This is one of the better litmus tests for whether you’re pushing your use of AI enough or not. Up until maybe six months ago probably the best you could do with AI was pull off something you already would produce, but just doing it a little faster. But now we’re entering a point where the output of working with AI can and should dramatically exceed what you would been able to do before, in a fraction of the time. The product prototype should not only be fully functional and interactive, but it should probably have meaningfully better features than it would have had before. The engineering architecture should be able to deliver performance gains that you wouldn’t have been able to go after otherwise. The research for a project should consume every single fact on the subject across every dimension of analysis possible. The business process you implement with AI should operate entirely differently and learn from all its past failures in a way no human could. We’re unquestionably entering a period where AI will be smarter than us on most axes. Perhaps not on every dimension that matters (intelligence is still very jagged), but at least on enough dimensions that when we combine it with what we’re good at, you get qualitatively different outcomes. So the simple test is: if you could have done what you’re doing before AI, you’re likely not pushing it enough.

It’s almost incomprehensible how wide the distance is between someone who’s working at the frontier with AI and basically everyone else right now. The crazy thing is these lines are not converging, but only growing farther apart. If the curve of AI progress had leveled off, it would certainly have given most people and companies time to catch up or implement their workflows in a standardized way. The technology would have neutralized competitive advantage because everyone would be using it the same way. But what’s happened is the opposite. The curve of AI progress is basically going vertical right now, which means if you had codified your processes 3-6 months ago you’re likely already behind again. Now imagine most companies that kicked into gear a year ago and thought that they were done. Even further behind now. I’ve never seen a technology in my life and career that improves at this rate, which consequently means you need to upgrade your expectations at a similar rate. Our brains weren’t wired for this, and our organizational change management processes certainly weren’t either. With any form of recursive self improvement, things just get wilder. It’s a time when you will need to be constantly reevaluating the things that didn’t work just a couple months ago to see if they now can be solved. AI native companies certainly have a strong advantage due to this right now, so now it comes down to can the bigger companies keep up. Wild times.

Agents are going to consume 1,000X more tokens than we consume today. Because they’re going to be running in our workflows, processing all of our data, building out nearly all software, securing enterprises, and much more. To do this at scale we will need a variety of approaches at the frontier of AI. This includes continuing to push the boundaries of closed models, while also increasing the capability of open weights models, making them more secure, as well as making them more efficient for large workloads. Very exciting to see what @Suhail is building with Particle on this front.

This is correct. The vast majority of enterprise workflows have not actually had agents applied to them in any meaningful way. The economy probably has this deployed < 1% right now. At best you have a few automated systems in a company that do a single pass of an LLM to make a decision or provide feedback, but very little real “automation”. Some of this is a statement about the adoption rates of technology in enterprises, but the vast majority of it is really just due to the capability of AI itself. That’s all changing. Now with computer use, long running agents, harness improvements and surrounding infrastructure, we’re finally at a point where we can start to apply agents at more complex workflows. We will see self-improving systems that can take any particular workflow goal and improve the execution rates over a volume of instances. And with the emerging form factor of agent swarms, you can just choose to deploy any amount of compute at a problem and agents will off and do the work. This won’t be about and users prompting agents to complete a specific task for them, but instead the organization reengineering the underlying workflows to take advantage of agents in their processes. This will have a massive impact in finance, life sciences, healthcare, and other information-heavy domains. Lots of opportunity to build and deploy these systems right now.

If you wanted to know all the jobs that AI will produce, look no further than cybersecurity. This is going to become one of the biggest investment areas for enterprises going forward. And even as the work becomes more agentic and autonomous you will need more talent to point the agents at the most critical work, review findings, patch systems, respond to incidents, and more. Lots of opportunity right now for the systems that protect data and enterprises, as well as the professionals that are deploying them and protecting organizations. It’s going to only get more wild out there.

Cyber is going to be one of the biggest spaces for AI agents in the future. Obviously the risks of software exploits and the attack surface area increases 100 fold due to the ability for agents to work through any crack to find what they’re looking for. But this goes the other way as well. The ability to have agent swarms review every line of code, test any number of permutations of exploits, and research any incident faster than humans as they happen, will be a huge boost in how we secure systems in the future. Cyber has always been a category where it’s hard to keep up with (and triage) all the incoming risks, alerts, and changes in the enterprise and across all system. You can never hire enough people or work through the backlog of all known risks. Agents actually give you a chance finally to have far better coverage of this. This is going to be a huge opportunity as well because we’ll see plays at the labs, data providers, the layer of the network and firewall, identity, devices and endpoints, SDLC, and even how we protect our data in the enterprise. Lots of interesting dimensions to cyber at the moment.

