Aaron Levie
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3 November
The divergence in AI output weirdly may be the highest it’s ever been, even though adoption is the broadest it’s ever been. A few years ago the best productivity gains you could get, even in the most capability-aligned field (like coding), was maybe 30-50% better productivity if you were very good at it. You’d spend a decent chunk of time cleaning up the mistakes of models, the context windows were too limited to do any long running work, and the interfaces required highly synchronous interactions. Now, the models can do far longer tasks at a time with limited human interaction. The harnesses… Quoting @ChShersh Somewhere in the world there’s probably an entire team of 6 devs, working full time on a project that’s just one Claude Code prompt
2 November
Increasingly this will be the new job description for a lot of roles, not just coding. We’re clearly moving from a world where agents help us do our work incrementally better or faster, to us helping agents do their work. This turns everyone in one of these positions to that of a manager of a team of agents, instead of an individual contributor with just some added leverage from AI. The biggest areas of impact will be information-heavy jobs, like coding, cybersecurity, legal work, biotech, engineering, and many other fields. And as a result, in these jobs your leverage will begin to be decoup… Quoting @unclebobmartin Great line from the CTO of Proton. I’m paraphrasing and stealing it : Your job is not to use agents to help you write code. Your job is to lead the agents to help them write code. cybersecurity
31 October
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 ar… Quoting @sundeep If you’re going to make something with AI, make sure it’s 10,000x better than anything you would have made yourself. If not, you just come across as lazy not smart :)
30 October
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…
28 October
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. Quoting @Suhail 1/ Excited to announce our new company Particle This is our first step in a series of models focused on developers. With particle-1, we focused on token efficiency and managed to keep parity in performance while using ~45% fewer tokens. Live now: 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 surr… Quoting @saranormous we remain in the autocomplete / early claude code era of enterprise workflow automation, but not for long LLMs
26 October
This is the key stat. The average Fortune 500 customer of Anthropic spends less than .1% of their expenses on Anthropic. While that number likely will eventually asymptote somewhere (token prices coming down, and other large enterprise spend areas not going away), there’s no reason it can’t be 10-50X higher than this, reaching a “mere” 1-5% of spend. Which would still be a fraction of what most startups are spending. Thus, you can see how big of a divide there is between the frontier users of AI and the rest of the world still. Today, only a microscopically small set of companies are actually… Quoting @BenBajarin More than half of the Fortune 500 already qualifies as a significant customer, but Anthropic’s revenue from those companies averages less than 0.1% of their expenses. Its ten largest Fortune 500 customers spend more than eight times the broader cohort average while still spending less than 1% of ex… Anthropic startups
25 October
Good assessment of the on the ground reality for security in most large enterprises right now. The fragmented landscape of the risk surface is only going to get more gnarly, making it even more complex for enterprises to solve. There’s a never ending set of libraries to keep up with, tools to leverage, and sprawling infrastructure, data, and code to secure. Most large enterprises can barely keep up with this, let alone smaller startups or most companies outside of tech. In many cases enterprises need to work to modernize their tech stacks. Data, in most organizations, is highly fragmented whi… Quoting @nikesharora It's been an eventful week. Multiple vulnerabilites in Internet facing software, coding repositories, examples of agent behavior that seem concerning and a flurry of security specialists with solutions that can partially solve your problem. There in lies the challenge. Given the state of IT and sec… startups infrastructure
We’ll likely have 1,000X more tokens flowing through cyber agents soon than we do today. As the surface area of cyber risk increases, at some point soon it will be nearly impossible to keep up with the volume of new risks (we’re essentially already there). Large companies barely had the resources to pull this off before, but the vast majority of companies don’t. There’s a never ending set of libraries to keep up with, tools to leverage, and sprawling infrastructure and code to secure. We’ll have agent swarms that can fan out, leverage any security tool, test various perpetuations of attacks,… Quoting @EnclaveAI Node2Shell (CVE-2026-111497) is a memory bug in Brotli, the compression library bundled into Node.js, Python, system libs, Envoy and more. Most teams don't know every place Brotli runs in their stack. It's usually a transitive dependency or an OS package, and pip-audit may not flag it yet. Enclave'… infrastructure
23 October
Agent swarms for cyber defense is about to be one of the biggest categories in AI. We are going to have agents reviewing our code and infra, testing every permutation of risk, processing gobs of data, and more. Security is one of the best use-cases for swarms because it’s an obvious area where more compute in equals more value out. There’s a wide range of testing that people can never get around to - or at least quickly enough - that agents have no problem going after. Going to be a wild time in cyber. Quoting @EnclaveAI Big news: Enclave is now open to everyone, and today we're launching Threat Feed. Enclave is an autonomous security team for AI-native companies: a fleet of security agents that test your code, cloud, and infrastructure the way an attacker would. We built it on a simple belief. Attackers already us… cybersecurity
