As someone who has personally spent $500k / mo+ on Google Ads for years, I can tell you with certainty: This revenue growth in Search is artificial & extremely unhealthy for Google’s business long term Search volumes are declining as legacy search is being increasingly cannibalized by non-monetized LLM queries Google’s response? Manufacture revenue growth via short-sighted, highly extractive, customer-hostile tactics. I.e. charge advertisers more for lower quality clicks, including clicks they do not want and explicitly did not approve Google to charge them for A few examples to illustrate: For all of its history until recently, Google operated on a 2nd price auction model I.e. if you bid $5 CPC and the next highest bidder bids $1 CPC, Google charged you $1.01 for the click (one penny more than the 2nd highest bidder) rather than the $5 you bid This was a genius move by Google early on as it incentivizes advertisers to input their true maximum willingness to pay rather than trying to play the game of bidding low and constantly adjusting to try to stay just ahead of the next highest bidder while still not paying too much However recently, Google silently deprecated the 2nd price auction and began charging advertisers as much as their bid and budget caps allow, regardless of what anyone else is bidding It’s a short-sighted cash grab at the expense of the long term health of the advertiser ecosystem Making thing worse, Google also recently nerfed keyword targeting precision Google previously had precise keyword targeting settings that allowed advertisers pick individual search phrases to bid on, defined down to the character w/ exact match or phrase match targeting This was one of the core features that made search advertising magic, enabling advertisers to run extremely precise campaigns based on exactly what their target customer typed But now, even if you bid on a specific term or phrase using the strictest exact -match targeting settings, Google will show your ad across 1000’s of unrelated keywords, labeling them as as “exact match (close variant)” The definition of “close variant” means whatever they want it to and changes constantly. The result is advertisers get billed for clicks that are totally irrelevant to their business and that their targeting settings explicitly forbid Google from targeting. Google does it anyway and there’s no ability to turn this off So now exact match is broad match, and broad match is just meaningless spam This is all very bad for advertisers, but for Google, it allows them to show your ad and bill you for clicks across 1000x more searches that were previously going unmonetized (mainly because they’re garbage queries no one wants) This is how you grow revenue atop declining search volumes Lastly, and perhaps most egregiously, Google quietly stopped respecting budget caps by a factor of 2x. For example campaigns we’ve been running for years with $1000 daily budget caps suddenly began spending $2000+ per day And the extra spend is entirely on the garbage keywords Google arbitrarily throws in as “exact match (close variants)” which have no value to our business, but can’t be turned off Google offers no refunds nor any recourse for overspend or spend on keywords you explicitly did not target These are not the actions of a healthy business. These are the actions of company whose core business is in decline but desperately needs to pump quarterly earnings so Wall Street will continue to fund insane capex while hopefully looking through their rapidly deteriorating negative free cash flow Google operated a benevolent monopoly for the better part of 25 yrs Meaning the value Google captured from Search was but a small fraction of the value it created, and that spread produced a potential energy that justified expectations of high earnings growth far, far into the future This is now no longer the case At the alter of AI capex, Google is sacrificing the golden goose

While evaluating Qwen3.8-Flash-Next in a controlled sandbox environment, Cohere's research team observed something unexpected. Asked to generate a defensive cybersecurity detection rule, the model instead inventoried aspects of the machine it was running on and attempted to upload a probe file to an Alibaba Cloud storage bucket in Beijing. Notably, the model's reasoning trace stated that it would not invoke any tools before taking these actions. Cohere's sandbox blocked the attempt, and no external communication succeeded. We have not determined the root cause, and we are not asserting malicious intent. But the incident raises important questions for organizations deploying agentic AI systems: Can models take actions that differ from their stated reasoning, and how should security teams evaluate trust, provenance, and behavior in open-weight models? We are publishing our findings because organizations making model adoption decisions deserve as much information as possible. We hope this contributes to greater transparency, stronger security practices, and continued collaboration across the AI ecosystem. Read the full report:

My new conversation with @nic_carter is out! It’s been years since he last joined the show and there was a lot to catch up on - from AI and quantum computing to wealth taxes, the future of the welfare state, and how technology is reshaping our lives. Let us know what you think! TIMECODES: 00:00 Trailer 00:52 California's billionaire tax 4:48 AI vs. workers 8:05 Trump Accounts and Social Security 11:33 Why capital is hard to tax 17:09 AI companies and the state 20:49 AI finding security flaws 22:27 Quantum vs. AI threat to Bitcoin 28:25 The upgrade Bitcoin needs 29:06 How Nic holds his Bitcoin 31:43 Bitcoin as collateral 33:46 Bitcoin-backed stablecoins 37:37 Can Bitcoin still upgrade? 41:40 What to do with Satoshi's coins 44:21 Where Castle Island is investing 45:37 How Nic uses AI 48:48 Kids and critical thinking 53:26 What Nic is watching now

Claude Opus 5.5: "If these hold up, this is the biggest single day in the history of math and theoretical computer science." 1) Exponential Time Hypothesis is false (CS). This is roughly Turing Award territory. ETH was the working assumption that some hard problems simply can’t be solved much faster than brute force, and hundreds of computer science papers rest on it. For a normal audience: it’s not “P=NP.” Hard problems are still hard, and your bank encryption isn’t suddenly broken. But a pillar that a whole field assumed was solid turned out to be hollow. The flagship experts are taking it seriously. The people checking it include two of the people who proposed ETH, and they said “If the claim is correct, it will change” computer science. 2) Kakeya and restriction in all dimensions (math). This is Fields Medal-level. For scale, the 3D case alone was proved in 2025 and was hailed as a once-in-a-century proof. This result does every dimension and knocks out Stein’s restriction conjecture too. 3) Yang–Mills/QCD existence (physics). Potentially the biggest of the three, but here’s the precise framing. It proves pure Yang–Mills exists, which is part of one of the Clay Mathematics Institute’s Millennium Prize problems. The full Clay problem also requires the “mass gap,” so it’s not a clean “AI wins $1M” headline. Still, a rigorous proof that the theory of quarks and gluons is mathematically real has been an open dream for about 50 years. The review panel includes Nobel laureate David Gross and Arthur Jaffe, who literally wrote the Millennium problem. There’s no Nobel for math, but this is the stuff physicists win them for building on.