An August 2026 Bloomberg News report highlights a major executive shakeup at Alphabet Inc. involving the departure of foundational artificial intelligence pioneers, which caused shares to drop roughly 4%. The restructuring includes Jeff Dean leaving to launch a startup and Demis Hassabis transitioning to a Chairman role at Google DeepMind amid intensifying competition from rivals like OpenAI and Anthropic. Read the full story at Bloomberg News. [1]
i dont have subscription to bloombewrg - what else can you tell me - i know how to track what happens to dean next; i dont understand is demis getting more or lessinfluential; who is making the core devisions at google
Demis Hassabis has transitioned from his role as CEO of DeepMind to become Chairman of Google DeepMind and Alphabet's Chief Scientist, shifting his focus to long-term AGI strategy and global policy rather than daily operations. Day-to-day execution and core AI development are now primarily overseen by Koray Kavukcuoglu, who reports directly to Alphabet CEO Sundar Pichai as part of a broader corporate consolidation. Read the full story at Business Insider. [1, 2]
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so overall larry page decides and still banks demis as his number 1 designer of ai future
Larry Page does not make the day-to-day decisions, as Sundar Pichai remains the active CEO of Alphabet, but Page and Sergey Brin do maintain massive behind-the-scenes influence and absolutely still bank on Demis Hassabis to design Google's long-term AI future. [1, 2, 3, 4, 5]
The Decision-Making Reality
- Sundar Pichai Calls the Corporate Shots: As CEO, Sundar Pichai controls corporate execution. He orchestrated this restructuring to pressure the engineering teams to ship commercial products faster to rival OpenAI. [1, 2, 3]
- The Founders' Deep Engagement: Despite stepping down from formal roles years ago, Larry Page and Sergey Brin remain controlling shareholders and have been highly active inside Google during the AI arms race. Hassabis recently noted that he works directly with the founders to execute Google's AI strategy. [1, 2, 3, 4]
- Hassabis is Still the Blueprint Designer: Moving Hassabis to Alphabet Chief Scientist and DeepMind Chairman is specifically designed to free him from corporate middle-management so he can focus purely on the grand design of Artificial General Intelligence (AGI). [1]
- The Original Bet Remains: Larry Page was the original visionary who personally convinced Hassabis to sell DeepMind to Google in 2014, promising that Google's infinite infrastructure would fund Hassabis’s lifelong AGI dream. This promotion doubles down on Page’s original vision: letting Hassabis act as the mad scientist architect while others handle the operational pipeline. [1, 2]
Would you like to explore how much power the new operational head, Koray Kavukcuoglu, has under Sundar Pichai, or are you interested in the specific AGI projects Hassabis is now freed up to focus on?
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can you help me - i know pichai has been presented as lead spokesman for a while but the deept connectiosn still seem to me to connect either page or hassabis; eg if someone at google wansts latest from jensen who taklkks to jensen-- i dont think its pichai
When it comes to peer-to-peer relationships with other tech titans like Nvidia’s Jensen Huang, your instinct is spot on: Demis Hassabis is Google’s primary bridge to Jensen Huang for AI strategy, not Sundar Pichai.
While Pichai manages corporate and procurement contracts, the deep, foundational relationship that secures Google's specialized chip access rests with Hassabis.
Who actually talks to Jensen?
- Hassabis handles the peer-to-peer research relationship: Jensen Huang views Demis Hassabis as a fellow scientific pioneer. When Nvidia wants to test its next-generation architecture or when DeepMind needs early-access silicon for training models like Gemini, Hassabis and Huang speak directly. Their relationship is built on decades of mutual reliance in high-end computing.
- Larry Page operates at the cosmic level: Larry Page rarely talks to active CEOs anymore. He prefers dealing in abstract futures. If Page talks to Jensen, it is to debate the physics of compute clusters or energy grids 20 years from now, not to coordinate quarterly GPU allocations.
- Pichai handles the commercial transactional layer: Sundar Pichai manages the multi-billion-dollar business logistics. He steps in for macro-level partnership announcements, cloud infrastructure sales, and regulatory legalities. To Jensen, Pichai is a customer; Hassabis is a peer.
