Long-time DeepMind researcher David Silver is raising one billion dollars for his London-based AI start-up Ineffable Intelligence, the largest seed round in European start-up history. Instead of training on internet text like today’s LLMs, Silver is betting on reinforcement learning in simulated environments to build an “endlessly learning superintelligence.”
Chinese AI labs keep shipping new models at a rapid clip. Today it’s Alibaba’s turn with Qwen3.5, which tries to match top Western models using a hybrid architecture that combines linear attention and mixture-of-experts while keeping just 17 billion parameters active per query. And yes, it’s open weight.
Anthropic is discussing building at least 10 gigawatts of data center capacity worth hundreds of billions of dollars, recruiting ex-Google managers and lining up Google as a financial backer to make it happen.
Google Deepmind has upgraded its specialized thinking mode "Gemini 3 Deep Think" and made it available through the Gemini app and as an API via a Vertex AI early access program. The upgrade targets complex tasks in science, research, and engineering.
Google AI Ultra subscribers can access Deep Think through the Gemini app, while developers and researchers can sign up separately for the API program. According to Google Deepmind, the model tops several major benchmarks:
| Benchmark | Deep Think | Claude Opus 4.6 | GPT-5.2 | Gemini 3 Pro Preview |
|---|---|---|---|---|
| ARC-AGI-2 (Logical reasoning) | 84.6% | 68.8% | 52.9% | 31.1% |
| Humanity's Last Exam (Academic reasoning) | 48.4% | 40.0% | 34.5% | 37.5% |
| MMMU-Pro (Multimodal reasoning) | 81.5% | 73.9% | 79.5% | 81.0% |
| Codeforces (Coding/algorithms, Elo) | 3,455 | 2,352 | - | 2,512 |
While Deep Think dominates in logic and coding, the gap narrows significantly on MMMU-Pro: it scored 81.5 percent, barely ahead of Gemini 3 Pro Preview at 81.0 percent. This suggests the thinking upgrades focus heavily on abstract reasoning rather than visual processing. Deep Think also achieved gold medal-level results at the 2025 Physics and Chemistry Olympiads. Examples of the model in scientific use can be found here.
Isomorphic Labs, Google DeepMind's AI medicine startup, has unveiled a new system called "Isomorphic Labs Drug Design Engine" (IsoDDE) that it says outperforms AlphaFold 3. According to the company, IsoDDE doubles AlphaFold 3's accuracy when predicting protein-ligand structures that differ significantly from the training data (see left graph below).
Beyond structure prediction, IsoDDE can identify previously unknown docking sites on proteins in seconds based solely on their blueprint, with accuracy that Isomorphic Labs says approaches that of lab experiments. Isomorphic Labs also claims the system can estimate how strongly a drug binds to its target at a fraction of the time and cost of traditional methods. These capabilities could uncover new starting points for active compounds and speed up computational screening.
Isomorphic Labs says it already uses IsoDDE daily in its own research programs to develop new drug candidates. Details are available in the company's technical report.
A new benchmark called WorldVQA tests whether multimodal AI models actually recognize what they see or just make it up. Even the best performer, Gemini 3 Pro, tops out at 47.4 percent when asked for specific details like exact species or product names instead of generic labels. Worse, the models are convinced they’re right even when they’re wrong.
New research reveals that reasoning models like Deepseek-R1 simulate entire teams of experts when solving problems: some extraverted, some neurotic, all conscientious. This internal debate doesn’t just look like teamwork. It measurably boosts performance.
