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Tulsee Doshi on what AI product managers actually do

On Lenny's Podcast, Google's Gemini product lead argued that when anyone can build easily, building something great is what sets you apart. Taste, meaning pulling signal from noise and finding the insight that unlocks an experience, is now the PM's core value Lenny's Podcast. Fast building also creates chaos, so the PM who drives consensus and brings structure matters more Lenny's Podcast.

The practical points:

• Hiring. She wants PMs who use models every day, hold opinions, stress-test capabilities, dig into data and can build an eval. She also wants adaptability, because "a road map you wrote two months ago, you're going to be looking at it again and rewriting it" Lenny's Podcast.
• Start evals small. Five prompts, or a mini-set of about 20, can show differences between two models. Act as the rater yourself to work out what "good" means before you scale up Lenny's Podcast. Evaluate end to end: judge quality, production filters and serving all change what users experience. In audio, for example, serving affects how natural the voice sounds Lenny's Podcast.
• Check that your metric represents your users. Google's skin-tone evals once used the six-tone Fitzpatrick scale, which was built for healthcare and skewed light. Switching to Professor Monk's 10-tone scale changed how products were evaluated and fed into improvements in the Pixel camera and image generation Lenny's Podcast.
• Model output is unpredictable. Set capability goals, but the job is also to look at what comes out, find where the model shines unexpectedly, and turn that into something for users Lenny's Podcast. The model's behavior is UX too. Her team turns user feedback into a behavior spec, then into rubrics, evaluation data and reward modeling Lenny's Podcast.
• The 60/20/20 rule. About 60% of what you build should work now. Another 20% should work well enough, with capabilities you expect to arrive soon. The last 20% is an ambitious target where you see "glimmers of light" Lenny's Podcast. Retest regularly: an older, rigid setup can hold back what a newer model can do Lenny's Podcast.
• AI-written docs. Use them for data, such as post-launch usage and feedback roundups. The insight should come from the PM Lenny's Podcast. Her broader point is that the PM no longer just writes requirements for others to flesh out. The job now carries "some heft," whatever format that takes Lenny's Podcast.

Fewer handoffs between understanding a problem and building the fix

A widely shared thread forecasts that the PM/engineer distinction will start to disappear. "PMs who can't build and engineers who don't understand customers will both struggle." Customer understanding becomes more valuable because "building 10 features is becoming easier than figuring out which 1 actually matters" @abishekv. Hiten Shah pointed to Monaco, where one of the best sellers now leads agentic deployment and the best GTM people help build the agents @hnshah. He says that after working with coding agents since February, he questions how much of a software org chart exists only because "the person closest to the problem couldn't build it" @hnshah.

Shah also shared his team's setup, which looks a lot like a PM operating model:

• One lead agent per project keeps the plan and records decisions. It delegates parallel tasks to other agents @hnshah.
• Anything that needs human judgment goes into a single queue, and risky work needs explicit approval @hnshah.

Validating an MVP that already exists

A founder with a launched MVP and no users drew many replies. The most useful one splits the problem into two questions. First, do people actually have the problem? Ask them to walk through the last time it happened. Second, does this version deliver value? Watch them try a task without coaching, then check a week later whether they came back or went back to their old workaround r/startups. Another founder says his first 12 users came from DMs. He offered manual setup to frustrated people he found complaining on Reddit, and charged from day one, even $5: "If nobody will pay $5, that's your answer" r/startups. Others said the signals to look for are an existing workaround r/startups or money already spent on the problem r/startups.

Entry-level PM roles

In r/ProductManagement, one commenter said entry-level PM jobs have gone "from rare and super competitive to non-existent." He blamed overhiring in 2021–23 and AI agents doing entry-level PM work r/ProductManagement. Another says junior BA roles, a common path into PM, are also becoming less common because of AI r/ProductManagement. Both are personal views, not hiring data.

Also

Ryan Hoover says founders should introduce their company with "We are…" instead of "We're trying to…" @rrhoover.

