CEO and Co-Founder at @cribl_io.
Oakland, CA
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I thought this was the best analogy I’ve seen, and he called it three years ago. It’s possible Dario, Sama, Elon, etc believe we need to slow development of AI because they can see things in their labs we cannot. Certainly the Chinese are not going to agree to pace their development.
I think David Sacks was spot on. They don’t need anybody’s permission to slow down. They don’t need to have a new regulatory authority make them. They could just do it. Instead, it looks an awful lot like regulatory capture. For companies like mine, much smaller, it’s terrifying there might be a governmental or even industry authority I need to certify our work before we can even ship.
As many others have said I think the real story is open models are eating into their growth and the charts they need to show exponential growth have started to bend flatter and they need regulatory capture to lock in their oligopoly. For our users and customers, our experience is the level of intelligence they need for telemetry use cases can be done for pennies on the dollar compared to the boutique token company’s models. It’s an exciting future.
Jevons paradox reigns supreme. Lowering the cost of tokens is going to open up so much demand for intelligence. The future is extremely bright, presuming we don’t allow the Baptists or the Bootleggers to lock us out of our path to the market.
The Baptists And Bootleggers Of AI
Economists have observed a longstanding pattern in reform movements of this kind. The actors within movements like these fall into two categories – “Baptists” and “Bootleggers” – drawing on the historical example of the prohibition of alcohol in the United States in the 1920’s:
“Baptists” are the true believer social reformers who legitimately feel – deeply and emotionally, if not rationally – that new restrictions, regulations, and laws are required to prevent societal disaster. For alcohol prohibition, these actors were often literally devout Christians who felt that alcohol was destroying the moral fabric of society. For AI risk, these actors are true believers that AI presents one or another existential risks – strap them to a polygraph, they really mean it.
“Bootleggers” are the self-interested opportunists who stand to financially profit by the imposition of new restrictions, regulations, and laws that insulate them from competitors. For alcohol prohibition, these were the literal bootleggers who made a fortune selling illicit alcohol to Americans when legitimate alcohol sales were banned. For AI risk, these are CEOs who stand to make more money if regulatory barriers are erected that form a cartel of government-blessed AI vendors protected from new startup and open source competition – the software version of “too big to fail” banks.
A cynic would suggest that some of the apparent Baptists are also Bootleggers – specifically the ones paid to attack AI by their universities, think tanks, activist groups, and media outlets. If you are paid a salary or receive grants to foster AI panic…you are probably a Bootlegger.
The problem with the Bootleggers is that they win. The Baptists are naive ideologues, the Bootleggers are cynical operators, and so the result of reform movements like these is often that the Bootleggers get what they want – regulatory capture, insulation from competition, the formation of a cartel – and the Baptists are left wondering where their drive for social improvement went so wrong.
We just lived through a stunning example of this – banking reform after the 2008 global financial crisis. The Baptists told us that we needed new laws and regulations to break up the “too big to fail” banks to prevent such a crisis from ever happening again. So Congress passed the Dodd-Frank Act of 2010, which was marketed as satisfying the Baptists’ goal, but in reality was coopted by the Bootleggers – the big banks. The result is that the same banks that were “too big to fail” in 2008 are much, much larger now.
So in practice, even when the Baptists are genuine – and even when the Baptists are right – they are used as cover by manipulative and venal Bootleggers to benefit themselves.
And this is what is happening in the drive for AI regulation right now.
However, it isn’t sufficient to simply identify the actors and impugn their motives. We should consider the arguments of both the Baptists and the Bootleggers on their merits.
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How quickly the future becomes the new norm. I'm annoyed that my AI Agent, which I can reach in under 30ms due to the excellent Starlink on my flight, which has generated essentially 3 person months worth of software in the last 24 hours, won't work faster.
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Today, we've released SecIT Bench to evaluate the performance of various models for investigative use cases on top of telemetry. TLDR: open weights models are good enough, especially if we start using LLMs as judges for their output.
secitbench.cribl.io/
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Blog post with more details on its creation: cribl.io/blog/introducing-se…
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Point an AI agent at your telemetry today and it can only see what one vendor lets it see. Agents are becoming a second consumer of telemetry, right alongside the people who run your systems. Our current siloed systems are not ready for consumers thinking at machine speeds. That’s agentic telemetry, and most enterprises aren’t ready for it.
Problem one: agent fatigue. Every vendor is bolting a copilot on its own silo and calling it done. Your security copilot can’t see your application traces. Your monitoring copilot has no idea what your SOC just found. Different agent, same walled garden.
