Ep. 023 - Everyone Leaves Google, Elon Forecasts 1T ARR, Reflecting On GPT-5 | Jon from Asianometry
Summary
- Google’s talent drain is real, with Jeff Dean, John Jumper, Noam Shazeer, David Silver and several Gemini leads departing. Demis Hassabis is described as moving into a higher-level chairman/chief-scientist role while also serving as Isomorphic Labs’ CEO. The panel splits on whether this is merely “the dream team…breaking up as they all turn 38” or the moment Google loses unusually broad system-level judgment that compute alone cannot replace.
- The bearish Google call is about execution, not earnings. Dylan argues that Google repeatedly invents foundational technology, fails to commercialize it, and risks becoming an AI-era Bell Labs: highly profitable, strong in TPUs and potentially “really good for the stock in the short run and medium run,” yet destined to “quietly bow out” of frontier models.
- China restrictions increasingly mean banning the best product, rather than excluding cheap imitators. Jon supports reducing revenue flows to Chinese suppliers but says firms may route around narrowly drafted rules through Vietnam or Thailand. With China strong in optics and the 1.6T/16T transceiver supply chain primarily there, an InnoLight ban could hurt Western buyers more than its intended target.
- Agentic coding redeemed the GPT-5 thesis. Jon says GPT-5 itself “probably wasn’t that great,” Doug calls 5.2 disappointing, but both see 5.6—and potentially “six”—as a major step forward: once agentic coding arrived, “everything hit the pedal to the metal.”
- Bespoke software can be built for hundreds of dollars. With four attempts, a Claude-written specification and Codex 5.3 or something similar, Jon built a 40 MB video editor tailored to his keyboard-driven workflow; 5.6 later completed a previously stubborn feature after roughly 25 specification questions. His advice: “roll your own software,” the technical equivalent of making your own furniture.
- AI may make the open internet toxic to machines. The panel’s mechanism is that software systems are too large for any human to understand, while a model can absorb the code and interdependencies in one context window, finding both high-level and memory-level exploits. Jon’s endpoint is “superhuman level breakage of the system forever,” potentially making nearby compute and models more attractive than constant web access.
- Elon’s $1 trillion forecast requires heroic physical ramps: SpaceX discussed moving from 1 GW to 10 GW by end-2027, then 20 GW, while pulling its revenue target from 2031 into 2030. Jon expects Terafab to start with memory; Dylan argues its scarcity and margin make it the fastest route to revenue. The panel’s deliberately sharp hurdle—“anything under $100 billion at the end of Q4 ’27 is a miss”—highlights how far the arithmetic outruns the required ramp.
Deep dive
1. Agentic coding rescued a disappointing GPT-5 cycle
Looking back at the October 2, 2024 discussion, Jon says GPT-5 itself underwhelmed, but the underlying expectation that progress would continue proved right. When agentic coding became useful, “everything hit the pedal to the metal,” changing his assessment more than the original model launch did.
Jon says 5.2 was also poor; Doug calls 5.6 “mind-blowing,” while Jon says the new base model and 5.6 are good. The panel also jokes that “six” is coming soon. Separately, rumors say the Doug model may be unusually good at writing.
The important change is practical rather than benchmark-driven: Dylan says projects that were outside his control with 4.5 became workable with Lovable or 5.6. Jon’s joking standard for his original podcast appearance—“it doesn’t really matter if I’m accurate, as long as I’m entertaining”—has yielded to repeatable evidence from tools he now uses.
2. China bans increasingly remove the strongest supplier
Jon is directionally hawkish on restrictions because the US should not bankroll strategic Chinese competitors, but he expects firms to satisfy the letter while evading the spirit of the rules. He names Vietnam and Thailand as examples of possible routing around narrowly drafted bans.
The optics case reverses the old trade logic. Jon says InnoLight may be better than Western alternatives, while the broader 1.6T and 16T transceiver supply chain is primarily in China. A ban could therefore be “probably more painful for the Western companies to bear,” especially while demand keeps rising.
The panel’s list now includes optics, humanoid robots, drones, solar panels and EVs—categories where excluding China can mean excluding “the best product in the world.” The proposed industrial-policy answer ranges from nurturing domestic competitors to forcing a 1960s-Japan-style minority joint venture and IP transfer.
3. Google can print cash while losing the frontier
The departures extend beyond public figure Jeff Dean to Quoc Le, Oriol Vinyals and Sanjay Ghemawat, described as ostensible Gemini leads, following John Jumper, Noam Shazeer and David Silver. Departing researchers can raise money—including from Google Ventures—buy GPUs and pursue ideas they may not have received enough compute allocation to pursue internally.
Dean is the hard case for the “stars do not matter” argument. His fingerprints span MapReduce, Spanner, Bigtable, TensorFlow and TPUs; the panel likens losing him to losing Google’s “Chuck Norris,” someone able to hold enough of the full software-hardware system in his head to provide insights others cannot.