At Box, we've been testing Gemini 4 Argon on our Box AI Complex Work eval for the past week. It's a huge upgrade over Gemini 3.1 Pro, jumping by 14 percentage points across all our tests, and firmly at the frontier. In our evaluation, we give the Box Agent (using Gemini 4 Argon) a variety of complex enterprise tasks involving enterprise documents in a range of industries. These tasks approximate the kind of hard, time-consuming work that knowledge workers do every day with their enterprise content. Gemini 4 is a huge leap on effectively every dimension of this work. Gemini 4 is far more capable on math, logic, analytics, and deep knowledge of a variety of business domains. It also works far harder to complete tasks fully without stopping short of the desired result. This is incredibly important for complex enterprise workflows that depend on robust task execution. Here are some example wins that we saw to put the improvements in perspective: * Financial services (+18 points): a funding-gap analysis for a family-owned company, where the brief spelled out every step of the projected statements, down to the interest on the bank loan and the overdraft for each year. Gemini 4 Argon built them exactly as specified, every line broken out and carried through to the funding gap. Gemini 3.1 Pro folded the interest into a single figure. * Healthcare (+25 points): an intensive care management plan where both models spotted a dangerous potassium level and a drug interaction. Only Gemini 4 Argon went back and pulled the offending drugs from the final regimen, so its plan actually matched its own diagnosis. * Technology (+37 points): a product performance review deciding which of two regional markets should have its cloud infrastructure optimized first. Gemini 4 Argon pulled the right month's user, retention and per-user figures for both regions and correctly flagged North America. Gemini 3.1 Pro often pulled the wrong rows, at one point reporting retention above 100%, and pointed the effort at the wrong region. * Energy (+16 points): an energy consumption report across two facilities that had to flag any gaps in the meter data. Gemini 4 Argon consistently caught that the data center's smart meter log stops in November. Gemini 3.1 Pro often missed it, at times declaring the logs complete. * Consumer products (+33 points): a product launch evaluation deciding which production expansion to fund. Gemini 4 Argon saw that existing inventory could bridge an early capacity gap, which made the smaller expansion viable with an earlier launch and a faster break-even. Gemini 3.1 Pro defaulted to the larger expansion and the later timeline. Gemini 4 Argon will be available in the Box AI Studio shortly for anyone to build custom Box Agents with.

Ok this is quite cool research and ultimately must be the eventual architecture of agents. In the limit, AI will obviously be better at determining its own harness once it sees the task at hand, than us being able to do this in advance:

What a crazy time in AI with Meta now jumping fully into the frontier. Now that there are multiple players pushing the state of the art, this is going to lead to a further acceleration of advances, as well as the lowering cost of tokens for even the highest levels of capability.

Agent swarms are going to be a core part of many enterprise workflows in the future. The ability to throw compute at problems that you can massively parallelize will be too useful to not take advantage of. Cyber will be one of the biggest beneficiaries of this as it’s a space where testing vast number of permutations of attacks (without getting tired or bored) is an incredibly useful property. You have a wide search space of possible risk vectors and you generally need to be able to chain together different scenarios to achieve the exploit. This will become one of the biggest token uses in large enterprises given the range of IT systems, software, physical infrastructure, and data that needs to be protected at all times. Super interesting times in cyber.

This is a good way to understand how early we actually are in AI diffusion right now. The vast majority of people are using AI as a chatbot for simple interactions that approximate next-generation search. This is super useful but dramatically underrepresents the full capability of AI. On the complete other end of the spectrum, agent swarms represent possibly the ultimate form factor of what agents will do inside of our workflows. Math, cyber, bio, materials science, software engineering, finance, and any other field that has valuable tasks that can be executed in parallel where compute can be applied to accelerate work all have this characteristic. Less than a fraction of a fraction of the population uses AI in this way right now, due to a mix of the capabilities of models relative to the user's domain, the cost of doing these types of workflows today, the harnesses available to do this style of AI work, and the availability (or lack thereof) of frontier models to the general public. There will also be a huge amount of change management necessary to get most areas of work -beyond software and AI research- to benefit from this style of agents. Companies need to upgrade their workflows, data needs to integrated and migrated into modern platforms, and companies will need to work through compliance, governance, and security frameworks to pull it off. But this all represents opportunity right now. A lot of this is where the applied layer of AI will really shine in the enterprise because of the domain specificity of this type of work. You can get an early sense of where things are going and the opportunity ahead by seeing through the funnel that Andrej lays out.

If you want to know where all the tokens will get consumed in the future, here it is. Agent swarms will have massive implications in a number of enterprise domains like software development, cyber, life sciences research, AI research, finance, and more. The ability to parallelize agents against a unified goal where the volume of work is large, or where the search space not entirely known upfront, is going to be massive. Agents can fan out and coordinate work together on a specific mission in completely different ways from what humans are able to do. Today even in the most resource-abundant organizations, your ability to parallelize work on most projects is quite limited. Humans run into constraints on being able to communicate with one another, share status, deal with conflicts in work, keep shared context, and so on. As AI models can handle larger context without getting confused, and as they’re able to communicate effectively across their work with shared infrastructure, they can avoid many of the pitfalls that people have. You can see how this can lead to not just faster execution of tasks, but qualitatively different outcomes in medicine, research or defending against cyber attacks, scientific and math discoveries, and so on. This is why we’re going to need 1,000X more compute.

