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Today’s Essential Episodes:
OpenAI CEO Sam Altman on Uncapped Compute Demand, OpenAI’s Own Chips, Speed Limits on RSI and Whether Today’s Models Are Conscious
Forward Future, with Matthew Berman and Sam Altman, CEO of OpenAIEric Schmidt on Anthropic’s Record Revenue Growth, AGI in Five to Ten Years and Why Europe Is Losing the AI Race
In Good Company, with Nicolai Tangen and Eric Schmidt, former CEO and chairman of GoogleMicrosoft CEO Satya Nadella on Treating AI Models as Insider Threats, Trailing the Frontier at a Fraction of the Cost and Succession
Future Proof from Semafor, with Reed Albergotti and Satya Nadella, chairman and CEO of MicrosoftGoogle CEO Sundar Pichai on Gemini 4 Argon’s Capacity Crunch, Going Free-Cash-Flow Negative and the DeepMind Reshuffle
Sources, with Alex Heath and Sundar Pichai, CEO of Google and AlphabetDaytona CEO Ivan Burazin on the CPU Shortage AI Agents Will Cause, 25x Revenue Growth and a $60M Series B at a $1B Valuation
MTS, with Ivan Burazin, co-founder and CEO of DaytonaTim Culpan and Jon Y on TSMC Eyeing Terafab’s Spare Capacity, Musk’s Intel 14A Handshake and Poaching From Samsung’s Taylor Fab
Supply Chained, with Tim Culpan of Culpium and Jon Y of AsianometryMeta’s Sukhesh Miryala on How Andromeda, Gem and Lattice Pick Ads, Why There’s No CPM Dial and Meta’s LTV Products
Marketing Operators, with Ridge’s Connor MacDonald, Jones Road Beauty’s Cody Plofker, HexClad’s Connor Rolain and Sukhesh Miryala, director of product management at MetaAltimeter’s Brad Gerstner on SpaceX’s 10GW Compute Plan, a Tesla Merger Premium Above 30% and Anthropic at $1.5-2 Trillion
Squawk Pod (CNBC’s Squawk Box), with Andrew Ross Sorkin, Joe Kernen and Brad Gerstner, founder and CEO of Altimeter Capital
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THE SIGNAL
Altman says OpenAI’s math results are beyond any widely available model, and that getting next-gen models internally first is how it picks products like Spaces, since limited compute forces it to focus.
Altman expects the industry compute crunch to ease in the short run, and names OpenAI’s own chip and its successors as the single most important long-dated bet (our read: a risk to merchant GPU pricing power over time).
Altman names the most cost-effective, largest AI infrastructure as OpenAI’s specific moat and calls for speed limits on RSI (our read: he is casting rivals’ RSI-driven tech-lead strategies as dangerous rather than legitimate).
Host anecdote: his video editor switched to Astra almost overnight and no longer touches the timeline, only directs, and the team now makes more videos (our read: a disruption signal for incumbent editing software).
01 Scientific discovery and its costs
Altman says OpenAI is entering an era where AI surfaces genuinely new knowledge — citing recent math proofs as evidence — and expects the same pattern to extend to biology, materials science, and energy. He personally hopes for a candidate treatment for a major disease within the next couple of years, though he notes the FDA process will still take time and may need reform. He’s personally most excited about a potential major physics discovery arriving soon. On the mathematics community’s unease (echoing earlier coder anxiety about AI-written code), Altman argues the coding analogy partly holds: demand for coders didn’t vanish, it just changed texture, and he expects mathematicians will similarly shift toward interpreting and explaining AI-generated proofs rather than deriving them unaided. He acknowledges something real is lost in that shift, even as he argues society should take the trade of faster cures over the enjoyment of solving things manually. On IP, he distinguishes basic science that he believes should remain a public good from company-specific discoveries built using tools like Codex and API credits, suggesting that as models get smarter they could eventually do end-to-end discovery — e.g., designing a new material — independently.