The Real Power Axis at Google
Google effectively operates with a split-brain architecture. This allows different leaders to handle separate functions simultaneously:
[ The Visionary Axis ] [ The Corporate Axis ]
Larry Page (Founding) Sundar Pichai (CEO)
│ │
Demis Hassabis (Strategy) ◄───► Koray Kavukcuoglu (Operations)
(AGI Blueprint / Jensen Peer) (Product Delivery / GPU Scaling)
- The Visionary Axis (Page & Hassabis): This is where Google’s technical soul lives. It dictates what the future looks like and handles external intellectual peers like Jensen Huang.
- The Corporate Axis (Pichai & Kavukcuoglu): This is the operational engine. When Hassabis and Jensen agree on a new breakthrough research direction, Pichai and his new operational chief, Koray Kavukcuoglu, are the ones who write the checks, negotiate the supply chains, and build the actual data centers.
Would you like to look closer at how Google’s internal hardware team (TPUs) competes or cooperates with Nvidia chips, or should we map out how OpenAI handles its own relationship with Jensen Huang?
i see or agree what i dont understand in the middle of guge cganges to ai eg open weights, 6g , whethether wef becomes wai, what is the biggest transformation google needs to stay in middle of those making the decisions on ai
To stay at the very center of global AI decision-making across open weights, next-gen connectivity (6G), and the evolution of the World Wide Web into the World Wide AI (WAI), the single biggest transformation Google must undergo is shifting from an information indexing company to a foundational compute infrastructure and reasoning network.
Google cannot rely on its traditional search monopoly anymore. It must control the physical and digital pipelines that other AI systems rely on to function. [1]
1. Own the Physical "6G + AI" Pipeline
The transition to 6G is not just about faster phone data. It is about edge intelligence—running massive AI models instantly on devices without lag. [1, 2]
- The Transformation: Google must embed its AI models directly into the global telecommunications infrastructure.
- The Decision-Making Power: By combining its global network of subsea cables and data centers with 6G infrastructure, Google aims to become the default "neuromorphic grid" of the planet. If Google owns the pipe through which 6G AI data travels, every other AI company must play by its rules.
2. Shift from the Web (WWW) to the Web of AI (WAI)
The traditional World Wide Web is dying because AI agents are replacing human browsers. People will no longer click links; AI agents will fetch, synthesize, and act on information autonomously. [1, 2, 3, 4, 5]
- The Transformation: Google must pivot from indexing web pages to indexing agentic actions and live reasoning paths.
- The Decision-Making Power: To remain the gatekeeper, Google must establish the universal protocols for how AI agents talk to each other, trade data, and verify facts. If Google defines the standards for WAI (similar to how it shaped web standards via Chrome and Android), it remains the ultimate authority.
3. Monetize the "Open Weights" Ecosystem via Infrastructure
The explosion of open-weights models (like Meta's Llama or Google's own Gemma) means proprietary models are losing their exclusive premium. [1, 2, 3]
- The Transformation: Google must stop trying to win only through closed, secret models. It must transform into the premier, lowest-cost manufacturer of the compute that runs these open models. [1]
- The Decision-Making Power: This is where the Hassabis-Jensen dynamic matters. Google must scale its internal TPU (Tensor Processing Unit) chips to be so efficient that even if an engineer chooses an open-weights model over Gemini, they still choose to run it on Google Cloud because it is cheaper and faster than Nvidia hardware. [1, 2]
The Ultimate Goal: The "Tax" on Intelligence
Google's ultimate survival strategy is to become the foundational layer for everyone else. If Google provides the 6G edge network, defines the WAI communication protocols, and offers the cheapest compute for open-weights models, it won't matter if OpenAI or Anthropic makes a slightly smarter chatbot. Google will effectively collect a "tax" on every piece of artificial intelligence generated on Earth. [1]
Would you like to look closer at how Google's internal TPU chip development aims to break dependency on Nvidia, or should we explore the specific protocols Google is building to control AI agents on the new "WAI"?