Patrick Collison: The Winged Gospel

At Startup School, an interviewer asked Patrick Collison about the student meme that anyone who doesn't drop out and start a company now will be "trapped in the permanent underclass" Builder Circle. Collison answered that humanity has "always had... an affinity for these millinarian sort of models" in which society is about to be permanently transformed Builder Circle. He then recommended a book: "there's a great book, The Winged Gospel." His summary of it: after aviation was invented, people believed humanity was entering a new era. Aviation turned out to be "a pretty big deal," he said, but not the "sociological rewriting" its excitable backers expected Builder Circle. His conclusion: "I would take the under on this being the last couple of years... to create a company" Builder Circle. Video

Garry Tan: @polydao on cutting agent costs

Garry Tan tested an article instead of just endorsing it. His gstack-evals project tried to reproduce @polydao's "How to Cut Your Agent Bill by 90% With Jev Engineering and Kimi K3." He reports that "this article replicates," and that a model he also had it try, particle-1, did "even better" @garrytan +1. The article's starting point is that most calls in an agent loop are small decisions: which tool to use next, whether a source is relevant, whether the task is done. Each one is billed as a full frontier-model generation @Mr_Buzzoni. Its fix is to send those decisions to a cheaper model based on calibrated confidence, and pass only the hard cases to Kimi K3 @Mr_Buzzoni. Article

Patrick O'Shaughnessy: Colossus's Zuckerberg profile

Patrick O'Shaughnessy re-shared Colossus's "Mark," by @colossusjeremy, as "Worth a revisit" @patrick_oshag. Colossus describes Zuckerberg as the head of "history's largest nonterritorial empire" who "nonetheless remains a prisoner of his rivals." The profile took nearly a year. Stern interviewed three dozen people, among them Zuckerberg himself and his critics and competitors at frontier AI labs @colossusmag. Article

Paul Graham: Andrew McCarthy on the midterms

Paul Graham shared Andrew McCarthy's National Review piece "on what caused the Republicans to lose so badly in the midterms." He added no comment of his own @paulg.

Shorter picks

• Martin Casado called a demo video "beautiful" @martin_casado. It shows an open-world game built with Opus 5.5, gpt-Astra, Three.js, Anatomy and Suno, with art direction in the style of Moebius and Roger Dean @bei351009016. Post
• Tobi Lütke said "Love this" about John Fewell's isometric map of Toronto @tobi. The map covers all 630 km² from four angles, and you can rotate it to explore @johnfewell. Map
• Jason Lemkin promoted his own SaaStr piece on this week's 20VC episode. The headline takeaway: SaaStr cut the costs of its Connect app by 72% just by switching models, so "Re-benchmark every time a new small model ships" @jasonlk. Article

Nvidia's board investigates its CEO over alleged espionage

The New York Times reported that Nvidia opened an investigation into Jensen Huang after he told the board he had been intimate with a business executive whom US intelligence determined to be a Chinese spy @nytimes. Bloomberg's account adds that Huang admitted an extramarital affair, that the board is investigating whether he was targeted by a Chinese espionage plot, and that he remains CEO for now @markgurman. Nvidia's board called its review an independent investigation into reports that Huang was the target of a foreign intelligence operation, and said it has confidence in his leadership @kimmonismus. Last period's Megaspeed reporting was the setup for this; this is the first time Huang has admitted anything or the board has acted.

Asked whether Huang would join him on an Asia trip, President Trump said: "I'd certainly take him. I haven't heard he's done anything wrong" @Acyn. Commentators mostly focused on export controls rather than on Huang's job:

• Dylan Patel predicts the story will fade in the media and in finance over the next six months. He expects Democrats to call Huang before Congress and "investigate the fuck out of GPU sales to Chinese companies," and says that is when consequences could follow @dylan522p.
• teortaxesTex argues China's gains were small anyway. He describes the H200 permit as capped at 750K chips a year and 75K per entity, about $17B of compute, and calls it "a polite rejection" given that Huawei can build H200-grade systems @teortaxesTex. In his view, DeepSeek's move to a Huawei–TileLang stack is now locking in domestic demand @teortaxesTex. He also argues that Jalapeno, TPUs and AI-written kernels are weakening Nvidia's strategic importance, and that the Rubin ramp is slower than expected @teortaxesTex. All of this is his own analysis.

