Demis Hassabis
Co-founder and CEO of Google DeepMind. He is mentioned in connection with Gemini 3.5 Flash and Google’s model launch.
Key Highlights
- Demis Hassabis is the co-founder and CEO of Google DeepMind and a central figure behind many of Google's most important AI launches.
- Recent mentions tie him to Gemini 3.5 Flash, Gemini Omni, Gemini 3.1 Flash TTS, Gemma 4, and broader Gemini product evolution.
- His public roadmap emphasizes low-latency multimodal AI, real-time voice, memory-rich agents, robotics, and scientific discovery.
- For AI PMs, Hassabis is a useful signal for where frontier AI research is becoming deployable product infrastructure.
- His work links foundational breakthroughs like AlphaGo and AlphaFold to commercial AI products and enterprise platforms such as Vertex AI.
Demis Hassabis
Overview
Demis Hassabis is the co-founder and CEO of Google DeepMind, and one of the most influential leaders shaping the direction of modern AI products. In the newsletter coverage, he appears as a key public face behind major Google AI and DeepMind launches spanning Gemini model updates, multimodal systems, text-to-speech, robotics, scientific AI, and long-term AGI strategy.For AI Product Managers, Hassabis matters because he sits at the intersection of frontier research and productization. His mentions connect breakthrough research programs like AlphaGo, AlphaGo Zero, and AlphaFold with commercial model launches such as Gemini 3.1 Flash Live, Gemini 3.1 Flash TTS, Gemini Omni, and Gemini 3.5 Flash. Tracking his announcements helps PMs understand where Google is investing across latency, multimodality, agent UX, scientific discovery, robotics, and safe AGI deployment.
Key Developments
- 2026-03-20: Demis Hassabis introduced Google Labs' Stitch, a "vibe design" platform that turns natural-language prompts into high-fidelity UI designs with rapid iteration and voice collaboration.
- 2026-03-27: He was mentioned in connection with the launch of Gemini 3.1 Flash Live, an audio model focused on more natural conversations and improved function calling.
- 2026-03-28: Hassabis announced a desktop import feature in Gemini, enabling users to migrate preferences and chat history from other AI apps.
- 2026-04-03: He was among the figures tied to Google DeepMind's Gemma 4 launch, highlighting Google's open-model strategy for reasoning and agentic workflows.
- 2026-04-17: Hassabis unveiled Gemini 3.1 Flash TTS, positioned as Google's most expressive and steerable text-to-speech model, available through the Gemini API, Google AI Studio, and Vertex AI preview access.
- 2026-05-01: Garry Tan interviewed Hassabis about DeepMind's playbook for turning research breakthroughs into products, including lessons from AlphaGo Zero and AlphaFold and approaches to safely scaling toward AGI.
- 2026-05-02: Hassabis recapped DeepMind's AGI milestones, linking AlphaGo, AlphaFold, and Gemini multimodal systems, and pointed to agents with memory and continual learning as the next frontier.
- 2026-05-13: He secured $2.1B in new funding for Isomorphic Labs, aimed at accelerating AI-driven drug discovery built on AlphaFold with an ambitious disease-solving vision.
- 2026-05-20: Hassabis unveiled Gemini Omni, a multimodal model that can ingest photo, video, and audio inputs to generate and iteratively edit new scenes.
- 2026-05-21: He was cited alongside the launch of Gemini 3.5 Flash, an optimized model variant built for faster, low-latency inference.
- 2026-05-21: Hassabis specifically unveiled Gemini 3.5 Flash as a compact LLM using Flash Attention for sub-second inference and lower GPU memory usage, with availability on Google Cloud Vertex AI.
Relevance to AI PMs
1. Signal for Google roadmap priorities: Hassabis's announcements consistently point to where Google sees product value next: low-latency inference, multimodal interaction, voice, robotics, scientific tooling, and memory-rich agents. PMs can use this to benchmark their own roadmap assumptions.2. Research-to-product pattern recognition: His work shows how frontier research can become usable product surfaces. AlphaFold to Isomorphic Labs, and Gemini research to API and Vertex AI launches, are useful case studies for PMs deciding when a capability is mature enough for commercialization.
3. Practical product design implications: Many of the launches tied to Hassabis are directly relevant to product decisions, including migration/import UX, real-time audio interfaces, steerable TTS, multimodal editing, and cost-efficient low-latency serving. PMs building AI apps can translate these into feature prioritization, packaging, and platform choices.
