bonds are broken.OpenAI faced its first true PR crisis, forcing it to act like a consumer company, not just a lab.But longing for “the old AI” is as unrealistic as yearning for Windows 95. Change is the only constant.The Shifting WebCloudflare’s Matthew Prince warns: AI is killing the Web.Perplexity’s $34.5B bid for Chrome shows the fight for browser control—but the browser itself may be obsolete.Just as Spotify freed music from CDs, AI is unbundling content from URLs and tabs. The web isn’t dying—it’s being liberated.Inputs vs. ManipulationAI’s real weakness? Databases. Models still can’t query live inventory, prices, or transactions.“SEO for AI” tries to paper over this by gaming prompts—just like spammers gamed Google.But the future isn’t tricks. It’s context engineering: clean data + authentic inputs.Winners & Losers40% of VC money is going to just 10 AI deals. The power law rules: winners take almost everything.Geoffrey Hinton warns of AI “alien beings,” but others argue that fear distracts from real infrastructure challenges—like power grids, chips, and data quality.The Real OpportunityStartup of the Week: Torch, a health AI that turns a decade of medical records into personalized insights.This is the real future—integrating trustworthy data into AI, not re-skinning old personalities.The controversy this week is simple:Do we cling to the familiar—or embrace the heartbreak that comes with progress?While some mourn GPT-4o, the real story is far bigger: AI is rewriting law, health, energy, and the web itself. And it’s happening whether we’re ready or not. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.thatwastheweek.com/subscribe

This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.thatwastheweek.com/subscribe

ContentsVenture CapitalData is actually not a great VC-backed businessWhy Every International Founder Should Spend Three Months in the U.S.A Path Forward for Seed VCsWhy Seed Rounds Are Growing as Startups ShrinkWhy our access is improvingOn 9 data-driven tips for YC startup foundersUltra-Unicorn Investors: These Firms Have Amassed The Largest Portfolios Of $5B+ Startups Landscape of VC-Backed M&AEssaysA Summer of AI in San FranciscoThe Satya of Satya’s Layoff MemoAds are inevitable in AI, and that's okayThe AI Search Tipping PointThe Slow Death of Social Networks10 Things I Wish I Knew Before Vibe CodingPageRank in the Age of AIEuropean WeaknessWise shareholders back plan to move listing from the UK to the USAIAs Anthropic goes, so goes the generative AI trade, says Big Technology's Alex Kantrowitza16z GP, Martin Casado: Anthropic vs OpenAI & Why Open Source is a National Security Risk with ChinaBalaji Srinivasan: How AI Will Change Politics, War, and MoneyAI that was inevitableIconiq set to lead $5bn funding round for AI start-up AnthropicThe Evolution of AI Agents: Navigating the “Fog of AI” in Rapidly Changing Foundations | Stanislas Polu and Harrison ChaseWhat will it take for robotaxis to go global?The AI SDR Reality Check: How To Actually Make It WorkOpenAI’s IMO Team on Why Models Are Finally Solving Elite-Level Math#259: Why Data Is the StackGoogle Lands $1.2 Billion Cloud Contract From ServiceNowPew Study: Google Users Click Less When AI Summaries Appear in Search ResultsLoveable and Replit Both Hit $100M ARR in Record Time. The Vibe Coding TAM: How Big Can This Market Really Get?New AI architecture delivers 100x faster reasoning than LLMs with just 1,000 training examplesTesla signs a $16.5 billion chip contract with Samsung ElectronicsThe Making Of Dario AmodeiChinese TechKimiChina’s AI Gambit: Code as StandardsHow Hangzhou Spawned Deepseek and UnitreeZhipu crushing benchmarksXi Jinping is the main thing holding China backChina Prepares to Unseat US in Fight for $4.8 Trillion AI MarketMediaNew Media: Podcasts, Politics & the Collapse of TrustIPOFigma’s Auction-Like IPO Set Up to Capitalize on Strong DemandEducationWhy You Should Still Study Computer ScienceInterview of the Week"AI Is Too Busy to Take Your Job: The Electrifying Truth about our AIgorithmic FutureRegulationBoston city council members introduce a bill to require drivers in WaymosM & APalo Alto Networks agrees $25bn takeover of CyberArk This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.thatwastheweek.com/subscribe

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Key Trend 1: Hyper-Accelerated Scaling and New Venture Capital DynamicsSignificance:AI innovation is driving hypergrowth that shatters traditional timelines. Companies now catapult from zero to hundreds of millions in ARR in months, not years. Venture capital must adapt to this new reality with massively larger and risk-tolerant funding rounds focused on parallel scaling — raising as much capital as revenue grows to capture market share rapidly.Why it matters:The "burn rate is a feature, not a bug" mentality defines funding strategies today, making capital intensity a necessity in winner-take-all AI markets. Companies ignoring this shift risk falling behind or being outspent by competitors who build moats with talent, infrastructure, and data first.Key Trend 2: Talent and Data as Critical Moats in the AI Arms RaceSignificance:Talent wars are escalating, with massive compensation packages used to acquire top AI researchers and engineers, reflecting a strategy to build defensible moats beyond pure technology. Additionally, proprietary data pipelines and reinforcement learning processes are becoming crucial competitive advantages, often trumping model architectures alone.Why it matters:As AI models become commoditized and easier to replicate, the real differentiation lies in costly-to-copy human capital and exclusive data ecosystems. Companies investing in these defensive layers will sustain leadership and fend off rapidly emerging competitors.Key Trend 3: Redefining Content Economics in the AI EraSignificance:AI’s reliance on vast amounts of web content to train models, often without compensation or permission, is triggering a fundamental rethink of content ownership, access, and monetization. Cloudflare’s new policies signal a shift toward pay-for-access models that require AI companies to compensate content creators, disrupting the previous “free crawl” economic bargain.Why it matters:This reshapes incentives for publishers, creators, and AI businesses alike. Content providers gain leverage to set terms and generate revenues from AI models, while AI companies must adapt business models to accommodate these new costs, potentially accelerating AI ad monetization.Key Trend 4: The Great Differentiation — Building Hard-to-Copy Moats in an AI WorldSignificance:As AI makes imitation easy and replicable, companies must differentiate through costly signals, authentic experiences, and unique assets that competitors cannot copy cheaply. This includes physical