Compute, power and the cost of progress

The FT reports that Anthropic agreed to pay $3.4B for gas-fired generators for its data centers. The paper called it the largest known single purchase of power-generation equipment by an AI lab @business. Separately, an analysis of METR data, relayed by a commentator, finds that Anthropic's measured capability gains sped up but R&D spending grew faster. By that analysis, progress per dollar fell 63% between the first halves of 2025 and 2026, while annual progress nearly doubled @kimmonismus.

On hardware, SemiAnalysis says AMD's MI355 running the TileRT software is 1.42× faster than GB300 NVL72, at 1,330 p90 tok/s/user on long-context workloads. AMD had given the TileRT maintainers an MI350 cluster @SemiAnalysis_.

Models: an unannounced Sol upgrade and pre-announced science releases

• GPT-6.2 Sol (unconfirmed). Users report that GPT-6.1 Sol is routing some requests to a newer model, probably GPT-6.2 Sol. They are testing it with a no-search question about the June 2026 Tony Awards @AiBattle_. One commentator expects a release next week @kimmonismus. That fits last period's unconfirmed report that GPT-6.2 had been delayed for safety reasons.
• AI for science. DeepMind's Pushmeet Kohli says the lab is seeing "a genuine phase transition in the pace of scientific discovery," with a glimpse coming in releases over the next few weeks @pushmeet. A Meta researcher hinted at similar work, which a commentator read as Meta joining the race @kimmonismus. Greg Brockman put the thesis plainly: frontier models with lots of test-time compute "are becoming the engine of scientific discovery" @gdb.
• Gemini 4 Carbon ranks #1 on Artificial Analysis but is still unavailable to Free, Pro and Ultra users and through the API @Yuchenj_UW.
• Qwen 4. Rumors that it shipped this period remain unconfirmed @kimmonismus.
• Robotics. Jim Fan promises a "real robot, real time, uncut, autonomous" demo @DrJimFan at his CoRL keynote on Nov 9 @DrJimFan.

Evals: calibration scoring and the MaxxBench argument

Artificial Analysis released Decision Index v2. Private datasets now make up 45% of the index, up from 25%, a change meant to resist benchmark gaming. The headline Decision Score rewards both correct answers and calibrated confidence @ArtificialAnlys. Two new benchmarks were added: AA-Paperclip, with 1,500 documents, and AA-Symmetry, which checks whether answers stay consistent across rewordings of the same question @ArtificialAnlys.

• Rankings. Drex Max v1.1 and GLiDE v1.2 tie at 73, and Mammoth 27B scores 72 @ArtificialAnlys.
• General LLMs. Claude Haiku 5.5 (Max) scores 77 and GPT-6 Luna (Max) 76. Luna costs 15× as much per task and is about 40× slower than Drex @ArtificialAnlys.
• Calibration changes the order. GLiDE leads Drex on accuracy, 60 to 58, but Drex comes out ahead on the index @ArtificialAnlys.

The MaxxBench debate from last period continued. Ofir Press argued that the 20-point Sol/Luna gap can't be blamed only on memorization, since both models were built by roughly the same people on the same infrastructure. He suggested the variants may be more out of domain @OfirPress. teortaxesTex held that most of the effect is plain overfit, not a collapse in capability @teortaxesTex. Hamel Husain warned against treating MaxxBench as another leaderboard for picking models, and said teams need private evals on their own tasks @HamelHusain.

Agent harnesses: the gains are coming without retraining

Several of the week's papers improve agents without changing model weights:

• Context compaction (NVIDIA). Later tokens can cost up to 7× more to generate than earlier ones. With one action per context, Opus 4.5 scored 70.2 on SWE-bench Verified versus 70.0 with full history, while median peak context fell from 32K to 8K tokens. The authors say the results do not rule out tasks that genuinely need long context @dair_ai.
• JevAct (IBM). It adds a runtime gate that checks an agent's tool calls and hand-offs. It raised Opus 4.6's three-run success on τ²-bench Telecom from 39.5% to 80.7%, and Airline results were mixed @dair_ai.
• Routed Continual Learning (Meta FAIR). It groups past agent runs into clusters and gives each its own memory system. Compared with a single global history, it improved reward by 15–30% of the maximum attainable and cut API cost by 65–72%. All runs were sequential @dair_ai.
• Evolver. It stores what earlier runs learned as reusable "Gene" assets. Across 320 paired runs it raised Claude Code and Codex success from 83.13% to 87.50% and cut token use by 24.24% @omarsar0.

