Aaron Levie @levie

There are some subtleties in this launch that are very important in practice. This isn’t just you interacting with Claude in a 1:1 format via Slack. In this case, Claude acts as a coworker that any user can tap into in a shared way. We’ve already seen some agentic coding systems start to adopt this pattern (as well as OpenClaw and Hermes), and doing it for general purpose knowledge work continues to push the idea forward. As a result, what this means is that this agentic coworker needs its own set of resources, access to tools, and data to work with. This is not the same as you giving it access to your personal resources and tools, because the agent then could accidentally then share those out with anyone. The agent needs to instead be like any other user in the system, and you need to be thoughtful about what it should have access to, and make sure its information that is safe to share with that group. When you can pull that off, it’s quite powerful. For instance, by connecting to Claude Tag to Box, you could have Claude access corporate sales materials for questions in sales conversations or generating RFPs, brand guidelines and marketing assets for campaign creation, product roadmap materials and product documentation for coding agents to use, contracts that anyone in the legal team can access, and more. But this is just the Box example. You can equally have it access your product or customer analytics data, CRM information, codebase, and other resources and agents that would make sense to work on in a collective manner. It’s awesome to see continued innovation in what the future of work may begin to look like with agents.

Andrej Karpathy @karpathy

a day ago

This is a new paradigm for interacting with Claude that is significantly more "inline" with all the other human activity org-wide. Once you do all of the under the hood engineering work to make this "just work" (e.g. across tools, integrations, compute environments, memory,

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@DaveRBanerjee It’s not really about norms, it’s just a complete nonstarter for any enterprise usage at scale. So the market would just move to whoever doesn’t do that.

Good post on the dynamics of AI pricing right now. We’re basically going to see a barbell dynamic on pricing between high cost frontier models and cheap but good open or closed weights models. Then the question is how do you maximize efficiency from the frontier models, and get better quality and performance of the cheaper models. That’s where the applied AI layer comes in. The applied layer can route to the best model at any time given the workload at hand, as well as mitigate either high token costs or worse performance. The closer you are to the underlying workflow, the better you can tune the use of models for a given business process. That’s all about understanding the workflows, domain specific FDEs, evals by customer or process, getting data setup well for agents, and more. Overall good for applied AI.

Nikesh Arora @nikesharora

2 days ago

The AI Business model trap: LLMs want cash flow to fund the race to AGI or the next model. Enter free consumer AI - they are losing a lot of money on the breadth of models to serve consumers for free! They are caught in the post training data trap, free consumer usage feeds post

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Another example of the power of headless software with agents. With Claude Tag, you can give Claude access to any corporate files in Box that you can interact with from Slack. Now all of your enterprise content becomes a portable knowledge base.

Claude @claudeai

2 days ago

Introducing Claude Tag, a new way for teams to work with Claude. In Slack, Claude joins as a team member with access to the channels and tools you choose. Tag Claude in and delegate tasks to it while you focus on other work.

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Almost all AI model and agent progress is downstream from evals. Open weights post training for specific domains comes down to evals. Agent improvements in the applied AI layer is all about evals. Agentic enterprise deployments that actually can augment work is all about evals. It’s all evals. This will become a core competency of any enterprise in the future. The companies that are able to best understand their own (and/or customers) workflows and how well agents participate in that work will be in the best position to actually drive real automation.

Garrett Lord @GarrettLord

3 days ago

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We heard that HTML is a big deal again. You can now preview, edit, manage versions, and securely share any HTML based content on Box. Great for being able to work with any agent produced content immediately.

Another new idea to push the state of AI architectures forward. Sakana released a model that effectively uses a mixture of models to get work done. You get a single API but then the work gets farmed out the model that best performs the task. “Fugu manages model selection, delegation, verification, and synthesis automatically. It solves tasks directly when that is enough, or coordinates a team of expert models when a problem calls for more. The complexity of a multi-agent system never reaches your code.” This is generally how applied AI products are building their agent harnesses at this point, but the idea of making this an LLM that any developer can interact with is also a great idea. As we get more innovation with both frontier closed and OSS models, there’s going to be a ton of value produced for the layer that can route the best.

Sakana AI @SakanaAILabs

3 days ago

Introducing Sakana Fugu: A full multi-agent orchestration system accessible via a single model API. Our ‘Fugu Ultra’ model matches the performance of Fable and Mythos, delivering frontier capability without the risk of export controls. Try it: sakana.ai/fugu 🐡

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Agents will use software 100X more than people. When that happens, theres a huge need for guardrails on what the agents are doing so they don’t leak data or change the wrong information, authoritative sources of truth for them to work with, logging and auditing of what they’re doing, the ability to collaborate with people through these systems, and more. A simple query on any given agentic task could pull in more data than a user touches in a month. As a result, there are lots of categories of software that when it goes headless that the usage and value go up substantially. Agents will end up using our CRM data, documents and corporate knowledge, analytics data, and other information far more than people ever did. The platforms that can move toward the model of powering these headless interactions, and have a business model and technology strategy to support this, will be in the best position in the future.