Related
- Google DeepMind: Hassabis is co-founder and CEO; this is the core organization behind many of the launches mentioned.
- Gemini / Gemini 3 / Gemini 3.1 Flash Live / Gemini 3.1 Flash TTS / Gemini 3.5 Flash / Gemini Omni: These model families and variants are central to Hassabis's recent product and research announcements.
- Gemma 4: Connects Hassabis to Google's open-model strategy for on-device and agentic use cases.
- Google AI, Google Labs, Google Cloud, Vertex AI: These are the distribution and product channels through which many DeepMind capabilities reach developers and enterprises.
- AlphaGo, AlphaGo Zero, AlphaFold: These landmark DeepMind breakthroughs are frequently used to frame Hassabis's broader AGI and commercialization narrative.
- Isomorphic Labs: Hassabis also leads this AI drug discovery effort, extending his influence from general AI into biotech and healthcare.
- Sundar Pichai, Jeff Dean, Josh Woodward: Key Google leaders connected to the broader ecosystem in which Hassabis operates.
- Sebastian Raschka, Simon Willison, Philipp Schmid, Garry Tan: Analysts, builders, and interviewers who helped amplify or contextualize launches associated with Hassabis.
- Boston Dynamics / Gemini Robotics: These references connect Hassabis's work to embodied AI and robotics applications.
- AGI, climate change, disease, World Economic Forum, CNBCi: Themes and forums associated with Hassabis's public positioning around AI's long-term societal and scientific impact.
Newsletter Mentions (21)
“Also covered by: @Demis Hassabis”
#2 𝕏 Google DeepMind launched Gemini 3.5 Flash, an optimized edition of its large language model engineered for faster, low-latency inference. Also covered by: @Demis Hassabis #3 𝕏 Google AI launched Gemini for Science, a suite of AI-powered tools and experiments to accelerate scientific discovery by connecting and analyzing massive datasets. It’s designed to scale research workflows, speeding up hypothesis testing and insight generation. #4 𝕏 LlamaIndex 🦙 built a 600-line Next.js demo agent using LiteParse (no vector DB) to ingest SEC filings and answer questions with exact citations highlighted on the original PDF pages. It tackles the ~70% of analysts’ time currently spent pulling numbers from PDFs. #5 𝕏 Summary: OpenAI highlights AI’s emerging skill in sustaining complex, cross-domain reasoning to accelerate breakthroughs in biology, physics, engineering and medicine. #6 𝕏 Cursor launched multi-repository support for automations, letting agents reason across codebases to automatically execute, test, and verify tasks. #7 𝕏 Philipp Schmid launched an API that spins up an isolated Linux sandbox for Gemini to reason, run code, browse the web, and manage files in a single call. #8 𝕏 Harrison Chase launched Code Interpreter, a lightweight code execution environment for running RLMs and programmatic tool calls (and more) without spinning up a full sandbox. #9 𝕏 Santiago presents AG-UI, an open, event-based protocol that standardizes agent↔user communication for long-running, nondeterministic tasks—eliminating the need for custom glue code. #10 𝕏 Teresa Torres outlines how to build Claude-powered AI agents—defining identity, scheduler, tasks, and scripts—to automate prep work, follow-ups, and weekly reviews on custom schedules. #11 📝 PromptLayer Blog Best prompt management platforms — Features, comparisons, and recommendations - As teams move from experimental prompting to production-grade AI, they face an infrastructure gap managing prompt versions, models, environments, and changes. The article outlines that gap and compares platform features to help teams choose. #12 𝕏 Andrew Ng launched a short course with Google Cloud on building self-evaluating AI agents for image and video generation, teaching three evaluation techniques—image-text similarity scoring, LLM judges for custom criteria, and structured rubrics. #13 𝕏 Julien Chaumond launched Hugging Face Hardware, a community-driven dashboard revealing the real-world GPUs & CPUs powering open-source AI, plus VRAM distribution and inference hardware trends. #14 𝕏 Sebastian Raschka flags a new LLM parallel block design that matches vanilla transformer performance while delivering significantly higher throughput. #15 in Guillermo Rauch launched the AI Gateway plugin for WordPress, bringing any AI model or provider—covering text, image, video, and