David George

Gavin Baker has spent the summer asking every AI leader “Can you tell me one quantitative data point in your business that’s getting worse? Just one” — and through July and August, nobody could. AI broadly accelerated both months even as some AI names fell into significant drawdowns; the index calm is misleading because “you can drown crossing a river that’s on average two feet deep.” His caveat: Anthropic is in an IPO quiet period.

Both men reject zero-sum framing: this cycle is “not an or thing, it’s an and thing,” where frontier labs, open source, neoclouds, applications, and Nvidia all win. Every LP conversation starts with “How is this all gonna go wrong?”, but the supply-side data shows a roughly nine-to-ten-month payback for Nebius ($50B/gigawatt, 50-60% prepaid by customers), Blackstone/KKR/Apollo financing at low cost, and useful lives extending — true equity payback “might be way inside of a year.”

The demand side is “absolutely nowhere”: the companies’ roughly $180B of revenue rests on maybe sub-10 million heavy users against 1.5 billion knowledge workers. Baker’s fund Atreides grew internal token consumption 100x from March to August; some AI-native companies already spend 10%+ of human compensation on tokens versus ~1% at old-economy firms. Both worry more about undersupply than overbuild through ’28 — Dwarkesh’s scenario of token prices rising 10x is “the opposite direction of where everybody thinks this is gonna go.”

Public markets will have to digest lab revenue as a dial, not a stream: a lab monetizing 8 gigawatts of inference at ~$60B per gigawatt per year could cut revenue from $480B to $120B in this example by reallocating to training — “and I actually think they would do that.” Satya “blinked” on capex and regrets it; Dario chose bankruptcy-avoidance over share, “and OpenAI was aggressive, and now OpenAI is back in the game.”

Baker and George argue that the AI industry must tell its own truth: data centers are “probably the best thing that has ever happened to working-class Americans.” Town tax revenue “10Xs,” Loudoun County pairs America’s highest income with its highest data-center density, and cheap natgas ($2-3 vs ~$20-25 in Europe/Asia) is reindustrializing America — while Baker alleges “an organized CCP-funded campaign… laundered through TikTok” against data centers. His favorite Dario line: “stop talking about curing cancer and actually cure cancer.”

Orbital compute flips on Starship reusability: of $50B per gigawatt, ~$35B is IT either way, while the $15B of terrestrial power/cooling/labor is inflationary — and reusable launch takes the space alternative under $1B. Elon and Jensen have co-designed a Reuben rack targeted for a Q4 ’27 launch; even two quarters late, “that’s 2028,” and per Brad Gershner it’s “happening in plain sight.” Training stays on Earth — latency and speed of light are real.

The endgame is an ensemble of models behind routers, and the “arbiter of intelligence” abstraction layer is the most vied-for position “in the history of business.” Enterprises will RL open-source base models (soon likely Nvidia’s, via Nemotron and the Poolside acquisition) on their own data rather than hand context to frontier labs; Fireworks Nexus is the best instantiation today; Kirkland & Ellis’s $500M in-house build validates the category but understates the difficulty.

On Nvidia, Baker’s rule for semiconductor CEOs is “the only thing you should ever say is ‘Thank you, Jensen’” — while George’s rule of thumb is that every 1% of accelerator share is worth ~$100B, so plug into Jensen’s ecosystem rather than tugging on Superman’s cape. Baker estimates Jensen has locked up roughly 70-80% of the supply chain; his data centers are the most financeable ($15B equity on $50B), and George’s deal hierarchy reads true customer preference — equity investments beat RVGs beat token-priced warrants beat naked warrants.

🔗 Original source & video: Why AI Demand Is Outrunning Compute Supply

Stripe’s cohort data pushes against the software-commoditization thesis: first-half signups grew 50% YoY, the median 2026 cohort generates 50% more revenue than 2025’s (which was 70% above 2024’s), and the new SaaS-platform cohort is 103% larger despite the narrative that vertical platforms would struggle. Gaybrick says the commoditization thesis is “plausible” and that “we’re either in the singularity or creeping towards the singularity,” but “we are seeing the exact opposite right now”—more software created, monetized faster, with ElevenLabs using 14 Stripe products.

Stripe’s internal agent fleet, “Stripe Minions,” went from ~1,200 one-shot PRs per week at its January/February blog post to 7,000 last week—roughly 30% of all Stripe PRs—and Minion PR share is one of the company’s most important metrics. The design principle: “you’re not going to iterate, you’re not going into planning mode, you’re just going to say this is what I want, go do it.” The org answer is flatter, smaller teams: Stripe Projects was mostly built by a PM and one senior engineer in a few weeks, and that engineer now orchestrates 16 agents.

