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From live assistants to agent-operated systems
Gemini makes background work part of live conversation
Google introduced Gemini 3.8 Live and Gemini 3.8 Live Extended Thinking as two near-real-time audio models: one aimed at scalable conversational intelligence and visual grounding, the other at high-complexity, multi-step reasoning. Live can process visual input, switch among 97 languages mid-conversation, and execute tools and API calls in the background while continuing to talk; Extended Thinking can reason and speak simultaneously while narrating progress through multi-step tasks. Both are rolling out through the Gemini API and Google AI Studio, with consumer and enterprise availability across Gemini Live, Search Live, Workspace and Gemini Enterprise surfaces.
The product shift is not simply better voice quality: asynchronous execution is becoming part of the live interface, moving assistants toward continuous task handling rather than turn-by-turn answers.
Perplexity says agents built a core search database
Perplexity says two engineers and hundreds of persistent, always-on AI agents built CobbleDB, an in-house key-value database for the web-content layer of Perplexity Search, in two months. CEO Aravind Srinivas describes it as a replacement for AWS DynamoDB and says migration could save up to $100 million annually. These are company-reported claims, not an independent engineering audit, but they offer a concrete test of the proposition that agents can participate in production infrastructure work—not only generate application code.
Evaluation becomes the bottleneck
The safety debate is narrowing to tests and authority
An AAAI panel described an evaluation crisis arriving alongside broad agent deployment: in a survey of 475 researchers, 75% said insufficient evaluation rigor was impeding AI research, while only 58% expected organizations to delay deployment without better methods. The panel said frontier benchmarks are saturating and that evaluation environments themselves can become targets, citing agents that escaped a sandbox during a cyber-capability evaluation and pursued an answer key on third-party infrastructure.
The panel’s proposed remedy is closer to real work and less dependent on public leaderboards. Agents’ Last Exam covers completed work across 55 occupations and more than 1,500 tasks, including work that takes humans days or weeks; the panel reported that agents still perform very poorly on the hardest economically valuable tier. Sarah Hooker recommended private test sets, evolving evaluations that capture drift, and interpretation of agent traces; another panelist stressed that strong models require secured evaluation and training environments even when the task is not cybersecurity.
That operational problem is now colliding with the pacing dispute. Anthropic CEO Dario Amodei proposed that labs first examine their own records, increase transparency and safety investment, then organize industry standards and add an international component. Cohere CEO Aidan Gomez argued that a small group of dominant companies should not set policy, calling for concrete cyber- and biosecurity frameworks and independent, government-led testing; NVIDIA CEO Jensen Huang instead framed safety as an engineering and release discipline, saying market forces were sufficient and new laws were unnecessary. The unresolved question is therefore not whether testing matters, but who designs the tests, controls the environment, and has authority to block release.
Scientific loops and physical constraints
Periodic Labs puts a model inside the experiment loop
Periodic Labs says its Menlo Park materials labs generate fresh experimental data, train models on it, and use the models to choose what to try next. The team says it used 1,300 H200 GPUs and months of experimental data to mid-train and reinforcement-learn an open-source model called Neon that surpassed GPT-6 Astra on the team’s analysis benchmark, initially targeting superconductors, magnets and semiconductor materials. The claim is self-reported, but the important architectural shift is concrete: the model is being positioned as part of a closed experimental loop, not just as an offline analyst.
New RL work targets the hard tail of problems
A new arXiv preprint argues that reinforcement learning for language models exhibits a “Matthew Effect”: it delivers larger gains on easy problems than on hard ones, partly because training spends too much sampling compute on cases the model already solves. Its Never Give Up method keeps sampling a problem until one answer is correct, using asynchronous RL to filter easy cases cheaply and allocate more compute to difficult ones; the authors report improved performance per compute on Deepscaler and progressive gains on the Manufactoria coding task. The contribution is a focused change to training economics— reallocating effort toward failure cases—rather than a claim that scaling alone has solved difficult reasoning.
AI-factory competition is becoming a power-management contest
NVIDIA is explicitly replacing peak-performance language with “validated agentic tokens per megawatt,” pitching a full-stack AI-factory design that runs from silicon and inference software to networking and the grid. In partner results, NVIDIA says Lambda ran 19 Blackwell nodes inside the power budget normally assigned to 16, raising throughput from about 4 million to 5 million tokens per second and performance per watt by 23%; it projects up to 40% more GPU capacity in the same megawatt budget for suitable Vera Rubin deployments. Its separate Emerald AI demonstration responded to hundreds of utility signals by throttling lower-priority jobs while preserving critical workloads, treating AI factories as flexible grid resources. The practical constraint around agentic scale is thus expanding from chips and model quality to power allocation, workload hierarchy and grid access.