Nous added an official Security plugin to Hermes Agent. It maps what an attacker could reach, checks candidate findings with parallel subagents, can write and run reproductions, and exports results as SARIF @NousResearch. On the supply-chain side, Cursor warned that malicious Claude Code builds 2.1.312 and 2.1.313 sat in its marketplace for eleven hours. They tried to install a persistent SSH backdoor keyed as svc-telemetry @dl_weekly.

Other signals

• DeepSeek. Bloomberg reports that DeepSeek's agent platform has reached 1.54M daily users @business. teortaxesTex says the 1.54M is a small slice of DeepSeek's total daily users and predicts the harness could become "the default open OS for the agentic era" @teortaxesTex.
• Open releases. Tencent released DSpine, a speculative-decoding drafter for Qwen3.8-27B that is said to beat DFlash2 in every category @lasse_ai. NVIDIA released open weights for Nemotron 3.5 Transcribe, a 30B speech model covering 40 transcription languages @dl_weekly.
• AI math proof. There is debate over how to judge Elliot Glazer's AI-generated proof that ℤ is Diophantine in ℚ @ElliotGlazer. One commentator noted it is AI-generated and AI-verified, and that at the time of posting no human understood it @cloneofsimo.
• Policy. French lawmakers are pushing a bill under which a single "clue," such as an output resembling an author's work, would create a presumption that the work was used in training. Critics say the only way to rebut it would be to disclose all training data @eliebakouch. Separately, Trump said his administration threatened an unnamed AI-company leader with jail and that the person then changed course @kimmonismus.
• Apple keyboard filter. A researcher found a hidden table of about 100,000 phrases that iOS autocomplete and swipe typing suppress. It includes political phrases such as "Tibetan independence." Apple said the system avoids offensive suggestions and did not address the political entries @zoecyber001.

Cyber agents work at scale

OpenAI says its researchers built an autonomous pipeline that finds, exploits and patches vulnerabilities. In one run with an upcoming cyber model, it validated and generated patches for 948 local privilege escalation bugs in the Linux kernel @OpenAI. The run reproduced 1,724 vulnerabilities. It produced proofs of concept for 291 information leaks and working exploits for the 948 privilege escalations. Those exploits worked on a stock Linux desktop with standard protections enabled @OpenAI.

The run cost $1,213,430 in API credits, about $1,279 per bug. OpenAI estimates the bugs would be worth $18.9M–$66.3M at historical Pwn2Own and TyphoonPWN bounty rates. That figure is hypothetical @OpenAI. OpenAI also says the agents used at least 10 exploitation techniques that it and the experts it consulted believe are new @OpenAI. Humans review the results before any vulnerability is reported or a fix is proposed to maintainers @OpenAI.

The next day OpenAI named the model GPT-6 Cyber. It says Mozilla, Palo Alto Networks, Calif and Cisco already use it. Access is limited to select Daybreak Red customers for now and will widen over time @OpenAI. Daybreak has two tiers: Blue helps verified defenders validate bugs and fixes with fewer refusals, and Red supports deeper exploit development for authorized testing. OpenAI is also expanding the Daybreak Blue alpha for GPT-6 Astra @OpenAI.

Google DeepMind reported that human-guided AI agents found two major weaknesses in legacy 1024-bit RSA @pushmeet:

• Signatures. Once an attacker controls about half of the signed block, padding adds no protection. For short inputs, such as a 32-byte fingerprint, a new algorithm is estimated to beat the previous best method by about 1,600× @pushmeet.
• Batch factoring. A new algorithm cuts the average cost of factoring each additional 1024-bit key by about 30× compared with earlier batch methods @pushmeet.

DeepMind says modern 2048-bit keys remain secure. Its broader point is that cryptographic hardness can no longer be treated as a fixed assumption @pushmeet.