infrastructure, branding, cultural elements, and deep human expertise — all forming sustainable moats in a landscape of digital abundance.Why it matters:In a world where digital replication is trivial, the economic value shifts toward rarity and authenticity. Companies adopting this mindset can build lasting competitive advantages that resist commoditization.Key Trend 5: The Geopolitical and Regulatory Landscape of AISignificance:AI development is not just a technology race but a geopolitical contest, with varied national approaches balancing innovation speed and regulation. Europe’s AI Act exemplifies efforts to govern AI but faces pushback for potentially stifling competitiveness compared to the US and China’s growth-first posture.Why it matters:The regulatory environment shapes where and how AI innovation flourishes. Diverging standards and delayed coordination may influence global market leadership, investment flows, and the speed of AI adoption.Discussion QuestionsHow does the new model of “parallel scaling” of funding and revenue fundamentally change startup growth strategies in AI compared to traditional SaaS? What risks and benefits does this introduce?With talent and data becoming primary moats, is the AI market at risk of consolidating power among a small set of firms? How can startups compete in such an environment?Cloudflare’s “Pay Per Crawl” aims to rebalance value between content creators and AI companies. Will this model incentivize innovation or hamper the open data flows AI depends on?In a world where AI makes copying easy, what are the most viable forms of costly signals for differentiation? Can digital firms realistically replicate physical or cultural moats?Given the divergent regulatory approaches between the US, Europe, and China, how might geopolitical competition affect the speed and ethics of AI adoption globally?How do the controversies around tokenization and digital asset legitimacy, like OpenAI’s rejection of Robinhood tokens, reflect broader regulatory challenges for blockchain-based financial innovation?Is the venture capital industry prepared to adapt investment models to AI’s capital intensiveness and growth patterns? How might smaller VCs or new investors respond to the concentration of “ultra-unicorns”? This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.thatwastheweek.com/subscribe

This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.thatwastheweek.com/subscribe

OverviewThis newsletter issue commemorates 20 years of TechCrunch, reflecting on its landmark influence in shaping the startup ecosystem and tech journalism since its launch in 2005. Beyond nostalgia, the content reveals key ongoing shifts in technology, venture capital, AI innovation, and market dynamics that continue to define the industry’s present and future.Listeners will gain perspective on how TechCrunch grew from a simple Web 2.0 weblog to a foundational startup network hub, alongside insights into current critical trends such as AI’s evolving role in venture capital and software development, Apple’s design and AI strategy, evolving IPO markets, and debates around AI ethics. The combination of historical context and forward-looking analysis makes this a compelling episode for anyone interested in the tech industry's trajectory.Key Trend 1: The Enduring Influence and Evolution of TechCrunch as a Startup NetworkTechCrunch’s founding vision was not only to report new Web 2.0 companies but to serve as a connective platform for entrepreneurs, investors, and innovators globally.It emerged as the definitive startup network akin to how Facebook shaped social networks, fundamentally influencing tech culture, funding, and ecosystem formation.Today, TechCrunch remains a vital resource, expanding its global footprint with strategic partnerships and deeper engagement in key startup hubs like Europe.Key Trend 2: AI’s Growing Impact on Venture Capital, Software Development, and Industry StructureAI continues to reshape venture capital with strong focus on B2B operational tooling, platform/API-first startups, and developer-centric innovation.Large models and AI coding tools (e.g., vibe coding, integration in Xcode) signal a shift towards AI-assisted software creation workflows.However, challenges remain in reasoning capabilities of AI models, skeptical internal debates on AI safety, and ethical implications within leading tech firms.Strategic investments and valuation surges of AI companies, such as Anysphere’s rapid growth and Meta’s big bet on Scale AI, highlight intense competition for AI supremacy.Key Trend 3: The Resurgence of Public Markets and Shifting Investment Dynamics2025 has marked a reopening of the IPO window, especially favoring growth-stage B2B SaaS companies and innovative tech firms with strong fundamentals.High-profile IPOs like Circle and CoreWeave demonstrate renewed investor appetite, with smaller deals sometimes outperforming large ones.Secondary markets in venture capital are becoming primary liquidity sources, with record transaction volumes and large funds specializing in venture secondaries addressing liquidity constraints.AI and defense tech sectors continue attracting major funding rounds and valuations, underpinning strategic industry shifts.Apple’s new “Liquid Glass” design language and UI changes blur lines between iPad and Mac, signaling acknowledgment of evolving user expectations.AI-driven interfaces are moving beyond traditional input methods to embrace natural language, voice commands, and conversational experience.Voice AI technologies, such as “Voice in a Box” and true speech-to-speech models that incorporate prosody and emotion, are poised to revolutionize both consumer and enterprise interfaces.The future of devices will increasingly be defined by AI assistance quality rather than hardware aesthetics, with “legacy” hardware becoming less relevant.Key Trend 5: Ethical, Social, and Political Implications of AI and Tech PlatformsMajor tech companies wrestle internally with AI safety, privacy risks, and ethical governance amid fierce innovation pressures.AI’s societal impact carries dual potentials for utopia or dystopia, prompting calls for governance frameworks balancing innovation with responsibility.Social media platform changes, such as X’s transformation and decentralized alternatives like Bluesky, reveal ongoing tensions in moderation, community cohesion, and political discourse.Criticism of Big Tech growth focus and user experience degradation shows persistent cultural dissatisfaction despite transformative potential.Discussion QuestionsHow has TechCrunch’s role as a startup network reshaped the venture capital ecosystem compared to traditional tech media? What lessons does this hold for emerging platforms today?Given the dominance of B2B and automation-focused AI startups in YC’s recent