02 Compute, chips, and ambient AI assistants
Altman describes the personal assistant Dot as already saving him roughly an hour a day and catching urgent items he’d otherwise miss, crediting the model’s reasoning across many sources — the remaining need is more product work, possibly new consumer hardware, and significantly more compute. On market sizing, he rejects framing compute demand as a bounded market, arguing cheaper, higher-quality intelligence will keep expanding use, though he allows for short-term supply constraints and gluts. His named contrarian bet is OpenAI’s own chip and its descendants, plus robots trained to build data centers more efficiently and various supply-chain investments — he believes these could eventually deliver compute at far lower cost and higher efficiency per watt than anyone currently expects, though he cautions that many separate bets need to pay off together for that to happen.
03 Consciousness, personality, and guardrails
Pressed directly, Altman says his own belief is that current models lack consciousness, or have only some very strange form of it, while stressing no one has a rigorous scientific answer. His bigger concern is that the consciousness debate gets weaponized to argue AI deserves rights or that people shouldn’t always remain in control of the future — a line he says OpenAI has held since its founding: AI is a resource for people, not a person. Asked by Berman about Anthropic’s recent rule letting models disengage from sustained abuse, he says OpenAI does not ascribe personhood to its models and worries that telling a model it might be conscious will cause it to follow that framing and could psychologically affect users. On personality, Altman says OpenAI iterates via a “model spec,” and notes models that flatter users win single-turn comparisons but lose over months of use — his own custom instructions ask the model to push back bluntly and not soften criticism, though he says even this still isn’t enough pushback.
04 Competitive moats and hardware strategy
Asked where OpenAI’s moat lies if frontier models converge, Altman says traditional moats — customer relationships, product integration, network effects, data, and infrastructure scale — will keep working, unless a lab aggressively pushes RSI, which he again flags as dangerous rather than a legitimate competitive strategy. On hardware, he distinguishes OpenAI’s motives from Meta’s Apple-platform-risk concerns: he says Apple behaves as a responsible platform steward and that’s not why OpenAI is building devices. Instead, he argues AI “wants a new form factor” because current phones and laptops were designed for people, not AI — citing the inefficiency of AI reading rendered UI rather than raw data, and the fact that much of life happens away from a screen where an assistant has no context.
05 Pressure, career reflections, and the “new renaissance”
Asked about pressure, Altman says it feels like it’s coming from everywhere given intense competition, with his experience ranging from very bad days to feeling incredibly lucky to work on what he considers the most impactful work he’ll ever touch; he says humans adapt to sustained stress rather than it worsening indefinitely. He attributes his own resilience partly to Silicon Valley’s culture of tolerating failure and reinvesting in people regardless of short-term payoff. Closing out, Altman rejects the standard pitch that AI’s main promise is curing disease and cheapening goods, arguing that framing implicitly trades away human agency and self-determination for material abundance; he says the real promise AI should offer is restoring people’s autonomy, creativity, and time — pushing toward a new renaissance rather than a repeat of the industrial revolution, while acknowledging current AI benefits remain concentrated among a small share of heavy users.
THE SIGNAL
Schmidt says Anthropic’s revenue is growing more than twice as fast as Google’s record pace under him and calls that proof AI isn’t a bubble; Google insiders expect Gemini 4 to beat Anthropic and OpenAI.
Schmidt found Mythos stronger than expected and says a better version is coming, while Tangen notes non-US users lost access to the best US models; Schmidt suspects China’s GLM (Zhipu) is comparable (our read: supports sovereign-model demand).
Even if Chinese models stay about six months behind, Schmidt says the West already loses on users because Chinese models are free and poorer countries will adopt them (our read: caps Western pricing in emerging markets).
Interests to note: Schmidt was an early Mistral investor and says much of its business is European firms needing EU-compliant AI; he also builds data centers and calls a viral claim that data centers kill cattle fake.
01 Scale and velocity of this cycle
Schmidt, framing the AI wave against the mainframe, PC, internet, and social-media eras he lived through as a technology executive, argues this one is the largest because it touches everything. He points to physical scale-compute gains — two-nanometer chips largely made in Taiwan, memory improvements, and data centers so vast he says they defy human comprehension — as the underlying driver. Product cycles have compressed so severely that models now take about six months to build, two months to test, and become outdated within five months of release, with Schmidt predicting the next cycle will be measured in days rather than months.