Gaybrick’s contrarian resource-allocation call: most companies treat agentic efficiency as opex reduction; Stripe’s belief is “I’m being a little cheeky, but build everything,” because “optimizing your cost structure is going short on your own future potential.” Evidence for Jevons over layoffs: internal AI tool KAI, built by two people, lifted seller productivity 20%—and the conclusion was “we need a lot more sellers”—while global tax filing shipped in a third of the time US filing took, at greater complexity.

Agentic commerce hasn’t had its “Claude Opus 4.5 Cambrian explosion moment” because primitives are missing—hence the machine payments protocol with Tempo, where a service returns a 402 response saying “here’s how you buy me,” and the Link agent wallet CLI atop 400 million Link users. Gaybrick thinks checkout pages could go away, at least in the best-case scenario, for humans as well as agents. The most exciting area is B2B: agents provisioning Vercel or Browserbase via Stripe Projects. David George says this already shapes a16z dev-tool theses: “assume the agents are going to be the shoppers.”

Micropayments, a failed idea since the dawn of the internet, may finally work because agents plus stablecoins remove the old friction. The old squeeze—per-article pricing loses to both subscriptions and ads—breaks when agents act as “little hummingbirds going around the internet just slurping up a little data here… doing a little compute over here” on ephemeral budgets (“your budget is $15, go”), without creating a monthly account for every service.

The stablecoin thesis is geopolitical plumbing: national rails like UPI (~86% of sub-$5 payments in India versus single-digit percentages on US cards) and Pix work locally, “but you need a Schelling point for the global economy,” which crypto rails could provide. Concretes: Stripe reaches around 60 countries in fiat but around 150 in stablecoins; Felix Pago is now between 5% and 10% of remittances along the US-Mexico corridor after a few years. Tempo aims to provide a payments-specific chain with first-class privacy, stable throughput and fees, with DoorDash among its collaborators.

George estimates Stripe is over $2 trillion in volume; Gaybrick sees tokens blurring into money. Attacks on general-purpose token consumers like Cursor and Replit are “very reminiscent of what we see in terms of people trying to steal money from Stripe users,” creating a mandate to make token↔dollar movement as safe as dollar↔euro. On fraud, Gaybrick thinks Cursor was Stripe’s first user in this area and says something like one in six free-trial users were abusive; Stripe’s signals now help ElevenLabs block 2,000 free-trial abusers daily.

The strategy in one line, via Clerk’s Colin: “win all the startups and then win them again”—startups are canaries for the next opportunity and hold Stripe to the highest standards (enterprise CSAT praises Stripe reporting; startups call it “garbage”), pulling Stripe toward nearly half the Fortune 500. Taste scales through top-down repetition, product use and simulation: PII-free, growth-randomized synthetic accounts with live-feeling disputes and refunds. George’s investor frame is that founders are the asset class that finds the next product areas; Gaybrick’s closing ambition is to “build the next Stripe inside of Stripe.”

🔗 Original source & video: “Checkout Pages Should Not Exist" - Stripe on Agentic Commerce

The scale prior changed in November: George says Anthropic and OpenAI already add more monthly revenue than Meta, Google, or Microsoft while AI diffusion across the real economy remains below 5%. Clark says he would not be surprised if the pair reaches a combined $200 billion revenue run rate by year-end—before open source and other vendors—framing this as roughly 10% of the Fortune 500’s collective $2 trillion profit pool. That cost pressure means open-source and local models may matter sooner because “cost is going to hit us in the face.”

Enterprise AI remains an operational redesign story, not merely a software-adoption story. Clark says strong companies currently direct their best people toward new products rather than automating internal work; George characterizes recent layoffs as “trimming of previous fat,” not demonstrated AI efficiency. The most cutting-edge people Clark has spoken with are still documenting workflows and capturing context. Native-AI companies offer the preview: lean teams whisper instructions into “swarms of agents,” with tooling moving from reactive assistance toward proactive engagement.

AI’s power law is steepening even as the identity of the winners becomes less predictable. Clark cites a top-1% exit threshold of $10 billion for 2020-24 and $20 billion after the February update covering 2025 and the first two months of 2026. George says recently closed exits have pushed it to $32 billion, potentially north of $100 billion by September if OpenAI and Anthropic enter the sample. Yet George notes that 40% of last year’s Forbes AI 50 dropped off this year’s list; Clark says the half-life of these companies feels incredibly short.

