#Google

So Google just announced some major changes in DeepMind’s leadership. Demis Hassabis will become Chair of Google DeepMind and Alphabet’s Chief Scientist, leaving daily management while continuing to advise its models and research. Basically it gives Hassabis more time for AGI strategy, scientific discovery, global policy work, and Isomorphic Labs, while Kavukcuoglu carries the pressure of shipping Gemini. The timing also reflects scale: Gemini now serves 950M monthly users. So model research, releases, and app execution needs a much larger operating job. The announcement also confirms that Gemini 4 is in development, though Google provided no release date or technical details. Jeff Dean and Sanjay Ghemawat are separately leaving to form a public-benefit research company, with Alphabet remaining an investor and Cloud partner. Google is building two leadership tracks, one deciding where advanced AI should go and another turning that research into products used at Google scale.

Here, Jeff Dean (Google's chief scientist) explains here how the idea behind TPUs was born at Google. In the early 2010s, he noticed that new speech recognition and vision were getting much better, but they were extremely heavy to run on regular CPUs. His back-of-the-envelope calculation was: if speech recognition got good enough that, say, 100M people talked to their phone for 3 minutes a day, and all of that ran on CPU servers, Google might need roughly 2x the total number of machines it already had, just for that one feature. That was obviously insane in cost, power, and time to deploy, so he concluded they needed a completely different kind of hardware, purpose built for neural nets. Also given neural nets mostly use a small set of simple numeric operations and are very tolerant of low precision and noise, so specialized chips could be dramatically more efficient than CPUs or GPUs. Instead of using flexible but inefficient FPGAs, he pushed for a custom ASIC: TPUv1, an inference chip on a PCIe card, then convinced the CFO to deploy about $50M worth of them in data centers before every use case was even fully mapped out. --- From 'Radical Ventures' YT channel (link in comment)

Announcing Discovery Loop! I am very excited to announce that, along with my longtime friends and collaborators @Sanjay_Ghemawat, @OriolVinyalsML and @quocleix, we are founding Discovery Loop (@DiscoLoopAI), a Public Benefit Corporation whose mission is to automate machine learning, science, and engineering to accelerate discoveries and progress. The four of us have worked together for 14 to 30 years, and have helped build some of the world’s most used products, infrastructure and AI models, and we’re excited to turn our attention to this ambitious endeavor. ♾ Learn more at: http://www.discoveryloop.com

Announcing Discovery Loop! I am very excited to announce that, along with my longtime friends and collaborators @Sanjay_Ghemawat, @OriolVinyalsML and @quocleix, we are founding Discovery Loop (@DiscoLoopAI), a Public Benefit Corporation whose mission is to automate machine learning, science, and engineering to accelerate discoveries and progress. The four of us have worked together for 14 to 30 years, and have helped build some of the world’s most used products, infrastructure and AI models, and we’re excited to turn our attention to this ambitious endeavor. ♾ Learn more at: http://www.discoveryloop.com

Announcing Discovery Loop! I am very excited to announce that, along with my longtime friends and collaborators @Sanjay_Ghemawat, @OriolVinyalsML and @quocleix, we are founding Discovery Loop (@DiscoLoopAI), a Public Benefit Corporation whose mission is to automate machine learning, science, and engineering to accelerate discoveries and progress. The four of us have worked together for 14 to 30 years, and have helped build some of the world’s most used products, infrastructure and AI models, and we’re excited to turn our attention to this ambitious endeavor. ♾ Learn more at: http://www.discoveryloop.com

Just shared some changes we’re making to the teams at @GoogleDeepMind. @DemisHassabis is stepping up to become Chair of @GoogleDeepMind & Chief Scientist of Alphabet, in addition to leading @IsomorphicLabs. He’ll be able to dedicate his time and focus on shaping the future of AGI and scientific discovery. It’s work that is vitally important to Alphabet and humanity, and I can’t imagine a better person than Demis to do it. He’ll stay closely connected to Koray and the GDM teams. @Koraykv will become the SVP, @GoogleDeepMind, responsible for all aspects of model development, GDM research, and @Geminiapp & dev teams. Koray has been at GDM for 13 years and is a world-renowned expert in the field, starting our deep learning team and driving breakthroughs like WaveNet & DQN. GDM is in great hands! Excited for this next chapter. You can read my note along with the message Demis sent to @GoogleDeepMind here: https://blog.google/company-news/inside-google/message-ceo/next-chapter-ai-momentum/

“If you hit recursive self-improvement, that curve will go to hyperexponential, and that is a key part of the investment thesis, the scientific thesis, and a key part of why society’s investing what it’s currently investing in.” Google DeepMind Chief Strategy Officer Jasjeet Sekhon says AI infrastructure spending is financing a hoped-for self-improvement loop. In its strongest form, recursive self-improvement (RSI) will let AI design, evaluate, and train more capable successors with progressively less human supervision. The current evidence is still narrower: e.g. Google's AlphaEvolve proposes algorithms, then automated evaluators run and score them against human-defined objectives. Google says its resulting kernel changes reduced Gemini training time by 1%, an example of bounded improvement rather than autonomous successor design. ---- From "Berkeley RDI" YouTube channel, (full video link in comment)

$SPCX SpaceX Q2 FY26: • Revenue +92% Y/Y to $7.8B ($1.0B beat). • Operating margin -2% (+22pp Y/Y). • CapEx $18.4B (including $15.8B for AI). • GAAP EPS -$0.09 ($0.20 beat).