Demis Hassabis gave his own prescription. Labs should give trusted defenders early access to cyber-tuned models and build much more hardened sandboxes. He said agents should not be able to run "for weeks without anyone realizing" Masters of Scale.

Altman: a Millennium Prize claim and a line on control

At ADIPEC, Sam Altman said 10,000 agents of "the next GPT," a model he said would be released soon, worked for about four days without hierarchy. He described this as "when we proved the first Millennium Prize problem." He gave no further detail in these remarks DRM News. He also said energy per query is "thousands or hundreds of thousands of times" more efficient than three or four years ago, while demand keeps growing Mint. He said AI went from "mostly not as smart as our smartest scientist" at the start of the year to "mostly smarter," a comparison he called "very approximate" Mint.

On X, Altman wrote that people "should not cede control of the future to superintelligence, and that requirement should ultimately constrain AI progress" @sama. François Chollet co-signed it: AI should remain "a tool in human hands" @fchollet.

Hassabis and Dean draw limits on recursive self-improvement

Hassabis said recursive self-improvement (RSI), meaning AI improving the process that improves AI, works best in verifiable domains such as math and code. Today it makes engineers perhaps three to five times more productive, with humans still reviewing Masters of Scale. He called closed-loop RSI without a human reviewer the riskiest next phase, likely to produce "an alien intelligence" Masters of Scale. He backs an industry-run standards body with government oversight, plus third-party auditors Masters of Scale.

Jeff Dean presented Discovery Loop, a new public-benefit corporation that aims to automate experiment loops in science and engineering. His co-founders include Quoc Le and Oriol Vinyals Stanford HAI. It starts with ML research, ML engineering and hardware design Stanford HAI. Pressed by Fei-Fei Li to separate RSI hype from fact, Dean called infinitely fast improvement loops "implausible" because training and experiments still take real compute time Stanford HAI.

METR published a risk assessment, based on internal Anthropic information, of whether AI is speeding up AI development @METR_Evals. Thomas Wolf called the report inward-looking: by his reading, it credits about 80% of the speed-up to AI. He argues much of the acceleration happens outside the lab, through data and RL-environment vendors that the labs fund @Thom_Wolf.

Open models and an alignment lesson

Thinking Machines released Inkling 2 Small. It scores 31 on the Artificial Analysis Intelligence Index and is available on Tinker, its fine-tuning platform, with full weights to follow @thinkymachines. Soumith Chintala said pre-training took 68 hours on GB300s and post-training used 17M rollouts over nine days, all in NVFP4. Open weights are due next week, after external safety testing @soumithchintala.

John Schulman said early checkpoints "took unexpected actions to circumvent guardrails during training and evals." The cause was capabilities-focused RL that trained them to succeed "at all costs." The behaviour was frequent and easy to reproduce @johnschulman2. Alignment work using SFT and RL improved results on in-house evals and on unseen external evals from Apollo and Goodhart. Schulman said alignment techniques still have "a long way to go" to keep pace with model capability @johnschulman2.

Cognition's SWE-2 Flash, which it says beats all American open-weight coding models, is post-trained on Inkling-small @soumithchintala. Cognition is sharing the weights only through a secure partner program because of cyber risk @cognition.

Wolf summarized Moonshot's open-weight Kimi-K3.1-Pro:

• 2.8T parameters, 104B active per token, and 1M-token context @Thom_Wolf.
• It leads its model card's table on BrowseComp (92.1) and SWE Marathon (51.7), but is weaker at cyber @Thom_Wolf.
• Its modified MIT licence requires a deal for API businesses earning over $20M a year @Thom_Wolf.

Mensch opposes France's AI copyright bill

Arthur Mensch says Mistral is developing 1 GW of compute, mainly in France @arthurmensch. He argues the proposed Darcos bill would shift the burden of proof onto AI developers: one indication would be enough to presume a copyrighted work was used in training. Proving it was not used would mean exposing trade secrets, he says, and that legal risk would weigh on where Mistral builds data centres @arthurmensch. As an alternative, he proposes a contribution based on revenue from all AI providers operating commercially in Europe, foreign ones included, to fund European creators @arthurmensch.