Good post to start thinking through the long term safety approaches to managing intelligence in an enterprise. As agents begin to do vastly more task execution in the organization than humans, our need to secure, govern, control, and audit that work will become paramount. You’ll have agents operating against most of your most valuable data; they’ll be making decisions that will be harder and harder to reverse engineer; we’ll need to understand the liability and accountability regimes when models make decisions for us; and we need ways of containing the blast radius of accidentally or intentionally malicious agents. “Treating frontier closed and open weight models like insider risks is a way to build such a system. Not because they are necessarily malicious, but because any sufficiently capable actor with access to important systems can make mistakes or be compromised, and the architecture of containment and control must account for that” In a sense, AI will need to have its “zero trust” era that security saw. And all of this leads to needing various layers of protection and auditability of what agents are doing, what data they can work with, and controls for when things go wrong. And a huge opportunity right now for those building such systems across the enterprise.

Agents spawning other agents, agents operating in the background on every task, and agent swarms will consume 1,000X more tokens than people prompting agents 1 at a time. The usage of AI as a chat system that can only work for you at the pace that you can prompt it will seem like a relic within a year or two. The vast, vast majority of tokens will be consumed by agents that are just doing continuous work for us in the background and in our workflows. This is why we’re still so early in the curve of AI agent adoption right now, and how much more compute and infrastructure build out will be needed to sustain this.

This sounds weird, but it is actually probably a good policy. Even if you don’t believe AI is conscious (I don’t), it stands to reason that you don’t want future models trained on endless content of humans being rude to models. The models only understand the data they’ve been trained on, or what they run into in their interactions, so if you want safe and aligned models we probably want AI to have lots of good interactions in their training data. Anthropic may have more extreme beliefs than this (though even if they didn’t, you may want to word policies like this), but this is sort of an easy Pascal’s wager for AI. Just be nice to the AI.

Today Hone launches Engines: AI that owns business outcomes. Chatbots give answers. Agents do tasks. Engines deliver outcomes, and we hold them responsible for creating real value, measured in actual dollars instead of tokens or vibes. You point it at a business metric, and the Engine figures out the rest. It onboards itself, builds the necessary agents, skills, and memories, collaborates with the right people, and gets better with experience. From AI leaders to global enterprises, our customers have already used Engines to: • Uncover millions in procurement savings • Optimize product funnels • Manage credit risk • Increase qualified pipeline in new territories • Accelerate implementations and improve account management To scale this impact across the economy, we’ve raised a $60M seed led by Benchmark and Index with participation from Elad Gil, Hanabi, Definition, Diffusion, Lux, SV Angel, and Align. AI’s raw capabilities are far ahead of the value it creates in the real world, and Engines will help close the gap. Here’s how they work…

We built Box Mount to allow developers to mount Box as a file system directly in any agent sandbox. As agents do more complex work, they need to be able to work with files and data just like a person would. The future is headless.

The compute needed for the stage of AI we’re about to enter is going to be insane. Personal agents, agent swarms defending enterprises, agents that review all of our code for security issues, agents that process nearly all enterprise data in workflows, background agents working on tasks 24/7, and on and on. This will take a mix of inference volume that is going to keep growing by orders of magnitude, as well as infrastructure that agents need alongside the tokens. They need computers, networking, file systems, and so on. We’re only in the early stages of what this buildout is going to look like.

Cyber will be one of the most defining domains for AI in the coming years, and a huge area of focus for most enterprises. AI is going to now create a whole new level of work for security dealing with the increase of vibe coded issues, agentic attacks, and even accidental agent swarms hunting for data. OpenAI + Hugging Face is just a preview of what’s to come. Security teams have often been the most strapped for resources teams in the enterprise, and now it’s only going to get more intense. AI agents of course become the solution to this problem as well, and we’ll see a range of new agentic products for protecting code, enterprise systems, mission critical infrastructure, and enterprise data. Huge opportunity here right now. And it’s going to be a booming market for cyber professionals that can effectively deploy agents for security purposes in the enterprise. Great time to be in security.

A decent amount of agent adoption and deployment is bottlenecked by being able to test, tune, and optimize agents on “real” work environments. The files agents need to access, the CRM environments, email, and more. It’s impossible to know how agents are performing if you don’t understand how well they’re executing on your evals and how they’ll perform in the future with a model change or workflow upgrade. Every enterprise has to do this 1 by 1 right now, which is obviously slow and tedious. Every enterprise is going to have someone managing evals, infrastructure for building and running evals and simulated environments, and more. Huge space.