builder in residence @cognition, previously founder of @promptlayer
NYC
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Frankly I think this is the reason Devin is having such a comeback
Nobody is really doubting the productivity gains of AI, and I would guess that companies would still be willing to pay the exponential if they must... But token spend is scaled and open source is now really good. It makes sense we are now spending energy to curb the runaway train
Extreme high-growth startups are only now thinking about token spend, but this has been an enterprise (read: Publicly Traded Company) concern since day 1
Want to understand how Cognition so quickly grabbed all the big banks and giant Fortune 100 enterprises as customers? Aligned incentives is the answer.
1. Being an independent company
Because we are not a model lab with $100B+ raised and $1T+ of data center commitments, we don't need to "catch up" by selling increasingly more expensive tokens
Nor do we need to push a specific model family to make margins.
Our only calculus is
- "Is this the best model for the job?"
- "Can we make the user more productive?"
- "Can we save the user money?" (increasingly)
This comes in the form of post-training research (building cheap + specifically tuned coding models) + new coding evals (FrontierCode benchmarks) + model routing (a lot behind-the-scenes of Devin's cloud harness).
You should be skeptical of an Italian restaurant pushing the expensive market price specials. Just like you should be skeptical of a model lab pushing the newest most expensive model
2. Enterprise cost controls
As a pre-requisite to selling enterprise contracts to the biggest companies in the world, you need really good spend controls.
These banks and big conglomerates have been token-sensitive since day 1. They saw the writing on the exponential.
For this reason, Devin has the most complete & robust spend controls of any coding agent on the market.
The boring stuff of orgs, users, scopes, limits. But it matters.
3. AI Productivity alignment
Cognition has an "AI Productivity Guarantee"
That means if Devin delivers less engineering value than you’re paying for, Cognition will fund your usage until it does, up to $10 million.
This is the tip of the iceberg and the one thing about Cognition that has been most novel to me since joining. Everything (and I mean everything) in our GTM motion is oriented around ROI.
Every conversation is rooted in the actual engineering tickets we are taking off the backlog.
I can only imagine what it would be like if instead conversations were rooted in "how can we entice users to burn through tokens"
How to keep AI spend flat while token usage grows exponentially: Not with friction and spend alerts. With better defaults, routing, and caching.
Better Defaults (not Usage Caps) – Engineers can choose any model they want, but defaults matter. We’re experimenting with defaulting to open weight models like GLM 5.2 and Kimi 2.7 through our LLM gateway, while still encouraging engineers to choose the right model for the task. 91% of our employees were never hitting their usage caps, so instead of lowering caps and driving up alerts, we're moving to cheaper defaults. Note that code reviews use a diversity of models, so they can check each other's work.
Better Routing – In our custom harnesses, we preprocess prompts and route to the best model for the job, considering cache hits and model pricing. For instance, you may want a frontier model for planning, but not for execution where they can be overkill. Ultimately, humans shouldn't be choosing models - AI can automate this task.
Better Caching – Cache misses are the easiest way to drive your cost up. All of our requests are cache aware, so we’re reusing a warm cache wherever possible. For example, our cache hit rate went from 5% → 60% in LibreChat once properly implemented.
Keep Context Lean – Start fresh sessions when switching tasks. Scope file context narrowly. Disconnect unused tools. Don't just compact. The goal isn't fewer tokens used, it's fewer tokens wasted.
Better Visibility – Our engineers can use as many tokens as they want, from whatever model they want, but we’ve made usage visible – and the more you spend on AI, the more impact we expect.
The goal isn't to suppress usage. It's to build the infrastructure that makes exponential growth sustainable.
Putting this into practice has cut our AI spend nearly in half, while our token usage continues to grow.
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Jared Zoneraich retweeted
JUST IN: Cognition AI announces it has surpassed $1,000,000,000.00 in annualized revenue run rate.
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I showed Devin a few sketches I did and told it to draw in the same style as me and it was pretty accurate
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Jared Zoneraich retweeted
Hey @washingtonsun just a heads up, your website fails accessibility testing. You might want to consider using some AI tools. They help a lot.
The Trump administration wants AI to conduct the tests that ensure blind people and those with other disabilities can access the government websites that Americans use to sign up for benefits or find other critical information.
The outcry was immediate.
washingtonsun.com/agencies/a…
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Jared Zoneraich retweeted
Cognition is shaping up to be the best business story in Silicon Valley history
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Jared Zoneraich retweeted
btw, @cognition is hiring:
- GTM ops
- people ops
- talent ops
- brand marketer
- dev community manager
- product marketer
- engineers across the board
- biz ops
- event producer
apply here: jobs.a16z.com/jobs/cognition…
Cognition has crossed $1B in annualized revenue run rate.
This milestone belongs to our customers. Here's how a few of them are building with Devin.
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Jared Zoneraich retweeted
Took a little less than 3 years but can finally say we did zero to one.
All credit goes to the incredible people I get to work with. Never would I have imagined getting to work with such a world-class team on such a history-defining technology.
Cognition has crossed $1B in annualized revenue run rate.
This milestone belongs to our customers. Here's how a few of them are building with Devin.
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Jared Zoneraich retweeted
adding Cognition’s growth to a chart that circulated last year (h/t @Yuchenj_UW), you can see that execution speed defines the company
this should not surprise those who saw Scott’s childhood mathcounts video, but hard to fully appreciate until seen in the context of the greats
Cognition has crossed $1B in annualized revenue run rate.
This milestone belongs to our customers. Here's how a few of them are building with Devin.
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Jared Zoneraich retweeted
$1B in annualized revenue. Less than two years after signing our first customer.
Hard to put into words how grateful I am to our customers, partners, and everyone who has believed in us along the way.
Milestones like this only happen because of trust. Customers trusting us with their most important problems, and an incredible team at Cognition relentlessly delivering for them.
We’re just getting started. The ambition from here is even bigger.
Cognition has crossed $1B in annualized revenue run rate.
This milestone belongs to our customers. Here's how a few of them are building with Devin.
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Jared Zoneraich retweeted
Cognition has crossed $1B in annualized revenue run rate.
This milestone belongs to our customers. Here's how a few of them are building with Devin.
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I gave a talk this week on "How Devin Builds Devin"
As a company, Cognition is growing at light speed. Which means we are shipping tons of code... which also means we need to review tons of code.
Luckily we have Devin
Covered the following topics:
- how much code we are actually shipping (40x!)
- Devin as a first-responder for alerts
- real examples of how Devin triages VM warnings
- SRE agent best practices
- finding that <2% of alerts led to code changes
- the war on AI slop & FrontierCode
- building async coding workflows
Thanks to @datadoghq for inviting me!
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18 months ago, I went all in on studying biology. I started by building a lab in my apartment (worms!) and later joined one at UCSF.
Meanwhile, models were making advances in the virtual world, but in the lab — they fell short.
We started C5R to fix this. See more below!
In 12 weeks, we built a research facility that is run entirely by AI.
AI designs, executes, and observes experiments end-to-end across biology, chemistry, and materials science.
We’re introducing SciUniverse: a benchmark that measures AI’s ability to do real-world scientific research.
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