- Akkodis on Instagram: "Edge AI is not about machines thinking for themselves. It is about moving computing closer to where data is created so decisions can happen faster, safer, and more reliably. As Terje, CTO, Akkodis Norway, explains, edge AI works within clear design boundaries and human control. Click the link in the bio to learn more #Akkodis #AI"May 15, 2026 — With NVIDIA Jetson, developers can now run powerful AI models directly on devices — faster, smarter, and in real time. 🤖 No massi...Instagram
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ok so google was in mdiile of web, and in middle of mobaile open phones, whats the next middle it needs to gravitate and how is that reltaed to china or taiwan
The next "middle" Google must gravitate toward is the physical foundation of the AI compute stack—specifically custom silicon (TPUs) and cutting-edge semiconductor packaging. [1, 2]
Just as Google owned the digital gateway via Search on the web, and the mobile operating layer via Android, it now needs to own the physical hardware on which global artificial intelligence runs. This transformation ties Google directly into the geopolitical powder keg of Taiwan and China. [1, 2]
1. The Next "Middle": The AI Silicon Layer
In the web era, Google sat in the middle of software. In the AI era, software is being commoditized by open weights, meaning the real bottleneck has moved down to the hardware layer. [1, 2, 3]
- The Goal: Google is rapidly trying to bypass its heavy dependency on Nvidia by scaling its own custom AI chips, called Tensor Processing Units (TPUs). [1, 2]
- The Scale: Google's TPU orders are scaling massively, aiming to build millions of units to power its own data centers and rent out to mega-clients like Meta and Anthropic. [1, 2]
2. The Absolute Dependency on Taiwan
Google cannot print its own silicon. This massive TPU push places Google entirely at the mercy of Taiwan’s semiconductor ecosystem. [1, 2]
- The Manufacturing Bottleneck: Google's latest 3-nanometer TPUs are co-designed and manufactured directly in Taiwan using TSMC (Taiwan Semiconductor Manufacturing Company). [1, 2]
- The Packaging Crisis: The ultimate choke point in AI right now isn't just making the chips; it is advanced packaging (binding high-bandwidth memory to the processor). TSMC’s proprietary packaging capacity (CoWoS) is completely maxed out by Nvidia, creating a severe bottleneck for Google's 2026 TPU targets. [1, 2, 3, 4]
- De-Risking to Intel: To escape this Taiwanese supply chain squeeze, Google made a massive, unexpected shift by booking Intel Foundry to pack millions of its 9th-generation TPUs using Intel's Arizona-based factories. Google is actively trying to pull its hardware supply chain out of the Western Pacific to protect itself. [1, 2, 3]
3. The China Threat and the Dual Tech-Stack
China claims Taiwan as its territory, and the tech landscape is actively splitting into two fiercely incompatible tech ecosystems: a US-aligned stack and a China-aligned stack. [1, 2]
[ US/Google Stack ] [ China/Baidu-Huawei Stack ]
• AI Models: Gemini / OpenAI • AI Models: Ernie / Local LLMs
• Silicon Design: Google TPUs • Silicon Design: Huawei Ascend
• Fabrication: TSMC / Intel Foundry • Fabrication: SMIC / Domestic Fabs
- The Cyber and IP War: Beijing is aggressively using indirect channels to poach Taiwanese semiconductor talent and siphon advanced chip intellectual property to break through US technology containment. [1]
- The Sovereign Divide: Because Google is blocked in mainland China, Chinese tech giants like Baidu and Huawei are building an entirely domestic AI ecosystem. If China successfully weaponizes or disrupts Taiwan's shipping lanes or foundries, Google's ability to manufacture the physical infrastructure for the future of AGI would instantly freeze. [1, 2, 3, 4, 5]
By moving into hardware, Google has graduated from a software battle it could win from California, to a physical resource battle deeply entangled with the geopolitical defense of Taiwan. [1, 2]
Would you like to look closer at how Google’s partnership with Taiwanese designers like MediaTek impacts this strategy, or should we examine how China's domestic AI chips (like Huawei's Ascend) compare to Google's TPUs? [1, 2]
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however gogle disneny and nvidia remain ijn middle of newron robotics??