Podcast Alpha @PodcastAlphaX

4 days ago

Levie now uses Salesforce 5x more than at any point before. The Box CEO @levie connected Salesforce's MCP server to Claude Code. Now he runs customer and market intelligence queries he would never have bothered pulling up by hand. The agent removes the friction. The underlying

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Hugely underrated point. You basically state this, but to underscore it: for most knowledge work other than code, the digital output is often an intermediary step in the value creation process. Comparatively, for code automation, it's literally is the equivalent of automating on the production line in manufacturing. But for most other areas of knowledge work, there's still a ton of real-world interaction for the ultimate value creation to occur. For a sales rep, automation gets them to the next stage in a sales cycle faster, but they still need to meet with the prospects and customers and have a compelling message and presence; in legal, the value is created only once the client and counterparty agree on deal terms after lots of back and forth; in life sciences, only when in real life medical trials and the drug works did the automation matter, etc. The main implication here is just that diffusion will be different than many anticipate because they're looking at coding as the proxy. One huge positive point here is that it's one of the reasons we're not going to be in the doomer timeline.

Pretty remarkable what’s happening with open weights AI right now. We’re seeing models achieve SOTA results on specific tasks, and getting close to frontier on some areas of coding and other domains. The more that open weights is able to maintain only a marginal gap from the frontier, instead of a widening gap, the more value that can be created with AI. Incidentally, this is actually fine for the frontier labs as well; if we can lower the cost of an overall task then AI usage goes up in general. You’re still likely using frontier models for planning, orchestration, reviewing, and other parts of work. But this is all very good for the applied layer of AI, which is now in a great position to cost optimize workloads with cheaper models or use tailored open models post-trained for specific tasks to improve performance.

Design Arena @Designarena

5 days ago

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The main variable in getting success with agents is whether you can get the agent the context it needs to do its work; and a major factor in that is if you can create a shared working area for that agent that a human can understand as well. This is one of the reasons why agents using file systems is such a big deal. It creates a unified system that both the person and the agent can work within to pass around data. “What they need is a working set: plans, notes, task lists, policies, drafts, summaries, logs, corrections, decisions, etc. For that layer, a filesystem-shaped interface tends to be more legible to both the model and the humans supervising it.” It turns out giving agents access to the systems we already know how to use, but in a way that is best optimized for them, is the perfect primitive for agents to work.

Carter Rabasa @crtr0

a week ago

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@avdepotx @Box Please send any feedback! @FernandoCerenza @bpalit

The fact that open weights models are being discussed credibly at this level of capability should be a huge update for many. The implications of open models getting to frontier performance ensures that you can always have sovereign AI, have the ability to post train for your specific workflows, cost optimize for various workloads, and actually afford to do much more with AI (which opens up meaningfully different applications). Huge win for the applied AI layer.

Marc Andreessen 🇺🇸 @pmarca

7 days ago

Interesting. (Founder of Z.ai, creator of GLM AI models.)

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This is a good update for getting access to Fable. It also gives us a view into what the future is likely going to look like with AI regulation. The government will have frameworks that are used to determine future model releases past a certain threshold of capability or compute levels. Given all the constituents involved, and the economic and societal significance of AI, this was practically an inevitability. It may seem small but the implications are massive. It will mean that each model update will go through an extensive review, testing, and feedback process. And in that processes lots of groups will weigh in on the risk of the model, and there will be lots of subjectivity on what the actual risks are or practicalities of exploiting those risks. A positive potential future here would be we still get massive model progress but they just happen in bigger jumps at once, where the labs pack in major improvements since the cost and slow down of each review stacks up. On the other side, the risk is that past a certain threshold we may not get to see the rapid back and forth of model progress that we’ve gotten used to which can have negative compounding effects. Hoping for the former outcome.

Sophia Cai @SophiaCai99

6 days ago

NEW: White House and Anthropic are working to create a formal technical assessment framework that can quantify the severity of the jailbreak in question and create a standardized methodology for evaluating similar incidents in the future.  It’s the clearest sign yet that talks

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@curiousgangsta @Kantrowitz Don’t insult my sister!