audio—to 42% of the web. #16 𝕏 claire vo 🖤 notes that Anthropic has locked down enterprises on Claude en masse, but warns vendor lock-in slows you from seeing the real frontier. Cutting-edge builders instead hop between OpenAI’s Codex, CoWork, and AI Studio to stay fast, flexible, and impactful. #17 𝕏 clem 🤗 celebrates Cohere’s release of the Apache 2.0-licensed “command-a-plus-05-2026-bf16” model on Hugging Face, highlighting their strong open-source momentum. #18 𝕏 clem 🤗 built and released Carbon: a frontier DNA base model with open weights, training code, and data pipeline. It’s 275× faster than the next-best model, runs locally on a laptop, and can process a whole human genome on a single GPU. #19 𝕏 claire vo 🖤 recaps her favorite #GoogleIO launches—Antigravity, Gemini, AI Studio, Flow, Omni, Stitch, Pomelli, and more—for engineers and designers. She also shares hands-on trials (and occasional failures) with the new tools. #20 𝕏 Peter Yang stresses that teams should “just try a lot and build to learn,” running 3–4 rapid iterations to discover what works, and stick to 90–120-day roadmaps instead of year-long plans. #21 𝕏 Sam Altman spotlights AGI’s three key impacts—accelerating research, powering companies, and personal AI—celebrates the “unit distance” breakthrough and offers $2M in OpenAI credits to every YC company. He urges ramping up personal AGI to help individuals achieve their goals. Also covered by: @Sam Altman , @OpenAI #22 𝕏 Logan Kilpatrick calls Gemini 3.5 the start of a new era after 2½ years of building its infrastructure, products, and team, and emphasizes that “the model is the product,” urging users to keep the feedback coming. #23 𝕏 Demis Hassabis unveils Gemini 3.5 Flash, a compact LLM using Flash Attention for sub-second inference and reduced GPU memory footprint, now available on Google Cloud’s Vertex AI. Also covered by: @Demis Hassabis #24 📝 Surge AI Blog Slop is a choice. Introducing Antidote. - Antidote is an evaluation framework that emphasizes expert human reviewers who read and grade AI outputs to push model evaluation beyond superficial or automated metrics. Its goal is to reduce low-quality "slop" by relying on human judgment and nuance. #25 𝕏 Boris Cherny revamped the usage UI so you can now run `/usage` to see exactly which calls are consuming your tokens. Found this valuable? Share it with another PM - they can subscribe at genaipm.com Unsubscribe • Switch to Weekly
“Demis Hassabis unveiled Gemini Omni, a multimodal AI that ingests photos, video, and audio to generate and iteratively edit entirely new scenes.”
#19 𝕏 Demis Hassabis unveiled Gemini Omni, a multimodal AI that ingests photos, video, and audio to generate and iteratively edit entirely new scenes. It starts with video support and will soon handle any input/output format.
“#6 𝕏 Demis Hassabis secured $2.1 B in new funding for Isomorphic Labs. The investment will turbocharge its AI-driven drug discovery platform—built on AlphaFold—to one day solve all diseases.”
#6 𝕏 Demis Hassabis secured $2.1 B in new funding for Isomorphic Labs. The investment will turbocharge its AI-driven drug discovery platform—built on AlphaFold—to one day solve all diseases.
“Demis Hassabis recapped DeepMind’s AGI milestones — from AlphaGo’s Go victories and AlphaFold’s protein-folding breakthroughs to the new Gemini multimodal models — and emphasized agents with memory and continual learning as the next frontier.”
Demis Hassabis recapped DeepMind’s AGI milestones — from AlphaGo’s Go victories and AlphaFold’s protein-folding breakthroughs to the new Gemini multimodal models — and emphasized agents with memory and continual learning as the next frontier.
“Garry Tan sits down with Demis Hassabis to unpack DeepMind’s playbook for turning research breakthroughs (AlphaGo Zero, AlphaFold) into real-world products and charting strategies for safely scaling toward AGI.”
#13 𝕏 Garry Tan sits down with Demis Hassabis to unpack DeepMind’s playbook for turning research breakthroughs (AlphaGo Zero, AlphaFold) into real-world products and charting strategies for safely scaling toward AGI.
“#4 𝕏 Demis Hassabis unveiled Gemini 3.1 Flash TTS, Google’s most expressive and steerable text-to-speech model offering granular control over AI-generated voice; it’s available in preview today via the Gemini API and Google AI Studio, with enterprise access on Vertex AI.”