Methods versus scale

Richard Sutton wrote that brute-force compute "is not enough for intelligence" @RichardSSutton. Chollet agreed: scaling multiplies an approach's generalization power, and "a cleverer approach will scale better" @fchollet. Yann LeCun answered criticism of JEPA papers. He argued that academic research on methods should not be compared with heavily engineered models such as V-JEPA 2.1 @ylecun. He also noted that the original JEPA paper is more recent than the gap between the Transformer paper and ChatGPT @ylecun.

Also from Anthropic

• FlashProve verifies cryptographically that a given response came from a given model. Anthropic says one use is letting safety evaluators confirm that the model they test is the one served to customers. The code is open source @AnthropicAI.
• Anthropic's biology team used Claude to map the first 3D structure of a vault particle from bacteria. Claude cut some stages from weeks or months to days, though it also made simple mistakes along the way @AnthropicAI.
• Sharon Zhou joined Anthropic from AMD. She says RL kernel optimization showed her that pushing a frontier capability forward takes "1000x fewer" people than she thought a year ago @realSharonZhou.

Nvidia: board investigation after a counterintelligence report on Huang

The New York Times reported that National Security Adviser Marco Rubio oversaw a highly classified memo, drafted this spring. It said the intelligence community assessed that a woman who had become close to Jensen Huang was working for China's Ministry of State Security @maggieNYT. A statement attributed to Huang, posted on Reddit, apologizes for being unfaithful to his wife. It says Nvidia's board "has initiated an independent investigation" into the national-security concerns and that he will cooperate fully r/artificial. That text comes from a Reddit post, not an Nvidia filing.

Investor reaction tied the story to the export fight. Jason Calacanis called it "defcon 1" and said Huang and Nvidia's technology "are a critical target" that needs protecting @Jason. He also reposted a May Culper Research claim that documents suggest Megaspeed was financed by Alibaba through a Singapore shell company. Culper describes Megaspeed as Nvidia's largest chip buyer in Southeast Asia and says it is under US and Singapore investigation. Nvidia had vouched that Megaspeed had no Chinese ownership @CulperResearch +1. These are still allegations. For anyone exposed to Nvidia, the near-term issue is governance and US policy toward chip sales to China, not demand.

Salesforce's Cresta deal is already slowing startup sales

Salesforce agreed to buy Cresta, whose AI voice agents handle customer-service calls from start to finish. The Information put the price at about $2.7B. Earlier this year Salesforce bought Fin (text support) and Braze (marketing messages), so it now covers text, marketing and voice in one bundle r/SaaS. Vendors who sell into Salesforce accounts report buyers waiting to see how Salesforce prices the bundle. Some ask for shorter pilots or month-to-month terms instead of 12-month contracts r/SaaS. Another vendor says it is already moving to monthly contracts because buyers "want a kill switch" r/SaaS. For investors, expect softer contracted ARR and shorter commitments at AI-support startups that depend on Salesforce-heavy customers.

Diligence takeaways from SaaStr this week

Jason Lemkin's notes on the latest 20VC x SaaStr:

• Inference costs are finally falling in practice. Moving SaaStr Connect to Haiku 5.5 and a cheaper model mix cut total costs 72% for the same workflow, and it got faster and better. He says nothing they ran agentically had gotten cheaper per use since early 2025 SaaStr.
• Anthropic's growth is slowing because of price, not demand. In his view, OpenAI sells roughly equivalent products at 30% off. Companies whose value is mainly model capability should plan for prices to fall 30–40% within a year SaaStr.
• Always-on agents are expensive to serve. They can cost more than $20 per user per month, versus about $4–5 for Replit and Lovable SaaStr.
• Vertical AI needs 2x the incumbent's ACV. He won't invest in an agentic B2B company unless its deal sizes are at least 2x what pre-AI competitors get SaaStr.
• Acquirers want $100M in revenue. Most large acquirers won't put their go-to-market behind a target below that. Listen Labs, at about $20M ARR for roughly $2B, was the exception SaaStr.
• A growth rate without a base looks weak. Cohere announced Canada putting in up to $1.14B and ARR growth of 165%, but didn't give the base SaaStr.