accelerator cohorts, what does this suggest about the future directions of AI entrepreneurship versus consumer applications?Apple is pushing hard on design and controlled AI integration, while Meta invests heavily in superintelligence labs—how do these divergent strategies reflect different visions of AI’s role in society and technology?What are the implications of the IPO resurgence and growing secondary markets for startup founders, investors, and public market investors in the current economic cycle? Does this signal a sustainable tech market rebound or potential volatility?With ethical concerns rising within companies like Apple and voices like Vinod Khosla warning of AI’s societal risks, what governance or regulatory frameworks should be prioritized to ensure safe and equitable AI development?How do changes in social media dynamics—such as the rise of decentralized platforms like Bluesky and the transformation of X under Musk—impact political communication and community building in the digital age?What does the evolution of voice AI and UI convergence (e.g., iPadOS blending with macOS, ‘vibe coding’ tools) mean for how individuals will interact with technology in the near future? Could these trends reduce technical barriers or introduce new challenges?Closing SegmentTechCrunch’s 20-year journey exemplifies the power of dedicated media to build ecosystems and influence innovation rhythms. As we stand on the threshold of AI-driven transformation, the themes resonate: human connection remains central even as machines advance; technology for good requires intention amid rapid change; and markets and devices evolve to meet new realities while grappling with legacy and complexity.Our final thought: The future will not be defined solely by the most advanced algorithms or sleekest designs, but by how well the industry balances innovation, ethics, human values, and global inclusion to craft a truly transformative technology landscape. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.thatwastheweek.com/subscribe

How can societies balance the undeniable economic benefits of AI-driven abundance with the urgent need to address political and social inequalities in distribution?Given the increasing capital intensity and funding polarization in AI startups, what strategies should founders adopt to succeed in this evolving venture capital landscape?What are the ethical and legal implications of AI companies using user-generated online content without explicit consent, and how might this shape AI development and regulation?How does the increasing integration of major tech companies with military and government agencies affect public trust and innovation trajectories?What role should governments and regulators play in managing the power of tech giants, especially in light of ongoing antitrust cases and geopolitical economic pressures?To what extent can emerging social media platforms like Bluesky reshape the media ecosystem, given the persistent dominance of entrenched networks such as X?How might educational institutions integrate AI tools ethically and effectively to enhance learning without fostering overreliance or academic dishonesty? This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.thatwastheweek.com/subscribe

Show Notes: What is Abundance? And is it a Good Thing?OverviewThis newsletter explores the concept of abundance, particularly in the context of technology, energy, and capital. It challenges debates about whether abundance is real or manageable, presenting it instead as an unstoppable force rapidly reshaping business, society, and governance. The content spans trends from explosive AI-driven startup growth and energy breakthroughs to shifts in media, venture capital, and political dynamics.Listeners will find this collection compelling because it connects broad macro forces—technology advances, energy cost collapses, investment flows—with societal and economic changes. It offers a nuanced view that acknowledges friction and obstacles but maintains optimism that abundance is already here, accelerating, and fundamentally altering the rules across multiple domains.Key Trend 1: Explosive Growth and Changing Dynamics in AI-Driven Innovation and FundingThe emergence of AI as a multiplier of human capability is driving unprecedented revenue growth in startups, reshaping the venture capital landscape, and redefining what “scale” means. Late-stage funding surges and monumental investments in AI infrastructure reflect growing confidence in AI’s commercial potential.The top 10% of B2B AI startups are achieving astronomic 236% ARR growth, marking a departure from the efficiency-first era to rapid expansion and capturing “escape velocity.”The size of top-1% venture-backed exits is nearly doubling every five years, signaling massive future capital returns at the intersection of cloud, mobile, and AI platforms.Late-stage AI investments dominate funding, including mega rounds like Anthropic’s $3.5B Series E, underscoring belief in scalability and profitability.Oracle’s $40B commitment to Nvidia chips for OpenAI’s new data center exemplifies the scale of capital pouring into AI infrastructure needed to power trillion-parameter models.The explosion of AI integration across tools, like Perplexity Labs generating complex work products or multiple AI agents collaborating on code, highlights multi-layered adoption in workflows.Key Trend 2: Energy Abundance as the Prerequisite for Sustainable Technological and Societal GrowthEnergy is the foundational “subsidy” enabling societal complexity, climate action, and advanced AI. Rapid advances in solar, nuclear, and fusion research herald a future of "energy too cheap to meter," which will be a game changer as demand explodes.Energy breakthroughs have historically powered leaps in human development—from fire to fossil fuels—and solar energy is the latest, with plummeting costs creating a tipping point.Government reform, grid modernization, and deregulation are essential to accelerate adoption and infrastructure buildout.Upcoming nuclear small modular reactors and fusion research (including AI-assisted catalyst discovery) represent critical next steps along the energy innovation trajectory.Meeting urgent energy needs is critical for climate solutions, AI’s soaring compute demand, and sustaining democratic institutions and economies.Key Trend 3: Institutional Friction vs. Market-Led Speed and Execution in Technology and GovernmentWhile abundance forces press forward, friction remains, especially rooted in institutional inertia, regulatory complexity, and political coalitions. However, the market champions the agile and fast-executing players who prioritize speed over bureaucratic safety.Biden administration’s infrastructure rollout illustrates government slowness: trillions in spending with slow