GenAI PM Daily April 17, 2026 GenAI PM Daily 🎧 Listen to this brief 3 min listen Today's top 25 insights for PM Builders, ranked by relevance from Blogs, X, LinkedIn, and YouTube. OpenAI Launches Codex for (Almost) Everything #1 📝 OpenAI News Codex for (almost) everything - OpenAI announces Codex for a wide range of uses, positioning Codex as a versatile product for many tasks. The post highlights product-focused capabilities and availability. #2 𝕏 Mike Krieger directs PMs to Anthropic’s follow-up blog on Claude Opus 4.7, outlining performance boosts, enhanced safety guardrails, and expanded multimodal capabilities. Let us know what you think! Also covered by: @Simon Willison , @LlamaIndex 🦙 , @Cursor , @v0 , @Mike Krieger , @Dharmesh Shah #3 𝕏 Qwen launched the open-source Qwen3.6-35B-A3B, an Apache 2.0–licensed sparse MoE model with 35B total (3B active) parameters. It matches coding performance of models 10× its active size and offers strong multimodal perception, reasoning, and dual thinking modes. #4 𝕏 Demis Hassabis unveiled Gemini 3.1 Flash TTS, Google’s most expressive and steerable text-to-speech model offering granular control over AI-generated voice; it’s available in preview today via the Gemini API and Google AI Studio, with enterprise access on Vertex AI. #5 📝 OpenAI News Introducing GPT-Rosalind for life sciences research - OpenAI introduces GPT-Rosalind, a model tailored for life sciences research to support domain-specific scientific workflows. The announcement emphasizes research applications and potential benefits for scientific discovery. Also covered by: @Kevin Weil #6 in Guillermo Rauch launched Workflow SDK, a framework that brings SQS/Kafka-style durability to AI agent backends—automatically handling LLM downtime, rate limits and database hiccups without the ops complexity and with self-hosting plus multi-environment support. #7 𝕏 Google Research launched YouTube AI Search (YouTube Ask on TV), enabling users to ask complex questions and hold iterative conversations to refine video results; catch the live demo at the Google booth at 10:30 AM #CHI2026. #8 𝕏 Google DeepMind built a bridge between Gemini Robotics ER and Spot’s system, letting the AI use plain English to move the robot, take photos, and grab objects for more complex tasks. #9 𝕏 Teresa Torres highlights Doist’s new Ramble feature in Todoist: a pure-AI voice-to-task pipeline built on Gemini live audio, dynamic tool calls and automated evals, validated through user research in five languages and primed for future multimodal support. #10 in Hannah Stulberg walked through how her team at DoorDash uses a shared GitHub repo called Team OS to centralize customer call summaries, metric definitions, PRDs and research so any coding agent can assist across product, design, analytics and engineering. #11 𝕏 Philipp Schmid built a voice-enabled Telegram bot in ~400 lines of Python using the Gemini Interactions API—leveraging Gemini 3. #12 𝕏 LlamaIndex 🦙 added LiteParse—4.3K+ GitHub stars, zero-cloud parsing at 500 pages/2 s across 50+ formats—to its ecosystem, now powering agents like Claude Code and Cursor. #13 📝 Claude Code Blog Best practices for using Claude Opus 4.7 with Claude Code - Practical guidance for using the Claude Opus 4.7 model inside Claude Code, covering recommended patterns, configuration tips, and usage best practices to optimize developer workflows when coding with Claude. Also covered by: @Simon Willison , @LlamaIndex 🦙 , @Cursor , @v0 , @Mike Krieger , @Dharmesh Shah #14 ▶️ New course! Spec-Driven Development Deeplearning.ai The video announces a free spec-driven development course by Deeplearning.ai and JetBrains, taught by Paul Everitt, covering how to write markdown-based specifications for AI agents to generate code and build the Agent Clinic web application. The course is built in partnership with JetBrains, taught by Developer Advocate Paul Everitt, and available for free enrollment at https://bit.ly/4toWsIY. Spec-driven development begins with a markdown file or long prompt that precisely defines functionality for AI agents to implement, reducing hallucination and context rot. Participants will construct "Agent Clinic," a fully featured web application where AI agents can diagnose and address problems like hallucination and context rot. #15 𝕏 Google Research unveiled Simula, a framework that reframes synthetic data generation as dataset-level mechanism design, using reasoning from