Any form of security through obscurity is fully over. This barely worked as a method with humans, but just simply is over in a world of autonomous systems that will look through every crevice and try every permutation of attack possible. As a result, years and years of legacy enterprise infrastructure will need to be upgraded, security practices modernized, data pipelines cleaned up, access controls better managed, and much more. Agents will help on the defensive side because they can both accelerate this work and equally simulate the risk vectors. But there’s a long road ahead to prepare for… Quoting @DabsMalone a lot of old software was protected by “exploiting this would be an unreasonable amount of work” we have invented a machine that does unreasonable amounts of work anyway how’s the patch backlog infrastructure
22 October
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. Quoting @fiveoutofnine So basically, today PHP got two critical bugs that let anyone run code on your server without even logging in, which is bad, and then the details leaked before the patch was even out, which is worse, and it works on WordPress and MediaWiki and Magento and Joomla and Laravel, and Censys says like 1.… cybersecurity
20 October
The big bitter lesson question in the applied AI layer is that as models get better and better, what is the best way to diffuse that intelligence into an enterprise. Ironically, I’d argue that as models are getting more capable, and can handle longer running tasks and better tool use, the ability to differentiate on a domain by domain basis goes up instead of down. When models were quite limited in what they could do, ironically all you could be literally was a “wrapper” because you were effectively delivering tokens back to the user with a custom prompt or some limited RAG. But in a world of… Quoting @signulll the more intelligent the underlying model becomes, the less intelligence a founder needs to build into a product in some sense. if you imagine some end stage of ai, it’s simply just intelligence without any fluff around it. today building ai software is much more about adding guardrails & specifyin…
19 October
Here’s why agent swarms are going to be a very big deal. We can finally make compute a critical variable in a large number of important tasks in the economy which wasn’t possible before. There’s a large amount of work in the world that is essentially only constrained by the amount of time that we put into research, data processing, discovery work, running tests, and more. All of this work is bottlenecked by our ability to plan, execute, review, and coordinate everything happening in these workflows. Which means we basically do very little of them today relative to their economic value. Agent…
17 October
The biggest misread of anyone predicting the end of software engineering was imagining that the world only needed a certain amount of software, and bringing automation to the field would reduce the need for labor. Exactly the opposite is happening where essentially most things are actually turning into software. Because agents can use tools, work with computers, write software on the fly, and process data like humans (but far faster), we can now throw “software” at problems that never could reasonably be digitized before. What that leads to is the role of engineering taking on greater importa… Quoting @fchollet Some observations about the "total" automation of software engineering: 1. There are more software engineers now than in 2025, and demand is growing. 2. Software engineers have never worked harder than they do now. 3. They largely don't write code anymore, agents write ~100% of the code. 4. The bot… It’s incredible to have reached a threshold where it’s practical that AI will be better at training models than humans are so quickly. One day recursive self improvement feels far off, and the next we already have signs of what it will look like. Model capability finally exceeding most human engineers, advancements in the harness and infra for long running agents, a growing array of data, and the ability to parallelize the agents to work on vastly more tasks and tests and approaches than any person could have before, creates an incredible flywheel. This flywheel will almost certainly accelera… Quoting @MTSlive SITUATION DETECTED: Anthropic’s Head of Global Affairs Michael Sellitto has told the IMF that Anthropic projects Claude could lead 80-90% of frontier model training by next year, up from 3% in April.
16 October
So in fact we actually can get robots, flying cars, superintelligence, fast internet on planes, space travel, space colonies, orbital data centers, unlimited energy, likely cures for nearly every disease, and more all in our life times. Pretty great. Quoting @elonmusk Turns out the future does have flying cars data centers AI alignment
15 October
They did it. They made a Muse open weights model, which is a huge step forward for OSS in the US. This is going to further accelerate AI diffusion because enterprises will be able to mix and match frontier performance for orchestration and high end tasks, while being able to have low cost and tailored post trained models for repeatable workflows. Overall this dramatically increases token consumption and workloads across the stack. Great to see Meta doing this. Quoting @alexandr_wang 1/ muse spark open is the most capable US open-weight model to date. 816B total params, 42B active. 1M-token context. natively multimodal. built for coding and agentic work like long-horizon tasks, hard software engineering, multi-step reasoning. america! 🇺🇲
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