George’s current rule for application-layer winners is blunt: “you have to be in the token path.” Legacy-software budgets cannot absorb rapidly rising AI costs, while value capture depends on unknowables including frontier-model competition, open source, local inference, and smaller models. Five frontier labs would likely mean cheaper tokens than two, supporting a broader application ecosystem.

The cost-performance battle could overturn today’s frontier concentration. George reports that leading Chinese LLMs appear roughly six months behind US models but 10x cheaper—an “innovator’s dilemma” in which 80% of capability costs 10% of the price. Clark says frontier demand remains voracious, though optimization may arrive sooner than expected; distillation reportedly costs around 2% of pretraining, while like-for-like token costs are falling more than 10x annually.

Today’s low AI loss rates are not evidence that venture risk disappeared. George says their early-stage funds historically have a roughly 60% loss ratio, versus probably single-digit losses recently in AI, and says the “laws of gravity” will reassert themselves. Clark rejects low loss ratios as the objective and calls never losing money “a horrible data point”; George adds, “That’s a PE firm.” The Khosla Ventures philosophy is to back the leader in a promising market, because massive winners—not unusually safe portfolios—drive returns.

Clark is pretty confident AI is not presently a bubble, chiefly because supply remains scarce rather than excessive. Data-center capacity at scale is unavailable until late 2028 or early 2029, and US construction may already be a year behind expectations; the main reversal risk is an algorithmic breakthrough enabling radically smaller models. Against a possible $5 trillion buildout, Clark considers $1-2 trillion of revenue a reasonable return expectation, especially if OpenAI and Anthropic alone approach $200 billion this year.

🔗 Original source & video: The New Rule for Picking AI Winners | The a16z Show

AI-native demand is separating sharply from the rest of software. George says AI companies are growing more than 2.5x faster, with top performers at 693% year over year and the fastest reaching $100 million in revenue much sooner than SaaS predecessors. “AI demand is crazy,” yet the fastest-growing companies spend less—not more—on sales and marketing.

The strongest AI companies pair extraordinary growth with $500,000-$1 million of ARR per employee. That compares with a roughly $400,000 SaaS-era rule of thumb, while lower gross margins can be a “badge of honor” when high inference costs indicate customers are actually using AI features. George cautions that strong demand, lean staffing and general efficiency gains after the bloated 2021 era explain much of today’s efficiency; wholesale AI-driven organizational redesign remains early.

Pre-AI companies must “adapt to the AI era or die” across both products and internal operations. One founder gave two AI-fluent engineers unlimited access to Claude Code, Codex and Cursor; they rebuilt a product he was frustrated with at an estimated 10-20x faster pace, prompting him to rethink the product-and-engineering organization within 12 months. The extreme operating question is now: “Can I do it with electricity or do I need to do it with blood?”

Engagement data makes the best application revenue look durable rather than experimental. George says Harvey users spend roughly twice as much time in the product; Abridge maintained or increased engagement while rapidly adding clinicians; and Navan now handles 50% of travel interactions with AI, helping gross margins expand 20 percentage points over three years. George cites Flock as solving 700,000 crimes annually, with officers clearing almost 10% more where it operates.

Enterprise intent is running well ahead of implementation, creating a widening execution gap. Fortune 500 leaders say they must become AI companies, but George calls change management—not model readiness—the central constraint. Early results show the stakes: Chime cut support costs 60%, while Rocket Mortgage saved 1.1 million underwriting hours and reached $40 million of annual run-rate savings.

AI winners have produced almost 80% of the S&P 500’s return, but George sees earnings rather than speculative multiple expansion underneath the rally. Multiples are above average yet far below dot-com levels, and investors favor profitable growth over the loss-making growth rewarded in 2021. His durable factor remains growth: “Ultimately, growth is the biggest thing that drives returns over five to 10 years.”

The infrastructure buildout has bubbly features, but utilization and financing still differ materially from prior bubbles. Hyperscalers are supported largely by historically profitable companies and cash flows, seven- to eight-year-old Google TPUs remain fully utilized, and rental pricing for A100s and H100s has held up—hence the relayed line, “There are no dark GPUs.” The watchpoint is debt: Oracle is making a large, cash-flow-negative cloud bet, while its credit-default-swap cost has risen to roughly 2%.