first principles to offer fine-grained control over coverage, complexity, and quality. #16 𝕏 Sam Altman announced major Codex improvements, including a macOS computer-use feature that lets the AI leverage all your Mac apps in parallel without disrupting your work. He also highlighted new plugin integrations to broaden its functionality. #17 📝 Simon Willison Qwen3.6-35B-A3B on my laptop drew me a better pelican than Claude Opus 4.7 - A comparison of pelican drawings produced by Qwen3.6-35B-A3B (Alibaba) and Claude Opus 4.7, with Qwen producing a markedly better pelican on the author's local machine. #18 𝕏 OpenAI launched GPT-Rosalind, its Life Sciences model series, as a research preview via ChatGPT, Codex, and the API for qualified partners including Amgen, Moderna, the Allen Institute, and Thermo Fisher Scientific. Also covered by: @Kevin Weil #19 𝕏 Kevin Weil clarifies that the Rosalind bio/drug discovery model’s enterprise and education partnerships strictly exclude their data from any training processes to ensure customer data protection. #20 𝕏 DeepLearning.AI previews AI Dev 26, where Andrew Ng outlines how AI is transforming software engineering workflows, skill sets, and future job roles. #21 𝕏 OpenAI notes that the US drug discovery-to-approval process takes 10–15 years on average. Advanced AI systems can accelerate this by boosting research efficiency, uncovering hidden connections, and helping scientists form stronger hypotheses faster. #22 𝕏 Cursor finds that as AI code generation improves, developers’ roles shift to managing that output—documentation (+62%), architecture (+52%), code review (+51%) and learning (+50%) are booming versus just 15% growth in UI/styling. #23 𝕏 Philipp Schmid breaks down bot audio costs, showing that at ~25 tokens/sec, 60 seconds of speech runs about $0.03. #24 𝕏 Google DeepMind partnered with @BostonDynamics to power Spot with Gemini Robotics embodied reasoning models. This enables the robot to better understand its surroundings, identify objects and carry out simple commands like tidying up a room. #25 𝕏 Demis Hassabis shares a dev.to prompt guide for Google AI’s new Gemini 3.1 text-to-speech model, walking through step-by-step techniques to craft prompts that maximize voice output quality. Found this valuable? Share it with another PM - they can subscribe at genaipm.com Unsubscribe • Switch to Weekly
“Also covered by: @Sebastian Raschka , @Simon Willison , @Philipp Schmid , @Jeff Dean , @Google DeepMind , @Demis Hassabis , @Demis Hassabis , @Sebastian Raschka”
Google DeepMind Releases Gemma 4 Open Models #1 𝕏 Google DeepMind launched Gemma 4, a family of Apache 2.0–licensed open models you can run on your own hardware for advanced reasoning and agentic workflows. Also covered by: @Sebastian Raschka , @Simon Willison , @Philipp Schmid , @Jeff Dean , @Google DeepMind , @Demis Hassabis , @Demis Hassabis , @Sebastian Raschka #2 𝕏 Qwen unveiled Qwen3.6-Plus, a next-gen multimodal agentic model with smarter, faster coding execution, sharper vision reasoning and a 1M-token context window by default via API, all while maintaining top-tier general performance.
“#11 𝕏 Demis Hassabis launched a desktop import feature in Gemini that lets users transfer their preferences and chat history from other AI apps in just a few clicks.”
#11 𝕏 Demis Hassabis launched a desktop import feature in Gemini that lets users transfer their preferences and chat history from other AI apps in just a few clicks.
“Also covered by: @Demis Hassabis , @Philipp Schmid , @Google AI , @Google AI , @Sundar Pichai , @Sundar Pichai”
#1 𝕏 Google DeepMind launched Gemini 3.1 Flash Live, an audio model that delivers more natural conversations with improved function calling for more useful, informed interactions. Also covered by: @Demis Hassabis , @Philipp Schmid , @Google AI , @Google AI , @Sundar Pichai , @Sundar Pichai
“Demis Hassabis introduces Google Labs’ Stitch “vibe design” platform for converting natural-language prompts into high-fidelity UI designs with rapid iteration and voice collaboration.”
#21 𝕏 Demis Hassabis introduces Google Labs’ Stitch “vibe design” platform for converting natural-language prompts into high-fidelity UI designs with rapid iteration and voice collaboration. #22 𝕏 Lenny Rachitsky warns that engineers now spend a growing share of their time deciding “how many resources should we put on this project,” making compute budgeting a core part of the job.