or no visible results.Companies achieving rapid ARR growth routinely bypass “progressive coalition politics” favoring execution and iteration.Elon Musk’s brief tenure in government showed the challenges of applying private-sector efficiency models to public institutions, ending with his resignation amid political conflicts.The “Abundance Agenda” calls for governance reform but faces entrenched interest-group resistance, reflecting recurring liberal factional rivalries.Key Trend 4: The Shifting Media and Information Ecosystem—From Screening to Summarizing, and the Challenge to Web ContentAI-powered search is transforming how users access information, moving from link-based discovery to AI-generated summaries that threaten traditional web traffic patterns and publisher revenue models.Google’s AI Overviews and AI Mode prioritize summarization over link retrieval, reducing user clicks to websites, shifting how “the web” is monetized and accessed.This shift generates tensions as content creators face reduced traffic even as their editorial authority becomes more valuable to AI training.New protocols like Microsoft’s NLWeb aim to make websites more accessible to AI agents, signaling an evolution toward AI-powered conversational interfaces.Publishers’ survival depends increasingly on establishing verified fact-based content and new business models compensating their data contribution to AI.Key Trend 5: The Democratization and Accelerated Meme-ification of Venture Capital and CultureThe VC landscape, and culture at large, is increasingly shaped by rapid narrative cycles, social media algorithms, and AI-driven content creation, emphasizing hype and viral content while paradoxically increasing the importance of genuine personal connection and location.Meme coins like Fartcoin, driven by AI-generated hype, show how narrative velocity can create rapid yet often ephemeral market spikes influencing capital flows.Social media and AI have democratized “taste,” with rapid cycles of trend formation driven by platform algorithms favoring engagement over depth.Venture capital branding is evolving to embrace memes and out-of-home advertising to reach broader retail investor audiences, challenging traditional LP communication.Despite hyper-meme culture, location and face-to-face networks remain crucial as a grounding force amid accelerated, digital-first trends. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.thatwastheweek.com/subscribe

A Year Just Happened in a WeekOverviewThis newsletter issue captures an extraordinary acceleration in technological innovation within an especially intense week, focusing on the broad and deep impact of AI across industries and devices. Listeners get a front-row seat to seismic shifts at major AI players—Google, Anthropic, OpenAI—and how their breakthroughs and strategic maneuvers are reshaping software, hardware, venture capital, productivity, and ethics.What makes this collection compelling is its exploration of AI’s layered disruption—from Google's AI-powered reimagining of search and productivity tools, Anthropic’s record-breaking AI assistant capable of deep autonomous work, to OpenAI’s audacious entry into consumer hardware design with Apple’s design luminary Jony Ive. The newsletter also provides reflections on startup funding trends, evolving AI workplace mandates, and foundational debates over AI’s ethical architecture and future ecosystem. Together, these pieces sketch a vivid snapshot of an inflection point in AI where technology, business models, and societal stakes intertwine.Key TrendsKey Trend 1: The AI Technology Leap — From Advanced Models to New Product ParadigmsAI development is surging at unprecedented pace, not just in capability but in practical integration across applications and devices. The focus is shifting from conceptual AI to usable, extended-duration, agentic assistants deeply embedded in daily workflows and consumer products.Significance: This trend reflects AI moving beyond isolated bursts of insight or simple chat interfaces to sustained, autonomous collaboration with users, spanning complex reasoning, coding, multi-modal inputs, and tool integrations. This lays the foundation for redefining productivity, creativity, and user experience in the AI era.Key Trend 2: Strategic Hardware Plays and the Battle Beyond SoftwareOpenAI’s multi-billion-dollar acquisition of Jony Ive’s startup signals a strategic pivot into hardware—building new AI companion devices designed to transcend conventional screens and possibly displace smartphones. At the same time, Google pushes integrated AI experiences centered on search and productivity on existing platforms.Significance: This trend shifts AI competition into physical devices and operating environments, creating new battlegrounds involving design innovation and consumer ownership models, with potentially profound effects on user habits and ecosystem dynamics.Key Trend 3: Venture Capital Evolution in the AI and Tech LandscapeFunding trends reveal concentrated capital flows into AI, with Series B rounds showing volatility but an overarching pivot toward efficiency, profitability, and selective aggressive capital deployment. Seed investing scales with new playbooks supporting early founder engagement and dynamic portfolio strategies.Significance: This trend highlights the ongoing maturation and transformation of venture capital amid AI’s rise, balancing risk, returns, and market realities, while exploring creative financing strategies crossing over traditional VC and private equity models.Key Trend 4: Workplace Transformation and AI-Driven ExpectationsLeading companies mandate widespread AI adoption to boost productivity, heighten efficiency, and reshape employee roles. Executives issue candid warnings on AI’s impact on jobs while simultaneously emphasizing the opportunity to master AI tools or face obsolescence.Significance: This trend underscores the sociological and managerial upheaval driven by AI in the workforce, where adoption is non-negotiable and where AI influences morale, workflows, and corporate culture at a fundamental level.Key Trend 5: Calls for an Open, Protocol-Based AI Ecosystem vs. Concentration of PowerThere is growing advocacy for “an architecture of participation”—a decentralized, interoperable AI ecosystem fueled by open protocols and multi-agent cooperation—to avoid premature monopolization by dominant platforms. Yet, industry maneuvers reveal increasingly concentrated power among a few mega players.Significance: This sets the stage for an ideological and practical contest over the future of AI infrastructure: will it foster broad innovation and cooperation or become locked under monopolistic control? The ultimate shape of AI’s ecosystem has huge technological, economic, and ethical implications.Talking Points for Each TrendTrend 1: The AI Technology LeapTalking Point 1: Anthropic’s Claude Opus 4 demonstrated sustained 7-hour autonomous coding and set new benchmarks (72.5% on SWE-Bench), reflecting AI’s step from quick interactions to deep, continuous collaboration.