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ived a major update with performance increases on a timeline that is like o1 - o3. We firmly believe GPT-5, Claude 4, and Grok 4 are considerably ahead of R1 though, especially in agentic tasks. However, problems in AI are solvable by two things: talent and compute. DeepSeek has always had the former, now they will have more of the latter. We expect DeepSeek's rate of progress to materially increase as they receive substantial volumes of any chip that gets into China. DeepSeek has ambitions to r

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rkload needs and size of workload. It also depends on how often the workload is cycled as frequently cycled KVs are not a good fit for the limited write/rewrite tolerance of NAND. Agentic use cases that use tool calling to pull in documents and data at very low latency and high cycle rates today further push caching from NVMe to DDR. These are architectural and user experience tradeoffs to consider, rather than direct substitutes to each other. As use cases evolve, there may be different hardwar

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rd party integrator might be used and charge up to hundreds of thousands for deployment. Most of the time stop-gap fixes were used to sync the systems instead, like GUI automation agents, a program that merely emulates a human clicking the right buttons. Because the robots needed a full cell installation, integrators chose ideal, cheaper spots, such as a pick and place station between two horizontal conveyors. Difficult locations, like the vertical putwalls, were skipped, because most robots lea

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order to enlarge your (human) understanding of the design space. If you can pick just one, go for Gemini 2.5 PRO. The fundamental requirement for the LLM to be used is: don’t use agents or things like editor with integrated coding agents. You want to: Always show things to the most able model, the frontier LLM itself. Avoid any RAG that will show only part of the code / context to the LLM. This destroys LLMs performance. You must be in control of what the LLM can see when providing a reply. Alw

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an internal hackathon and Matt Russell knew exactly what he wanted to work on. Russell, a staff engineer at the seven-year-old financial technology company, was aware that coding agents were advancing rapidly and he wanted to see what they could do for Mercury. He and his hackathon team created a tool that allowed them to assign issues in Linear to third-party agents, which would return a pull request for engineers to review like any other PR. Almost immediately they saw the potential. The proj

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lity can create a hugely positive impact; a small misalignment multiplied by hundreds of millions of people can cause a great deal of negative impact. 2025 has seen the arrival of agents that can do real cognitive work; writing computer code will never be the same. 2026 will likely see the arrival of systems that can figure out novel insights. 2027 may see the arrival of robots that can do tasks in the real world. A lot more people will be able to create software, and art. But the world wants a

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ronment, and priors. For a long time, RL researchers focused mostly on the algorithm (e.g. REINFORCE, DQN, TD-learning, actor-critic, PPO, TRPO…) – the intellectual core of how an agent learns – while treating the environment and priors as fixed or minimal. For example, Sutton and Barto’s classical textbook is all about algorithms and almost nothing about environments or priors. However, in the era of deep RL, it became clear that environments matter a lot empirically: an algorithm’s performance

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ucture, context, and a specialized environment for specific workflows. AI doesn’t replace the workbench, it’s a powerful new tool to place on top of it. As we move forward to more agentic workflows, the workbench becomes even more important. It will be the place where AI agents live and operate. The place where agents are deployed and managed. Where agents operate with clear guidelines and output is reviewed and approved. Humans stay in the loop, and design still has a massive role to visualize

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iction anymore - it’s the kind of thing that LLMs ought to enable. But it also implied an enormous amount of engineering as well: Apple was effectively talking about a multimodal, agentic, tool-using model, running mostly in on-device compute (or perhaps in the ‘private cloud computer - Apple ever explained what would run where), with direct access to read, write and take actions on your data, so it has to be right, or have a lot of guard rails, or both (Simon Willison pointed out the prompt inj

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increasing investment to stop in the near future. If these three observations continue to hold true, the impacts on society will be significant. We are now starting to roll out AI agents, which will eventually feel like virtual co-workers. Let’s imagine the case of a software engineering agent, which is an agent that we expect to be particularly important. Imagine that this agent will eventually be capable of doing most things a software engineer at a top company with a few years of experience c

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AI systems in secret. Pre-deployment testing, if it occurs after internal usage, does nothing to prevent internal misuse. Powerful AI pursuing unintended and undesirable goals. AI agents may autonomously pursue misaligned or unintended goals without direct human guidance. When used internally, such AI agents could attempt to sabotage further AI research, exfiltrate their weights from company-controlled hardware, and gather human supporters via persuasion or coercion. Such risks could even occur

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ng specific formats like JSON or pydantic schemas; Tool Calling: Enabled models with supported chat templates to generate tool calls autonomously, facilitating data processing and agentic flows; Distributed Inference: Introduced pipeline parallelism and disaggregated prefill to effectively scale workloads across GPUs and nodes Our 2025 Vision In 2025, we anticipate a significant push in the boundaries of scaling for both pretraining and inference-time scaling. We believe that open-source models

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with feedback from real world applications. We are now confident we know how to build AGI as we have traditionally understood it. We believe that, in 2025, we may see the first AI agents “join the workforce” and materially change the output of companies. We continue to believe that iteratively putting great tools in the hands of people leads to great, broadly-distributed outcomes. We are beginning to turn our aim beyond that, to superintelligence in the true sense of the word. We love our curren

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habits and ways of thinking around an entirely new kind of tool (remember when we printed out emails?). We can be certain that the models will get better at least to some extent - agents, voice and multimodal will expand the problems they can solve. But I’ve also argued (here last October, for example) that an LLM by itself is not a product - it’s a technology that can enable a tool or a feature, and it needs to be unbundled or rebundled into new framings, UX and tools to be become useful. That

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pected to grow over time, though there are reasons to be skeptical of the magnitude. We show preliminary results on a prototype evaluation that tests monitors' ability to catch AI agents doing side tasks, and AI agents' ability to bypass this monitoring. We build on our time-horizon work and analyze 9 benchmarks for scientific reasoning, math, robotics, computer use, and self-driving in terms of time-horizon trends; we observe generally similar rates of improvement to the 7-month doubling time i

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