The payback hurdle is enormous and may extend well beyond 2030, while private markets are now a major asset class. Against roughly $4.8 trillion of cumulative hyperscaler capex, annual AI revenue must approach $1 trillion by 2030—about 1% of global GDP—to clear a 10% hurdle rate; George’s rough current estimate is only $50 billion, albeit growing well above 100%. Meanwhile, about 86% of companies above $100 million in revenue remain private, and the ten largest North American and European unicorns hold almost 40% of a $5.5 trillion valuation pool.

🔗 Original source & video: AI Markets: Deep Dive with a16z’s David George

David George’s base case is that AI infrastructure is being financed by companies strong enough to absorb overbuild while the cost-performance curve compounds in application developers’ favor. Annualizing the latest quarter puts big-tech capex near $400 billion, “most of that” for AI infrastructure and data centers; model-access costs fell more than 99% in two years while frontier capability doubled every seven months. He expects AI to become “like electricity or Wi-Fi,” with large tech companies carrying much of the substrate cost.

The dot-com analogy breaks, in George’s view, because today’s capacity sits atop internet and cloud distribution and already has usage at global scale. ChatGPT reached 365 billion searches in two years versus Google’s 11, while George estimates 1.5–2 billion active AI users across products; the builders and tenants are stronger, though leverage routed through banks, private debt, and insurers remains worth watching. “It’s built on the back of the previous technology cycles.”

The addressable value pool is labor, not merely software: US software spend is about 1% of GDP versus white-collar payroll near 20%. George expects AI to exceed the roughly $10 trillion of value created by mobile and cloud, with perhaps 90% of value accruing to customers and 10% to vendors—still enough for enormous market caps. If completed work remains hard to measure and price, competitive forces will leave even more surplus with users.

Consumer AI may surprise on price before it surprises on reach. ChatGPT was described as having more than one billion monthly active users and 30–40 million paying users, versus perhaps two billion AI users overall; India pricing near $3–4 a month coexists with US premium products at $200–300. George thinks the P in P×Q has substantial runway because “there’s way more upside to monetize the base than there is risk of price pressure.”

Kha and George’s bottleneck view is that chip and infrastructure capacity should scale, but energy is likely the limiting factor for the next five years and cooling follows behind it. Kha points to nuclear, expects Three Mile Island to get powered back up, and cites West Texas natural gas; xAI’s speedrun required buying backup generators across a multistate region and labor off other projects. Kha’s next constraint is cooling the buildout “without boiling our oceans” or melting the chips.

For AI applications, George would accept temporarily weaker gross margins—but not weak product love. The underwriting hierarchy is 90% or more gross retention and easy customer acquisition ahead of current margin, conditional on multiple model suppliers driving inputs lower; GPT-5, Anthropic, and Gemini were cited as competitive pressure. Consumer products can be sticky, while raw developer APIs are “not very sticky” because switching can be one API call.

The investable high-growth frontier has migrated into private markets, making access and liquidity—not just selection—core return variables. Billion-dollar private companies total roughly $3.5 trillion versus $500 billion ten years ago, companies now remain private for about 14 years, and only around 5% of public software and internet names forecast growth above 25% for the next 12 months. a16z’s approach pairs “undeniable momentum” with unusually early bets on only the strongest research teams.

Incumbent software is vulnerable only where a startup can combine three breaks at once: reimagined UI/UX, a new data layer, and disruptive pricing. Salesforce is George’s example of an uninspiring front end attached to a sticky database; AI can shift software from keeping records to doing work, but he has not yet seen the killer dethroning idea. Near-term opportunities sit around systems of record rather than in wholesale replacement.

Beyond AI infrastructure and applications, George expects American Dynamism to be the next-largest area, with some AI-enabled health activity and crypto pursued alongside the crypto team. Stablecoin enablement could become more significant if that market takes off. The portfolio follows best ideas rather than a quota for new investments versus follow-ons, and George says a16z’s edge also comes from early-stage access plus market and product insights.

🔗 Original source & video: The Biggest Bottlenecks For AI: Energy & Cooling

The large-fund debate answered with data: a16z’s best-performing fund in firm history is a $1B fund — Databricks has returned 7x the fund and Coinbase 5x in DPI alone — and its larger funds have outperformed its smaller ones at similar multiples. The structural case: private market cap has grown 10x in 10 years to over $5T, and in a16z’s study of the top 50 IPOs from 2017–2025, 53% of the dollars of gain came at Series C or later. “It’s about the number of winners you capture.”