> “Anthropic is reshaping the landscape... pushing the boundaries of what machines can achieve in creative and technical collaboration over sustained periods.” (VentureBeat)Talking Point 2: Google’s Gemini 2.5 Pro introduces ‘Deep Think’ mode for complex multi-hypothesis reasoning, advancing AI’s understanding and problem-solving in dynamic environments.> “Gemini 2.5 Pro... features an enhanced reasoning mode called 'Deep Think', evaluating multiple possible answers before responding.” (VentureBeat)Trend 2: Strategic Hardware PlaysTalking Point 1: OpenAI’s acquisition of Jony Ive’s startup io ($6.5B) marks their largest deal, signaling a major move into “physical AI embodiments” with devices aiming to reduce screen dependence and potentially challenge Apple’s dominance.> “They are working on a new device... fully aware of a user’s surroundings... designed as a third core device alongside MacBook and iPhone.” (Reuters)Talking Point 2: Google, while heavily AI-centric, remains focused on embedding AI in software and services (Search, NotebookLM mobile, AI Overviews), reinforcing software ecosystems but facing competition on the device front.> “Google launched AI Mode... a 'total reimagining of search'... while rolling out NotebookLM mobile for on-the-go AI productivity.” (FT.com)Trend 3: Venture Capital EvolutionTalking Point 1: AI has grabbed roughly one-third of global venture capital ($100B+ in 2024), showing AI’s outsized role in funding flows amid overall tightening of Series B round sizes.> “AI sector dominated global venture funding, doubling from $55.6 billion to over $100 billion in 2024.” (vccafe.com)Talking Point 2: Seed-stage investing is scaling with firms like BoxGroup emphasizing early believer status and collaborative partnerships to back startups through various growth phases.> “BoxGroup makes 40 seed investments annually... focuses on supporting founders without dominating ownership or boards.” (TwentyMinuteVC)Trend 4: Workplace TransformationTalking Point 1: Shopify’s CEO Tobi Lutke mandates AI proficiency, linking job security to AI adoption and productivity boosts, signaling new workplace norms amid AI anxiety.> “Before asking more headcount... teams must demonstrate why tasks can’t be done via AI.” (NYMag)Talking Point 2: Fiverr’s CEO issued stark warnings about AI threat to jobs, urging employees to master AI tools or risk professional irrelevance.> “AI is coming for your jobs... You are expected to do more, faster, and better. If you don’t, your value will decrease.” (NYMag)Trend 5: Open Ecosystem vs Concentration of PowerTalking Point 1: Tim O’Reilly and others advocate for protocol-based AI ecosystems (Anthropic’s MCP, Google’s A2A, Microsoft’s NLWeb) fostering interoperability and distributed innovation, echoing open Internet ideals.> “Participatory markets are innovative markets... solutions can come from everywhere, not just from a dominant monopolist.” (O’Reilly)Talking Point 2: Despite open ideals, dominant players like OpenAI, Google, and Anthropic are actively building controlling ecosystems and platforms—OpenAI’s language of “operating system” and multi-billion-dollar acquisitions hint at winner-takes-most dynamics.> “It’s hard not to feel we are witnessing aggressive maneuvers... pursuing a winner-takes-most opportunity.” (Newsletter Editorial)Discussion QuestionsHow will the shift from AI as a tool to AI as an autonomous collaborator change the nature of work and productivity across sectors?What are the implications of OpenAI entering the hardware space with design leadership from Jony Ive? Can this challenge entrenched tech giants like Apple and Google?Considering venture capital trends, how might the concentration of funding in AI affect startup diversity and innovation outside the AI sector?Are the workplace mandates for AI adoption sustainable, or do they risk damaging employee morale and creativity? How should companies balance AI integration with human factors?What are the pros and cons of pursuing an open AI ecosystem based on cooperative protocols versus the reality of platform dominance by a few major players?To what extent could OpenAI’s and Google’s competition reflect the longstanding tech ecosystem rivalry between integrated and modular approaches, and what does that mean for consumers?With OpenAI aggressively building an ecosystem and platform, how might regulators or policymakers respond to ensure competitive, ethical AI development?Closing SegmentThis week crystallized a pivotal inflection point—a "Great Leap Forward" in AI’s maturity and reach. We’ve seen models like Claude Opus 4 and Google’s Gemini 2.5 evolve into sophisticated, sustained collaborators capable of seamlessly integrating into human workflows and devices. At the same time, strategic moves—especially OpenAI’s multi-billion-dollar hardware acquisition—signal a new battleground beyond software into hardware innovation and consumer experience design.The venture capital landscape is adapting rapidly with concentrated AI funding and evolving seed strategies spotlighting early founder support, all while workplace cultures grapple with AI-driven mandates that challenge traditional roles and morale.Beneath these shifts lies an ideological tug-of-war over AI’s future architecture—whether it will be governed as an open, participatory ecosystem enabling bro

This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.thatwastheweek.com/subscribe

This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.thatwastheweek.com/subscribe

Show Notes: Patterns in the ChaosDon't be a VictimMay 2, 2025Show Notes: Patterns in the ChaosOverviewThis week’s newsletter “Patterns in the Chaos” explores how individuals and organizations can move from passive observers to active agents of change. In an era defined by conflicting signals—tech giants under legal fire, explosive AI adoption, and lofty visions of abundance—the key is to identify cross-cutting patterns and choose which future you want to build.Our content spans court battles over monopolies, the rise of agentic AI in enterprises, evolving capital markets, debates over human value in an automated world, and strategic adaptation in turbulent times. Listeners will gain insights into how these forces interconnect and shape the next chapter of technology, policy, and society.Key Trend 1: The Rise of Agentic AI in EnterpriseSignificance: AI is shifting from a research topic to a mission-critical operational layer. Organizations are embedding autonomous software agents into R&D, go-to-market, and everyday workflows.Talking Point 1: Widespread Agentic AI AdoptionTalking Point 1: Nine in ten R&D teams plan to implement agentic AI this year, signaling a transition from experimentation to production.