Jon offers TSMC as the counterexample: semiconductor R&D is deliberately federated, with “no single ego,” because thousands of people must rub down the corners. Dylan agrees for chips but argues that frontier-model labs implicitly reject that logic whenever investors back a departing researcher on the belief that discoveries—not merely “the biggest engine with the most compute and the most data”—still matter.
Dylan’s broader “Google has an L culture” thesis draws immediate pushback. He says nearly everything beyond search was acquired and cites GCP’s late execution; the discussion counters with Maps, GCP, TPUs and Kubernetes, as well as Google’s $4.3 trillion valuation and position as the world’s third-most-valuable company: “Lose to who?”
4. Google’s Bell Labs problem may be bullish before it is fatal
Dylan separates the stock from the technology trajectory: Google can remain enormously profitable while becoming financialized, protecting search and focusing on TPUs rather than pursuing repeated frontier-model breakthroughs. His analogies are Intel abandoning mobile and IBM favoring mainframes despite once holding roughly 90% market share.
The sharper historical analogy is Bell Labs or Kodak: Bell Labs made the transistor, and Kodak made the first digital camera, yet both examples show how invention can fail to secure the commercial future. Google could likewise invent the transformer, settle for a “good enough” model and eventually become primarily an infrastructure supplier.
The panel’s pushback—worth keeping—is that scale and bureaucracy do not prove decline. Google serves ads, search, YouTube and “a billion surfaces every day,” whereas OpenAI and Anthropic still have one job; Demis Hassabis’s move upward may even position him as a future Alphabet CEO.
Dylan nevertheless calls the present an inflection point: in five, ten or twenty years, this could look like the moment Google chose infrastructure over repeated frontier-model leadership. His hedge is explicit: “This is not saying it’s a bad stock,” and the choice may be beneficial over the short and medium term.
5. Uncontrolled AI could make the web toxic
Dylan’s speculative endpoint is that malicious AIs could make ordinary internet use toxic—damaging computers and stealing money—while nearby compute, such as a neighborhood GB200, could make web retrieval less necessary.
Jon’s mechanism is that modern software is too large for any human to read and understand along with all its interdependencies. A model can take the whole codebase into context and find ways to break or repurpose it, from high-level Python vulnerabilities down to CPU memory leaks. He expects “superhuman-level breakage of the system forever” and jokes that the only fix may be an American Great Firewall, with a physical moat as an additional safeguard.
6. AI-built personal software is already economically real
Jon’s strongest evidence is his 40 MB custom video editor, built for a banking-style, mouse-light workflow full of keyboard shortcuts. Right-clicking an image can send it directly into the editor, while integrated Wikimedia Commons and video search insert material with the correct attributions.
It took four attempts and aggressive scope reduction. Claude questioned him about the workflow and produced a roughly 50-page specification; Jon then handed it to “Codex 5.3 or something” with instructions to build one simple structure that performed one task well enough to extend.
Later models changed the ceiling. Using 5.6 with a library called Easy Cheese, Jon answered roughly 25 questions for one feature—he says the tool can produce an interrogation of “50 words or 50 questions”—then watched it “one-shot the whole thing” after Opus had repeatedly failed.
His total spend was only a couple hundred dollars, using Codex, GPT Pro and a $20 Claude subscription. He has also built an audio post-processing app with a waveform despite having “no idea how it works”; the editor makes him faster, though the unintended result is longer videos, and viewers apparently have not noticed the tooling change.
7. Terafab’s $1 trillion path probably begins with memory
SpaceX’s abundance case moves from 1 GW to 10 GW by end-2027, then 20 GW, while advancing a $1 trillion revenue forecast from 2031 to 2030. The backdrop is Microsoft, Amazon, Google and Meta taking free cash flow toward zero to buy chips—an investment wave Jon says he still struggles to believe.
Jon expects Elon to start with memory. Dylan argues that memory shortages are crimping products in Taiwan and that high margins make it the fastest route to revenue. He cites a Tim Culpan report that Apple has N2 chips awaiting memory. Jon says Apple tried to negotiate a large discounted CXMT order and was refused; Dylan briefly guesses Huawei before Jon insists it was Apple.
The hosts stress-test the schedule with comic severity: “Anything under $100 billion” in Terafab’s Q4 2027 exit rate would be a miss, and Dylan clarifies that this is Terafab alone. Dylan invokes the maxim that “Elon makes the impossible late”; Jon agrees that a $1 trillion chip-revenue outcome in 2040 would still be extraordinary.
The ecosystem choice currently favors Nvidia. Elon wrote that SpaceX installs its GPUs because “they are the best,” while Dylan characterizes TPU software support outside Google as effectively “zero.” Jon points to StableHLO and related tools, but says a TPU user would have to roll their own software, GCP sales representative and console. The panel also cites David Silver’s new effort raising a $1 billion seed round and spending it on FuriosaAI chips.