George claims public markets still offer a cheaper cost of capital; Harry’s pushback lands — likely Ramp and Lovable are “priced at the same price as Wix and Wix is doing two billion of profit” — and George’s honest non-answer is “we’re not close to those companies.” The real private-market benefit, per George: avoidance of stock-price volatility and employee management (Stripe, SpaceX, Databricks), not cheaper money.

The labor-to-tech budget shift has green shoots: CH Robinson, a truck brokerage, disclosed a 40% AI-driven productivity gain (shipments per person per day since end-2022) with operating margin up 680bps; Microsoft cut headcount 6%. Meanwhile public software is “guilty until proven innocent… assumed doomed by AI unless proven otherwise” — Harry notes that caps anything without a labor-replacement story (his Monday.com and Duolingo positions included).

AI revenue counts only with engagement behind it: you can’t see years of renewals at this speed, so a16z’s bar shifted to retention and engagement as leading indicators — and the inverse tell is now a screen: “if we ever see a company that pitches us as an AI company and they have SaaS gross margins, we ask a lot of questions” — it probably means nobody is using the AI features.

Kingmaking is a flimsy thesis. “If the investment thesis is our investment is going to make them a winner, it’s probably a pretty flimsy investment thesis.” Preferential attachment to the existing leader is real; SoftBank’s capital-as-a-weapon was “a bit of an adverse selection machine” — the companies that opt in are the ones without a reason to win, and the subsidy funnels back to Google and Facebook.

Models won’t eat the apps — a16z “fully changed our mind” 18–24 months ago; radiologist headcount rose even though AI read scans better, because scans are only 30–40% of the job. The model layer itself goes oligopolistic like cloud: he thinks ChatGPT will lead consumer, the B2B fight with Anthropic is the head-to-head — and Anthropic is George’s lingering error of omission.

The autonomous-driving investment was the biggest internal fight — George’s team modeled the 2020 price as too high; Mark and Ben overruled with “it’s autonomous driving… the endless market size,” and the compromise (small check, then a much larger one later) worked. Flow is strength-of-strengths underwriting on Adam (likely Neumann): rent is 30% of the average US renter’s disposable income and “the only unbranded experience in anyone’s life.” Robotics, where a16z has made no large investment yet, George thinks could be “the largest category in AI.”

🔗 Original source & video: a16z’s David George on the Most Controversial Bet at a16z & Do Margins and Revenue Matter in AI?

Consumer AI’s monetization ceiling is a myth, George argues, and history proves it: private-market investors once capped Facebook and Google at “$20 a user” — a decade later they make ~$200 per developed-world user. ChatGPT (likely) reached Google’s scale roughly 4x faster, has ~a billion users and monetizes fewer than 50 million of them; the interface will shift from a reactive chatbot to something “proactive… long-form memory… multimodal,” and the ad format that wins will be as unforeseeable as feed-based ads once were.

On enterprise AI he’s deliberately the skeptic: the “software is $400B but white-collar labor is huge” slides are “a little bit handwavy.” His base case — “90% of the technological surplus is going to go to the end users,” like the steam engine, which wasn’t priced based on replacing 50 laborers. The clearest business-model progress is in customer support (outcome pricing) and coding (consumption); everything else is “pretty TBD” — yet the next generation of enterprise winners can still be bigger than the last.

The episode’s core market call: growth above 30% is still not fully valued because it’s hard for investors to model persistence — 2009 consensus for Apple’s 2013 was off by 3x on “the most covered company in the world,” and a growth rate that decays 80→75→65 instead of 80→65→50→40 is “a 3x difference in your valuation.” His portfolio is growing 112% dollar-weighted at a 21x revenue entry — “way less risky than a 12% grower in PE at 15 times EBITDA.”

Most tech markets are Glengarry Glen Ross: “First prize gets Cadillac. Second prize gets a set of steak knives. Third prize, you’re fired.” The vast majority of market cap goes to the leader — “there’s no number two to Salesforce.” Exception: the model layer looks like cloud or aircraft manufacturing, not airlines — multiple players with profit pools. Being #2 in model revenue is fine; being #2 in the dominant consumer chat interface is not.