“91% of R&D Respondents have implemented or are planning to implement agentic AI” (David Poole, Georgian & NewtonX – https://georgian.io/agentic-ai-adoption-insights-from-600-executives/)This momentum underscores that AI planning, reasoning, and execution capabilities are now viewed as essential for competitive R&D.Talking Point 2: Re-Architecting Infrastructure for AgentsTalking Point 2: Companies are considering the web browser as the next OS-level platform for AI agents.“We need to build an OS-level agent, and a browser is essentially a containerized operating system” (Aravind Srinivas, on Spyglass – https://spyglass.org/ai-web-browser/)To avoid catastrophic misinterpretations, teams must also cultivate robust semantic layers that feed agents the context they lack.“Teams will become cultivators of a constantly evolving collection of cross-domain semantic layers” (Tom Tunguz – https://www.tomtunguz.com/semantic-layer/)Key Trend 2: Monopoly, Regulation, and the Future of Tech PowerSignificance: High-stakes antitrust actions against Apple and Google are not just about market share today but about control over tomorrow’s AI and distribution channels.Talking Point 1: Google’s Default Search Under ScrutinyTalking Point 1: The Justice Department warns that Google's exclusive search deals could “supercharge” its AI rollout and foreclose competition.“Default placement … could be leveraged to ‘supercharge’ new AI offerings, ensuring consumers turn first to Google” (David McCabe, NYT – https://www.nytimes.com/2025/05/01/technology/google-antitrust-trial-ai.html)Regulators argue that data-fuel advantages from search defaults give Google an unfair head start in AI services.Talking Point 2: Apple’s App Store DefianceTalking Point 2: A federal judge referred Apple to criminal prosecutors for ignoring a 2021 injunction on App Store anti-steering rules.“Apple’s goal: to dissuade customer usage of alternative purchase opportunities and maintain its anticompetitive revenue stream” (John Gruber, Daring Fireball – https://daringfireball.net/2025/04/gonzales_rogers_apple_app_store_ruling)This decision forces platforms to reassess how they enforce fees and policies, with potential global ripples.Key Trend 3: Capital Markets Evolution—New Funding Paths and VC ModelsSignificance: Funding mechanisms are diversifying—SPACs are back, VC firms are retooling their partnership models, and founders must navigate changing incentives at each stage.Talking Point 1: SPACs Make a ComebackTalking Point 1: After a rocky 2021, blank-check companies are targeting sectors from autonomous trucking to nuclear power.“Kodiak Robotics announced a SPAC merger at a pre-money valuation around $2.5 billion” (Joanna Glasner, Crunchbase – https://news.crunchbase.com/public/spac/tariffs-ai-robotics-crypto-biotech/)Startups see SPACs as a viable alternative to traditional IPOs, attracting capital at scale.Talking Point 2: VC as a “Full-Stack” Support PlatformTalking Point 2: Andreessen Horowitz redefined venture capital by building specialized teams (talent, marketing, regulatory) around each investment.“Founders deserve more than just capital, but a comprehensive, long-term support system” (a16z – https://www.youtube.com/watch?v=qpBDB2NjaWY)This contrasts with multi-stage funds whose “pipeline” approach can inflate early valuations without seed-stage expertise (Taavet Hinrikus, 20VC – https://www.youtube.com/watch?v=RvHnRxKdg2M).Key Trend 4: Human Agency, Abundance, and the Value of PeopleSignificance: As automation and AI proliferate, human skills, relationships, and democratic participation become scarce—and thus, strategic—assets.Talking Point 1: From Scarcity to Abundance Requires Purposeful PolicyTalking Point 1: Ezra Klein’s “Abundance” review criticizes timid calls for incremental reform and urges a democratic blueprint for real abundance.“This is the real discussion we need to be having: how can we achieve the disruptive level of change required for an actual abundance agenda in a democratic fashion.” (Albert Wenger – https://paragraph.com/@continuations/abundance-book-review)Bold policy and collective agency are essential to realize unlimited access to what humans need.Talking Point 2: Human Interaction as a Luxury in the Digital EraTalking Point 2: AI companions and curated human engagement are emerging as valuable services for the socially isolated.“New technologies often feel dystopian, until they feel commonplace … AI friends are a good thing, actually.” (Rex Woodbury – https://www.digitalnative.tech/p/ai-friends-are-a-good-thing-actually)“Human engagement is often reserved for those who can afford it, leading to a society where personal attention is a luxury.” (Humans As Luxury Goods – https://platforms.substack.com/p/humans-as-luxury-goods-in-the-age)Key Trend 5: Strategic Adaptation Amidst ChaosSignificance: Periods of upheaval create openings for startups and organizations that can navigate uncertainty, discipline pilots toward production, and rethink industrial strategy.Talking Point 1: Chaos as a Catalyst for Creative DestructionTalking Point 1: Startups thrive as “tricksters” in turbulent times, deploying architectural innovation to topple incumbents.“Periods of upheaval … create opportunities for startups to disrupt entrenched incumbents.”(Packy McCormick – https://www.notboring.co/p/chaos-is-a-ladder)Embracing uncertainty can be a deliberate strategy for reinvention.Talking Point 2: Overcoming “Death by 1,000 Pilots”Talking Point 2: The real challenge is not launching PoCs but scaling them into production with robust infrastructure, monitoring, and operations.“It’s easy to fire up a pilot … you can get stuck in this ‘death by 1,000 pilots’ approach.” (Rodney Zemmel, McKinsey Digital, via O’Reilly – https://www.oreilly.com/radar/death-by-1000-pilots/)Companies must build clear paths from experimentation to value realization.Discussion QuestionsHow can enterprises avoid “death by 1,000 pilots” while rapidly scaling agentic AI capabilities?What are the trade-offs between antitrust interventions (e.g., breaking up defaults) and the risk of stifling AI innovation?In a world where human interaction becomes a luxury, how should companies balance automation with services that emphasize personal touch?Can SPACs and “full-stack” VC platforms coexist, or will one model dominate early-stage funding in the next cycle?Given the tension between incremental regulatory fixes and calls for disruptive abundance agendas, what level of policy boldness is both feasible and desirable?How might a forced sale of a browser or default search slot reshape the competitive dynamics of AI distribution? (Controversy: divestiture as remedy)Are hyper-optimistic growth projections (e.g., OpenAI’s $129 billion by 2029) fantasy, or do they play a functional role in mobilizing capital? (Controversy: realism vs. hype)Closing SegmentAcross regulation, capital, technology and human value, one pattern emerges: agency matters. Whether you’re a founder, policy-maker, or individual, inertial forces abound—but so do levers for change. Embracing agentic AI, reshaping capital structures, demanding bold policy, and elevating human skills can turn chaos into opportunity. As we close, remember: “History is decided by human decisions taken in real time.” What decision will you make today to shape tomorrow? This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.thatwastheweek.com/subscribe