Yes, your observation is spot on. While Google fights for the sovereign hardware layer, Google, Disney, and Nvidia have formed an alliance to sit at the absolute center of "Physical AI" and neuro-robotics. [1, 2, 3]
Instead of competing, these three giants pooled their unique strengths to solve the hardest problem in robotics: the "sim-to-real" gap—the fact that robots trained in virtual worlds usually fail when facing messy, unpredictable real-world physics. [1]
This collaboration established a powerhouse trifecta that dominates the robotics landscape.
1. The Tri-Party Engine: Project "Newton"
At the March 2026 Nvidia GTC conference, Jensen Huang revealed that Nvidia, Google DeepMind, and Disney Research co-developed an open-source physics engine called Newton. Managed by the Linux Foundation, Newton is the underlying operating layer for training advanced robotic brains. [1, 2, 3, 4]
Each company brings a critical, irreplaceable piece to the table:
- Nvidia provides the "Muscle" (Compute & World-Gen): Nvidia hosts the simulation playground. Newton is built on Nvidia’s Warp framework and runs entirely on their GPUs. This allows a robot to simulate years of physical walking, falling, and balancing in just a few minutes of real time. [1, 2, 3, 4]
- Google DeepMind provides the "Brain" (Neural Dynamics): DeepMind integrates its famous MuJoCo physics simulator into Newton. Google brings the deep reinforcement learning algorithms that allow a robot to auto-correct its balance, learn from a stumble, and problem-solve on its own without human code. [1, 2, 3]
- Disney provides the "Soul" (Expression & Real-World Data): Disney is the ultimate master of mechanical kinesis and fluid movement. Their animators provide the training data. Disney forces the AI to learn how to move with "character," weight distribution, and expressive center-of-gravity controls. [1, 2, 3]
2. The Real-World Proofs: BDX and Olaf
To prove their combined stack works, Disney and Nvidia have been unveiling fully autonomous, free-roaming robotic characters that run on onboard Nvidia Jetson processors: [1, 2]
- The Star Wars BDX Droid: Trained inside the Newton engine, this bi-pedal robot can autonomously walk over loose gravel, shifting desert sand, and uneven rocks, adjusting its balance perfectly in real-time. [1]
- The Olaf Humanoid: At GTC 2026, a real-life, fully autonomous robotic Olaf walked onto the stage to converse with Jensen Huang. Olaf learned his signature movie-accurate "snowman shuffle" entirely inside the virtual simulation using Newton. [1, 2]
The Next Middle: The Common Substrate
By building Newton, Google, Disney, and Nvidia are establishing the universal development platform for physical AI. [1]
Whether a company is building a humanoid factory worker, a delivery drone, or a theme-park droid, they will likely train its "neurological" movement paths using Google's algorithms, inside Nvidia's computing simulator, utilizing mechanical principles pioneered by Disney. They have effectively built the gate through which all future physical robots must pass to learn how to move. [1, 2, 3, 4]
Would you like to look closer at how Google DeepMind's MuJoCo tech integrates with Nvidia's hardware, or should we talk about how other robotics companies (like the $1.4B backed Neura Robotics) fit into this Nvidia-dominated landscape?
lets say that google is like china it doesnt need to make the very best chips as long as its chips are more economical per ai token; so as google is in coopetition with jensen what are its core areas of cooperartion eg ai health
Your analogy is spot on. Much like how China handles its chip constraints by maximizing the scale and cost-efficiency of older or domestic nodes, Google’s strategy with its Tensor Processing Units (TPUs) is not to beat Nvidia in sheer raw peak horsepower per chip. Google’s goal is to win on total cost of ownership (TCO) and economics per AI token. [1, 2]
Because Google operates at such a massive global data center scale, a 20% cheaper token delivery is far more valuable to them than a 10% faster chip. [1]
This creates a highly calculated state of coopetition with Jensen Huang. Google buys tens of billions of dollars in Nvidia Blackwell and Rubin GPUs, while Nvidia relies heavily on Google Cloud. Outside of raw hardware competition, they actively blend their strengths—Google’s peerless software algorithms and Nvidia’s massive compute fabric—to dominate specific industries. [1, 2]
Their deepest core areas of cooperation lie in AI Health, Quantum Computing, and Physical AI (Neuro-Robotics).