Problem two: context starvation. It’s not just missing logs, it’s missing context. A trace without the deploy, an alert without identity, a ticket without telemetry behind it. You can’t ask questions of data you don’t have. Neither can your agents.
Problem three: token economics. Your telemetry is measured in petabytes. A context window is measured in megabytes. You cannot shove the former into the latter, and agents ask orders of magnitude more questions than humans ever did. That math breaks fast.
The answer isn’t a more features of your siloed tools. It’s an architecture, an AI Platform for Telemetry: many capabilities, integrated, built on open protocols like MCP, where any authorized person or agent can ask questions of all your data, wherever it lives, at full fidelity, with no vendor holding a monopoly on it.
Our hope is by defining the architecture openly, you should be able to build this yourself, with @cribl_io or without.
Read my blog: cribl.io/blog/what-is-agenti…
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This just makes absolutely zero sense. There already isn’t a lack of supply in enterprise software. For any given category there are already dozens of choices. There are already funded and bootstrapped in every category. There are free and open sourced alternatives in nearly every category. So what is the limiting factor that drives spend to a handful of category leaders?
Trust. Trust is in very short supply. How do I solve this problem for my employer in a way that does not introduce new risk and get me fired? Buyers are not buying just a software package they are transferring risk from them to the vendor. Software is a people business. I’m going on record that a burgeoning supply of dozens of new competitors will do little to change the dynamics of where money flows in enterprise software.
.@nicbstme does a great job capturing the rapidly shifting sands in vertical SaaS!
This right here is the key… AI is arming the hordes - and they are coming for their cut of enterprise SaaS spend!
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The amount of people who are prognosticating with supreme conviction about the future of the software business while seeming to have little to no experience building software businesses is approaching COVID level faux expertise.
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Sharp’s Law of AI markets: any sufficiently large market will attract the attention of the model builders, so choose a large market but not too large
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Clint Sharp retweeted
We will look back at this time and recognize that DeepSeek was the tipping point on focus moving from training to real-time inference. Cost of intelligence is quickly marching towards zero.
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If you’re a product person and you’ve never really done sales, I mean really done it, then a) you’re missing out and b) you’re living a theoretical life that isn’t grounded in reality. Life is a negotiation. And I don’t mean negotiating with engineering.
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Excited to see @getmetronome unveil their latest updates in Metronome 2.0. We were an early adopter at @cribl_io, and Metronome is core to our cloud usage billing. The new capabilities are critical to new pricing structures we'll be rolling out next year. Proud to be an investor. Congrats to the team!
✨Excited to announce Metronome 2.0! Over the past few years, we've learned a lot from powering billing for companies including @OpenAI, @AnthropicAI, and @databricks. We've productized those learnings into Metronome 2.0 to help companies launch new products and pricing faster.
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Was so much fun to chat with @jonfortt! We cover Cribl, my background, the data space, and more.
I speak with @cribl_io CEO @clintsharp about data management in the AI era, and his entrepreneurial journey. nitter.net/i/broadcasts/1jMJgBavg…
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I’m so excited to share that Cribl has closed an oversubscribed $319M Series E round at a new $3.5B valuation, up 40% from our Series D just two years ago. The round was led by @GVteam in one of their largest ever investments, with participation from GIC, @CapitalG , @IVP , and @CRV . But that’s not all. GV partner and former GitLab CRO Michael McBride (@mcbmichael) is joining our board of directors. This is an incredible milestone for us and it goes to show that solving real problems for real users works really well!
Since day 1, Cribl was built to help you unlock the value of all your IT and security data. We’re taking a different approach to data management, with vendor-agnostic products that resolve the tension between data growth (28% CAGR and growing!) and budget growth while giving you choice, control, and flexibility. We’re here for the critically important but often underserved IT and security practitioners who keep businesses running. And we’ll continue to innovate to meet your needs.
Thank you to our customers, partners, investors, and all of the amazing goats who have supported and trusted us during this journey. Here’s to more incredible moments as we continue to succeed together!
Read more about the announcement in my blog post: cribl.io/blog/announcing-our…
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Wonder how much of that is logging...
Anecdata, but many of @cribl_io's largest customers and prospects are running logging installations on over 10k physical and virtual machines. Logging is the biggest application in the datacenter, maybe until AI takes off.
Video of the inside of Cortex today, the giant new AI training supercluster being built at Tesla HQ in Austin to solve real-world AI
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