Show Notes: Venture Blues: Cloud, Silver LiningOverviewThis week’s “Venture Blues” editorial brings into focus a brewing transformation in early-stage venture capital. As funds endure stretched timelines and mounting LP pressure, long-taboo secondary markets are stepping into the limelight. At the same time, traditional VC structures—anchored to power-law home runs and decade-long illiquidity—are under fresh scrutiny.What makes this collection compelling is its blend of on-the-ground investor testimony (from Dan Gray, Hunter Walk, Rob Hodgkinson) and hard data (Carta charts, Series B MOIC trends) that together sketch a venture asset class at a crossroads: can it engineer better liquidity and more dependable returns without sacrificing outsized upside?Key Trend 1: The Liquidity Imperative and Rise of SecondariesAs portfolio companies stall in late-stage rounds, early-stage VCs and LPs alike are waking up to the need for earlier liquidity—and rediscovering secondaries.Why it matters:– Stigma around selling GP stakes is eroding when 10-year fund cycles stretch toward 15 years.– Liquidity becomes critical to meet IRR targets and redeploy capital.Talking Point 1: From Taboo to ToolboxQuote:“The obvious desperation for liquidity has — for now — removed the stigma associated with secondaries.”— Dan Gray’s X postEarly-stage managers, once loath to let shares go, now view secondaries as a legitimate value-preservation tactic.Removing psychological barriers makes secondaries a core liquidity channel, not just a last-resort option.Talking Point 2: Fund Cycles Stretch, LP Calculations ShiftQuote:“For the earliest funds (pre-seed, seed) this means instead of 10 year fund cycles for LPs, you’re seeing closer to 15, which fundamentally changes LP calculations about the asset class.”— Hunter Walk, HomebrewLonger holding periods erode IRRs and cash-on-cash returns.LPs factor in delayed distributions, pressing GPs to surface secondary opportunities sooner.Key Trend 2: Structural Challenges in Traditional VC ModelsDespite aggregate Series B investments growing 476% over eight years, most value remains on paper—and out of reach.Why it matters:– Healthy MOIC doesn’t equate to real cash returns.– Most LPs lack access to top-performing funds and can’t live off latent value.Talking Point 1: MOIC vs. Cash—The Distribution DilemmaQuote:“And the 4.76x is measured in MOIC, not cash, so was not distributed.”— Venture Blues editorialVenture’s celebrated power law produces massive paper returns skewed toward a handful of winners.Without distributions, LPs can’t recycle gains, creating a false sense of asset-class health.Talking Point 2: Concentration of Compelling ManagersQuote:“Most LPs do not get returns, and certainly not liquid returns (the only real kind).”— Venture Blues editorialA small club of star GPs capture most performance.Broader LP community remains exposed to illiquidity without average outcome participation.Key Trend 3: Rethinking the LP Base and Investor AlignmentEconomic uncertainty is forcing a recalibration of who backs VC—and how.Why it matters:– Traditional LPs (endowments, pensions) face funding pressures.– New entrants (sovereign wealth, retail, alternatives platforms) demand different structures.Talking Point 1: Endowment Exodus to SecondariesQuote:“A harbinger of change is Yale, who pioneered the ‘endowment model’… selling $6 bn in its PE portfolio in secondaries for the first time.”— Rob HodgkinsonEndowments under the gun from taxes, tariff impacts and political hostility.Liquid strategies gain priority, reshaping demand for evergreen and secondary vehicles.Talking Point 2: LP Preferences Shape Fund ProductsQuote:“VC is changing. Venture firms need to rethink not just who they raise from, but how their LP base influences what they’re offering.”— Rob HodgkinsonA move toward evergreen, co-invest, direct, and secondary funds rather than classic 10-year vehicles.Funds must tailor structures to new LP appetites for liquidity and risk profiles.Key Trend 4: Emerging Structures for De-Risked, Liquid VC InvestmentsAlgorithmic selection and private-company indexes promise to lower risk, broaden access and embed liquidity.Why it matters:– De-couples returns from a small set of GPs and rare unicorns.– Creates tradable vehicles for average VC outcomes.Talking Point 1: Filtering the 7% That MatterQuote:“Investing in this 7% as an index gives investors the ability to participate in de-risked average outcomes.”— Venture Blues editorialData and machine learning reject 93% of Series B rounds.The top 7% deliver 6.2x MOIC in five years, enabling an index tilted for performance.Talking Point 2: Liquidity by DesignQuote:“There is no longer a dependency on which fund an LP can invest in… And liquidity is built into the index approach.”— Venture Blues editorialIndex shares can be bought and sold once listed on public markets.Retail investors and non-traditional allocators gain direct VC exposure.Discussion QuestionsHow has the elongation of fund cycles from 10 to 15 years altered LPs’ appetite for early-stage VC?Can the rise of secondaries truly resolve liquidity challenges, or does it merely shift them to later rounds?With secondaries becoming “primary” for early-stage VCs, is there a risk of misaligned incentives between GPs and founders?How might new LP entrants (retail platforms, sovereign wealth funds) reshape venture fundraising and governance?Is algorithmic selection and index-based investing a silver bullet for de-risking VC, or does it introduce new systemic biases?Is the core issue in venture the lack of liquidity or the inherent power-law structure forcing “home runs”?What unintended consequences could emerge from tradable private-company indexes?Closing SegmentVenture Blues reveals an asset class in flux: the thirst for liquidity is rewriting norms, LPs are demanding new structures, and data-driven models offer a glimpse at more equitable, de-risked returns. As we watch secondaries soar and index products emerge, the central question remains: can VC evolve beyond its 70-year blueprint to deliver both outsized growth and true liquidity?Final thought: the silver lining in today’s venture clouds may be a fundamentally redesigned asset class that finally brings average, liquid outcomes within reach.Stay tuned as we track which of these trends will reshape the venture landscape for good. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.thatwastheweek.com/subscribe