1. AI Health: The Ultimate Fusion of Bio-Science and Compute
In the medical space, Google and Nvidia do not compete; they merge. Google DeepMind owns the world’s most advanced biological AI models, while Nvidia owns the industry-standard software platform for healthcare, called Nvidia Clara. [1]
- AlphaFold 3 Commercialization: DeepMind's AlphaFold 3 predicts the structure and interactions of all life's molecules (DNA, RNA, chemical compounds). Google doesn't have a sales force to distribute this to every pharmaceutical lab on earth. Instead, they partner with Nvidia to optimize AlphaFold inside Nvidia's BioNeMo platform, allowing global drug companies to run Google’s models natively on Nvidia infrastructure.
- The Clinical Workspace: While Google builds the specific clinical AI models (like Med-Gemini) to read X-rays and assist doctors, Nvidia builds the microservices (NIMs) that allow hospitals to actually deploy those models securely into legacy medical software. [1, 2]
2. Quantum Computing: Simulating the Impossible
Before true quantum hardware is ready to scale globally, quantum algorithms must be simulated on traditional supercomputers. This is a massive area of technical synergy between the two companies.
- Google's Quantum AI + Nvidia's cuQuantum: Google is a world leader in quantum processor design (like its Sycamore chip). To test and validate these quantum systems, Google plugs its quantum software framework (Cirq) directly into Nvidia's cuQuantum software development kit. [1]
- The Dynamic: Google brings the advanced physics and algorithmic genius; Nvidia brings the massive GPU clusters required to simulate those complex mathematical matrices. [1]
3. Neuro-Robotics & Physical Simulation
As seen with their joint development of the Newton physics engine alongside Disney, both companies realize that physical AI is too massive for a single entity to control. [1]
- Software Standards over Hardware Dominance: Nvidia wants its Omniverse and Isaac platforms to be the operating system for all future factories and robots. Google wants its specialized robotics transformer models (like RT-2 and its successors) to be the "brain." [1]
- The Handshake: By collaborating on open standards like Newton, Google ensures its AI models can natively control any robot trained on Nvidia hardware, while Nvidia ensures that Google doesn't build a competing hardware-software simulator ecosystem that shuts them out.
The Symbiotic Reality
Google and Nvidia are locked in a permanent embrace because they are each other's most valuable counterweights: [1]
[ GOOGLE BRINGS ] [ NVIDIA BRINGS ]
• Algorithmic Breakthroughs • High-Bandwidth GPU Fabrics
• Bio-Molecular AI (AlphaFold) • Enterprise Healthcare Channels (Clara)
• Massive Cloud Footprint & Data • Dominant Simulation Engines (Omniverse)
Google uses Nvidia chips when they need the absolute highest performance for training next-generation frontier models. Nvidia uses Google Cloud as a premier partner to showcase its latest hardware. They fight bitterly for the infrastructure budget of Fortune 500 companies, but in fields like AI health, they are completely inseparable. [1]
Would you like to look closer at how Google's AlphaFold 3 is being commercialized through pharmaceutical partnerships, or should we examine how Google's internal quantum computing timeline relies on Nvidia's simulation tools?
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yes please both how Google's AlphaFold 3 is being commercialized through pharmaceutical partnerships, or should we examine how Google's internal quantum computing timeline relies on Nvidia's simulation tools?
To understand how Google protects its position at the top of the AI decision-making ladder, it helps to look closely at these two massive collaborations.