The likely Waymo trade is his formative lesson in overriding spreadsheets: in 2020 he resisted (“it’s going to take 10 years… the valuation is going to be really high”) and Mark and Ben said “Don’t care… this is the mother of all markets. Stop overthinking it.” A small 2020 check became a much larger one at end-2024 once “consumer preference slapped you in the face” — the likely Waymo overtook ~50,000 SF Bay Area Lyft drivers in market share with only ~400 cars. Robots, by contrast, face “endless degrees of freedom” — he thinks the robotics timeline is longer than expected.

Venture has quietly become a grown-up asset class: eight of the ten largest market caps are tech (seven West Coast venture-backed), private market cap is ~$5 trillion, up 10x in a decade — nearly a quarter of the S&P 500 and more than half of the Mag 7 — while public markets have shrunk by half in 20 years and fewer than five public software/consumer/fintech companies grow 30%. The scarce asset — high growth — now lives private.

How a16z grades AI companies: pull over push (“Is the market demanding more of your product?” is on a post-it on his monitor), durable engagement (Harvey usage step-changed when reasoning models landed — “lawyers need to reason”), and — inverted from the SaaS era — low gross margins as a badge of honor: “I’m an AI thing and I got 75% gross margins… no one’s using the AI stuff then.” Endgame maybe 50% margin businesses, not 80, but big enough not to matter.

🔗 Original source & video: How a16z Growth Invests

Gavin Baker’s answer is no: today’s AI buildout does not resemble the 2000 bubble by either valuation or utilization. Cisco peaked around 150–180 times trailing earnings versus roughly 40 times for NVIDIA; 97% of peak-era fiber was dark, while “there are no dark GPUs” and training clusters are pushing chips until they melt. The largest public GPU buyers have gained roughly 10 points of ROIC since ramping capex—though whether that persists through Blackwell spending remains “an interesting and open debate,” and Baker personally thinks it will.

The infrastructure bill is enormous, but its buyers have an unusually deep balance-sheet buffer. David George framed roughly $1 trillion of existing US data centers, another $3–4 trillion planned over five years, and estimated more than $1 trillion of OpenAI commitments; against that, the major spenders generate about $300 billion of annual free cash flow and hold $500 billion of cash. At $40–50 billion per NVIDIA-powered gigawatt, George sees “an $800 billion buffer growing $300 billion every year,” even if near-term buildout creates some mismatch.

The feared round-tripping is real but, in Baker’s view, small and strategically rational. NVIDIA funding OpenAI while OpenAI buys NVIDIA chips looks circular because “money is fungible,” but Baker says the real driver is competition with Google’s TPU, DeepMind and Gemini—not weak underlying demand. Baker estimates Gemini had taken roughly 15–20 points of traffic share in two or three months and suspects Google may already have more AI traffic than OpenAI or Anthropic on an actual-traffic basis; the cited share gain did not include AI Overviews.

AI could reinforce much of the Mag 7, but execution failure remains existential. Incumbents possess the essential inputs—data, distribution, compute, capital and talent—so AI might be a sustaining innovation if they execute; otherwise, “IBM might be a good fate.” David George called ChatGPT “Pearl Harbor for Google,” while Baker cautions that frontier labs will structurally carry lower gross margins than SaaS because scaling laws and test-time compute keep the products compute-intensive.

Application SaaS is not necessarily dead, but winning requires embracing margin compression. Baker has softened his early-2024 view that all application SaaS “might be a zero,” especially for vendors serving fragmented SMB customers; his warning is that protecting 80–90% gross margins can sacrifice the AI opportunity. The operative choice is “10 bucks of revenue with 90% gross margins or 50 bucks of revenue with 60%,” while incumbents can subsidize break-even AI products before leaders such as Cursor accumulate enough tokens to make catching up difficult.

Distribution and reasoning have made consumer AI less hostile to durable platforms. AI-browser launches could let Google watch the pioneers for three to six months before responding through Chrome’s roughly 5 billion users. Reasoning and RL can turn a large user base into the classic product-data flywheel: users improve the algorithm, which improves the product. David George says GPT-5 is not evidence that scaling laws ended because it was “a smaller model” designed to run more economically, not to maximize capability.

Outcome pricing is the likely business-model shift, with robotics as the physical extension of the same logic. Customer support can charge per resolved task because success supplies a verified reward; personal agents may collect affiliate fees for completed purchases, squeezing the advertiser overpayment that made Google search so lucrative. Baker calls robotics “very real,” expects Tesla versus China, and thinks the humanoid debate is effectively over because robots can learn from video or human demonstrations and receive clean task-level feedback.

🔗 Original source & video: “Is there an AI bubble?” Gavin Baker and David George