Show Notes: Are Google and Meta Screwed?And does it make any Sense?April 18, 2025OverviewThis week’s newsletter delves into a pivotal moment for two of tech’s biggest players: Meta and Google. Long dominant through strategic acquisitions and platform control, both are now under intense legal scrutiny. At the same time, a new platform shift—centered on AI—threatens to upend their business models, just as geopolitical forces reshape global markets.Listeners will gain insight into how antitrust battles, legacy acquisitions, AI innovation, and trade wars intersect to challenge the future of these giants. We’ll connect disparate articles to reveal patterns that go beyond individual stories.Key TrendsKey Trend 1: Government Antitrust Pressure and Legal BattlesSignificance: After decades of unchecked growth, Meta and Google face unprecedented antitrust scrutiny. The outcomes could reset the rules for digital markets—and determine whether breakups or massive fines become the norm.Talking Point 1: Meta’s High-Stakes Trial“In a just world, the FTC has no shot to win this case. The case is so nebulous and weak…”– M.G. Siegler, “The Meta Points of Meta’s Trial” (https://spyglass.org/meta-trial/)• Highlights the FTC’s challenge: litigating past acquisitions with vague theories of harm.Talking Point 2: Google Guilty in Ad Tech Monopoly“A judge ruled that Google holds a monopolistic position in the technology of online advertising, unfairly harming rivals and advertisers.”– David McCabe, New York Times(https://www.nytimes.com/2025/04/17/technology/google-ad-tech-antitrust-ruling.html)• Marks the second major U.S. court loss for Google in under a year, setting the stage for structural remedies.Key Trend 2: Strategic Platform Shifts and Legacy AcquisitionsSignificance: Meta’s survival has hinged on buying Instagram and WhatsApp; now those very deals are under fire. The pattern echoes past shifts—desktop to mobile—and underscores how acquisitions can both secure and imperil platform relevance.Talking Point 1: The Value and Vulnerability of Instagram“Without Instagram, Meta is screwed.”– M.G. Siegler, Spyglass (https://spyglass.org/without-instagram-meta-is-screwed/)• Shows Instagram’s ad revenue underpins Meta’s funding for new bets (metaverse, AI).Talking Point 2: Echoes of the Mobile Battle“Facebook 2.0 will try to kill Facebook 1.0 and Google 2.0 will try to kill Google 1.0.”– Editorial, “Are Google and Meta Screwed?” (Newsletter for April 11, 2025)• Reminds us how prior platform shifts demanded reinvention—AI may require the same.Key Trend 3: AI‑Driven Disruption and the Next PlatformsSignificance: Just as mobile upended desktop, AI is redrawing the map of search, discovery, and social engagement. Meta and Google must adapt to challengers like OpenAI, Anthropic, xAI and novel features such as memory and reasoning.Talking Point 1: AI Search and Discovery Race“OpenAI, Anthropic, Perplexity, and Grok capture users for AI based search and discovery.”– Editorial, “Are Google and Meta Screwed?”• Signals user migration away from traditional search and feeds.Talking Point 2: The AI Price War and Memory Features“OpenAI slashes prices for GPT‑4.1 by up to 75%, igniting an AI price war among tech giants.”– Bryson Masse, VentureBeat (https://venturebeat.com/ai/gpt-4-1-ai-price-war-developers/)“Claude’s memory feature … allows the chatbot to recall details from previous interactions.”– Michael Nuñez, VentureBeat(https://venturebeat.com/ai/claude-just-gained-superpowers-anthropics-ai-can-now-search-your-entire-google-workspace-without-you/)• Underscores how product feature arms races could outflank legacy ad models.Key Trend 4: Global Economic Realignments and Trade WarsSignificance: Tech doesn’t operate in a vacuum. Tariffs and nationalism are reshaping supply chains and consumer behavior, with knock‑on effects for digital giants reliant on ad dollars and global audiences.Talking Point 1: Tariffs as a “Tectonic Plate Shift”“Trump’s tariffs are part of a broader movement in the global economy which he describes as a ‘tectonic plate shift.’”– Peter R. Orszag, New York Times video (https://www.nytimes.com/video/opinion/100000010103488/trumps-tariffs-are-part-of-a-tectonic-plate-shift-in-the-global-economy.html)• Reflects how trade policy uncertainty seeps into tech investment and consumer prices.Talking Point 2: The End of Globalism vs Economic Globalization“Globalisation as we’ve known it for the past couple of decades has come to an end.”– Frank Furedi, Spiked (https://www.spiked-online.com/2025/04/15/the-end-of-globalism-is-nigh/)• Positions economic nationalism alongside persistent interdependence—tech firms must navigate both.Discussion QuestionsHow do the FTC’s and DOJ’s strategies against Meta and Google reflect a shift in government confidence and capability to regulate tech giants?Would breaking up Instagram and WhatsApp—or forcing Google to divest its ad tech—spur innovation or simply weaken platforms in an era of AI competition?In what ways has the shift from mobile to AI mirrored past platform transitions, and what lessons should Meta and Google apply as they pursue “2.0” strategies?Is the AI price war (GPT‑4.1 cuts, Claude memory, Grok features) a sustainable model for developers and businesses, or will it erode margins across the ecosystem?Do Trump’s tariffs and rising economic nationalism ultimately strengthen China’s tech incumbents (Huawei, Temu, Shein) more than they pressure U.S. companies? (Controversial)With visionaries like Jack Dorsey and Elon Musk calling to “delete all IP law,” how should tech firms balance creator rights against AI training needs? (Controversial) 7. How does the narrative of “the end of globalism” influence Big Tech’s investment in international expansion and localized product strategies?Closing IdeasMeta and Google stand at a crossroads: legal rulings threaten their core business structures while AI challengers redefine user engagement.Their historic playbook—acquiring emerging rivals and evolving ad models—now collides with fast‑moving technology, activist regulators, and geopolitical headwinds.Final Thought: Survival for these giants will depend on agility—embracing AI as the next platform, rethinking past acquisitions, and navigating a world where borders, both digital and national, are being redrawn.Generated on 4/18/2025 with Newsletter Creator This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.thatwastheweek.com/subscribe