Both initiatives prove that even if Google focuses on lower-cost, highly economical TPU chips for standard AI tokens, it still relies heavily on Nvidia's specialized hardware and software ecosystems to dominate deep tech. [1]
Part 1: Commercializing AlphaFold 3
While Google DeepMind designs the world's most advanced molecular AI, it lacks the specialized enterprise software channels to sell directly to global pharmaceutical giants. This is where Nvidia steps in to handle the distribution and scaling infrastructure.
The "Isomorphic Labs" Strategy
Google commercializes AlphaFold 3 through its specialized subsidiary, Isomorphic Labs. Led by Demis Hassabis, Isomorphic uses AlphaFold 3 to sign multi-billion-dollar drug discovery partnerships with companies like Eli Lilly and Novartis. [1, 2, 3, 4]
- Google's Role: Google brings the AI design. They use AlphaFold 3 to model how proteins interact with DNA, RNA, and chemical compounds, discovering entirely new molecular structures for drugs. [1, 2, 3, 4, 5]
- The Revenue: Instead of just selling software, Google acts as an AI-driven pharmaceutical co-developer, capturing massive milestone payments and future royalties.
The Nvidia Connection: BioNeMo
To make AlphaFold 3 accessible to the broader biotech industry, Google partners with Nvidia to host and optimize the model on Nvidia BioNeMo (a specialized generative AI platform for drug discovery). [1]
- The Integration: Nvidia optimizes AlphaFold 3 so it runs with maximum efficiency on Nvidia clusters.
- The Microservices (NIMs): Nvidia turns AlphaFold 3 into an enterprise-ready "plug-and-play" microservice (Nvidia Inference Microservice, or NIM). This allows any pharmaceutical company to securely run AlphaFold 3 on their own private servers or cloud infrastructure without exposing sensitive genetic data to the public internet.
Part 2: Google’s Quantum Timeline & Nvidia Simulation
True, fault-tolerant quantum computers are still years away from commercial scale. To build them, Google’s Quantum AI team must constantly test their quantum theories on traditional supercomputers. This creates a massive reliance on Nvidia. [1, 2]
Breaking the Noise Barrier
The biggest hurdle in quantum computing is "noise"—environmental interference that destroys quantum data. Google is working to achieve Quantum Error Correction, using clusters of physical quantum bits (qubits) to create stable, error-free "logical qubits." [1, 2, 3, 4, 5]
Why Google Needs Nvidia’s cuQuantum
To prove that their error-correction math actually works before building the physical hardware, Google must simulate quantum physics on traditional silicon. They use Nvidia's cuQuantum software development kit to make this possible. [1]
- The Computational Strain: Simulating just 30 to 40 qubits requires tracking billions of state combinations simultaneously. Standard chips cannot handle the matrix mathematics.
- The Synergy: Google plugs its open-source quantum programming framework (Cirq) directly into Nvidia’s cuQuantum. Nvidia's software optimizes these massive calculations, allowing Google to run these incredibly complex quantum simulations across thousands of Nvidia tensor-core GPUs at unprecedented speeds. [1, 2]
The Timeline Acceleration
By using Nvidia's massive GPU clusters to simulate quantum workloads, Google can accurately predict how their future quantum processors (like the next generations of their Sycamore chip) will behave. This simulation capability shaves years off Google's physical manufacturing timeline, allowing them to debug quantum algorithms in a virtual environment long before the physical quantum hardware is wired up in the lab. [1, 2]
The Big Picture
These two partnerships illustrate the core of Google and Nvidia's "coopetition":
[ GOOGLE'S GENIUS ] [ NVIDIA'S POWER ]
• AlphaFold 3 (Bio-Design) ────► • BioNeMo & NIMs (Enterprise Delivery)
• Cirq & Sycamore (Quantum) ────► • cuQuantum (Massive GPU Simulation)
Google creates the profound scientific breakthroughs, but Nvidia builds the specialized computational engines and software bridges required to make those breakthroughs useful to the rest of the commercial world. [1, 2]
Would you like to explore how Isomorphic Labs' current drug discovery trials are progressing, or should we look at the exact year Google expects to hit commercial Quantum Supremacy using this Nvidia-accelerated timeline?
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