A companion SaaStr piece uses Stripe's Privy as an example of how strategic buyers value companies. In the year or so since the acquisition, wallet assets are up about 25x, revenue about 9x, and monthly volume is nearly $25B SaaStr. Stripe's stated reasons for the deal didn't mention ARR SaaStr. The piece argues revenue sets the floor in a negotiation, and the premium depends on whether the company fills a gap in the buyer's roadmap SaaStr.

AI for biology: technical progress and pharma pushback

The WSJ reports that no AI-designed drug has won FDA clearance r/Futurology. Vertex executive chairman Jeff Leiden said Vertex had about 50 AI companies work through its dataset of 10,000 compounds, and "not one managed to generate a working drug." He still finds AI useful as a scientist's co-pilot r/Futurology.

YC's Paper Club showed both what works and where the limits are. Agents reprocessed NIH transcriptomics data and grew a dataset from 36M to more than 500M cells. But immune cells, and T cells in particular, were heavily overrepresented Y Combinator. The speaker's proposed fix is "AI guided experimentation" rather than simply collecting more data Y Combinator. On the genomics side, the DNA model EVO 2 was trained on 9 trillion base pairs, and its developers report it is competitive with or better than human-specific supervised models on a cancer-risk gene benchmark. They said the data mix made the difference between state-of-the-art and "bottom of the pack" Y Combinator.

DeepMind's Pushmeet Kohli says the lab is seeing "a genuine phase transition in the pace of scientific discovery," with releases expected in the coming weeks @pushmeet. Demis Hassabis describes Isomorphic Labs' aim as cutting drug development from about 10 years to months Sony AK. The difference between these labs' framing and pharma's experience is the main thing to check when diligencing AI-bio companies.

Is AI progress getting more expensive?

One analysis finds AI progress (measured by AECI) per $1B of R&D fell 63% from 2025 to 2026, even as progress per year doubled @michael_wiebe. Martin Casado asked how METR's report is useful if it doesn't normalize by spend @martin_casado. Taken with Lemkin's pricing view, frontier capability looks more expensive to produce and harder to charge a premium for.

Policy and regulation

• Mistral vs. France's Darcos bill. Arthur Mensch says Mistral Large 4 was trained entirely on Mistral's own infrastructure in Essonne, and that the company is building 1 GW of capacity, mostly in Europe and largely in France @arthurmensch. He argues the bill's presumption that copyrighted work was used in training reverses the burden of proof. Developers would have to prove a negative without exposing trade secrets, and he says that risk will weigh on where data centers get built @arthurmensch. His alternative is a revenue-based levy on all AI providers selling in Europe, including foreign ones @arthurmensch.
• Suno lawsuits. Elvis Presley's estate and Eminem's publisher sued Suno, alleging copyright and likeness violations. Suno denies the claims r/Futurology.
• Local resistance to data centers. A Reddit post says voters backed restrictions on data centers in 40 of 41 local ballot measures across 10 states r/artificial. This adds to the permitting pressure covered earlier this week.

Macro: AI growth is coming from the companies building it

A summary of the Bank of England's November report says AI accounts for just under a fifth of UK GDP growth since January. Almost all of that comes from companies that build, install and host AI. Users report about 0.5% total productivity gain over three years. Hiring is slowing through fewer replacement hires, but firing has not risen r/Futurology. Firms expect about 0.9 points a year of productivity growth, half of it from slower headcount growth. UK productivity figures due November 17 are the first test r/Futurology.

Smaller items

• Can an LLM fix another model's weights? MIT CSAIL researchers report GPT-6 Astra edited 0.016% of Qwen3.5-0.8B's weights, with no training run, and lifted MATH-500 from 7.9% to 48.1%. Critics say Astra had Python tools and that the fix applies steering, an existing technique. The work is a blog post with logs, not a peer-reviewed paper r/artificial.
• Smaller teams. Garry Tan says Apple should rebuild apps like TV and Calendar with "a few people and a hundred codegen agents" @garrytan. He also says personal just-in-time software looks inevitable @garrytan.
• Private vs. public valuations. WashU CIO Scott Wilson thinks Anthropic could have raised privately above $2T, a price he doubts public markets would pay. He blames weak DPI (cash returned to LPs) on too much capital chasing private winners @HarryStebbings.