Related
Independent AI commentator and developer known for practical analysis of LLM products. Here he argues Anthropic and OpenAI have found product-market fit.
A Google AI/Developer Relations figure mentioned for demonstrating Gemini Managed Agents and the Interactions API. He appears here as a presenter explaining hosted sandboxed agent execution.
Google's frontier AI lab. The newsletter references a Google Research privacy approach and Google I/O 2026 announcements, which are adjacent to DeepMind's broader ecosystem.
Google's AI assistant/model family mentioned as one of the systems that can answer category-level brand questions. It is presented alongside ChatGPT and Perplexity in the context of AI-driven visibility.
An ML researcher and writer mentioned for highlighting Gated DeltaNet-2 and sharing a primer on Gated DeltaNet. Relevant for technical AI architecture discussion.
A major AI platform and product company shipping Gemini models, Search AI features, and developer tools. Important for AI PMs because many of the newsletter’s launches reflect Google’s evolving AI ecosystem.
President and CEO of Y Combinator. In this newsletter he argues that AI builders should focus on automating repetitive tasks and that startups need specific lived insight.
Google AI leader and notable voice in model launches and research updates. Mentioned here in connection with Gemini 3.5 Flash and Google’s AI releases.
CEO of Google and Alphabet mentioned in the context of Google I/O and Gemini strategy. The newsletter cites him in a discussion about AI roadmap and product direction.
Google’s AI organization focused on models, tooling, and scientific applications. The newsletter mentions its Gemini for Science suite for research acceleration.
A model name referenced as part of a survey of recent LLM architectures. It is notable here as an example of the current pace of model iteration and architecture experimentation.
A Google product leader mentioned introducing Product Catalogs in Pomelli. Relevant to PMs for marketing automation and product-led growth tools.
Google’s consumer Gemini application, described here as serving a massive user base with an opinionated UX. It is contrasted against AI Studio’s developer-oriented defaults.
A Gemini model variant used here to power agentic workflow examples and multi-agent systems. It is relevant to AI PMs as an example of frontier model capability enabling more complex automated workflows.
Google Cloud’s managed AI platform for deploying and serving models. It is mentioned as the availability layer for Gemini 3.5 Flash.
A Gemini model variant highlighted for strong cost-per-intelligence performance. The newsletter frames it as especially efficient for simulated store operations on Vending Bench.
A Gemini model variant that was noted as moving out of preview status.
Google’s cloud platform offering infrastructure and model hosting. In this newsletter it appears in a course with Andrew Ng and with Gemini 3.5 Flash on Vertex AI.
DeepMind’s landmark Go-playing system, referenced as one of its AGI milestones.
A Google Labs AI product for design. It is positioned as a creative product-making tool in Google’s experimental portfolio.
Google’s video generation model with updates to portrait mode, visual consistency, and higher-resolution upscaling.
A Google AI text-to-speech model with native multi-speaker dialogue support across many languages. It is positioned as part of the Gemini product family.
Google’s experimental AI product incubator. The newsletter highlights a set of new Labs products across marketing, design, 3D, video, and research.
Google's email product, referenced here as gaining Gemini-powered AI Inbox and Overviews features. For PMs, it is an example of AI being embedded into a mature productivity workflow.
A robotics company that embedded Google DeepMind’s Gemini Robotics model into its Spot robot. It is relevant here as a deployer of embodied AI in real-world hardware.
Boston Dynamics’ humanoid robot platform. The newsletter references it as part of a robotics research partnership with Google DeepMind.
AGI is referenced as the frontier toward which current AI development is moving. In PM terms, it frames long-term product strategy, governance, and risk discussions.
A robotics model from Google DeepMind focused on embodied reasoning and multi-view environment understanding. Relevant to AI PMs building robotics or agentic systems with physical-world tasks.
A family of open translation models from Google DeepMind supporting 55 languages. For AI PMs, it highlights on-device, low-latency translation as a product direction.
An AI-driven drug discovery company building on AlphaFold. In this newsletter it is highlighted for securing major new funding.
DeepMind’s protein-structure prediction model and platform. It is referenced here as the foundation for Isomorphic Labs’ drug discovery work.
Stay updated on Demis Hassabis
Get curated AI PM insights delivered daily — covering this and 1,000+ other sources.
Subscribe Free