The past couple months we may be witnessing what the Applied AI layer will look like at scale. Despite some of the initial critique that this would just be a thin layer on the LLM, it’s turning out that actually driving agentic workflows in an enterprise is far more complex. And anywhere there’s complexity you generally gain a moat and value over time. Here are a few of the components that appear to make up the playbook based on the examples we’re collectively seeing in coding, legal, healthcare, customer support, financial services and other fields: * Build the features that bridge the gap between the intelligence and the workflow. Some workflows can be automated by simply going to a general purpose interface, but others need tuned interfaces and features tied to the work they’re augmenting or automating. They need features that are specific to capturing the kind of data that’s needed as context for the agent. And they need a variety of bespoke tools for the agent to use, and unique interfaces for the human-in-the-loop UX. Going far deeper than just presenting the output tokens is clearly critical, and the more depth there is here definitionally the more sustaining value. * Act as the model router balancing frontier intelligence with cheaper models. A natural advantage that any model neutral platform has is that it can naturally (in a business model-aligned way) leverage whatever level of intelligence is necessary for the workflows they’re automating to get done. There are plenty of scenarios where you need GPT-5.5 or Fable level capability, and also lots of workloads where a more efficient closed or open weights do the trick. Only the companies that have deep evals on specific tasks across all models, and the ability business model wise to leverage them, are in a great position. * Drive the actual implementation and change management via FDE or equivalent. A big reason the applied layer works at scale is that most enterprises need some degree of help and support with change management in implementing agents for their workflows. Data has to be cleaned up and moved to modern systems, processes have to be re-engineered and documented, workflows have to be evaled, SLAs have to get achieved, and so on. All of this is going to be unique for every type of process that gets implemented, which means the companies that have expertise in a given domain and come with all the relevant best practices will be in a strong position. * Implement domain specific GTM that creates expertise in that field. Beyond FDEs the companies that can build sales and GTM motions aligned to their domains also have a natural advantage. Most IT and line of business leaders have too many things to do in any given day; so if you’re not on their agenda, likely someone else is. Depending on the industry, there are entirely different sets of language you use, ways of working through security and compliance, regulatory controls you have to support, industry events that companies convene at, different system integrator and consulting partners you need to work with, and so on. The more generalized this gets the less you can speak the customers language, which is where the applied layer has a leg up. A final note. There remains a view that a lot of this is all mitigated by model intelligence alone, and the bitter lesson solves all of this in the limit. That’s possibly true, but enterprises need help changing *today*. And many aspects of how to bring intelligence to real world work don’t only depend on the axis of the pure capability of the model, so most of what you’re doing now to win ends up being important no matter how good the models get.

@jack_sondag @trq212 cc @mickcodez @swaroopbutala

One of the biggest questions in AI is how far behind open weights models remain from closed models at any given time. There are huge differences in market structures depending on whether open weights models remain 3 or 6 months behind, or if they fall behind by years. The answer to this will determine how the chip stack plays out, where inference can be run, what sovereign AI looks like, what happens at the applied AI layer, what the margin structure looks like in AI, how much companies can afford to spend on AI, and more. At the moment the open weights players appear to be holding up at keeping close to frontier levels of capability. Will be fun to see how this plays out.

Z.ai @Zai_org

a week ago

Introducing GLM-5.2: Frontier Intelligence, Open Weights - Significant improvements in coding and agentic tasks - Strong long-horizon capabilities with a 1M context window - Two levels of reasoning effort: GLM-5.2 (max) pushes the limits, while GLM-5.2 (high) strikes a strong

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The Cursor deal is symbolically quite significant. It was effectively the first mega success in the applied layer of AI. They firmly proved out the value proposition of having a deep domain focus, the role you play as a model router, when to lean into frontier models vs. when to train your own, and the role of applied AI GTM and distribution to make sure you’re actually taking advantage of the market opportunity. Every aspect of their business was tuned to carve out ground and keep doubling down in a highly competitive space. This is really the first at scale template for how to execute this playbook.

Chamath Palihapitiya @chamath

a week ago

$60Billion. This is the first, but not the last, big exit at the application layer of AI. As product value accrues and accelerates upwards, the focus over the next few years will be firmly on the “control plane”: What gives organizations who want to go all in on AI the

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Key post that gives a bit of insight into what the future of AI could look like. “The most interesting thing happening in AI isn't that one model is getting smarter. It's that intelligence is becoming increasingly customizable. The companies that win won't necessarily be the ones with the biggest models. They'll be the ones that turn intelligence into something uniquely their own.” The ability to combine your unique data, workflows, and a layer that can route intelligence to whatever model best performs the task is clearly the future.

Lin Qiao @lqiao

a week ago

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