Alex Calder @ATCalder
CEO @coworkerapp previously @uber coworker.ai San Francisco, CA Joined January 2011-
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In every convo I've had with folks at Coinbase, it's been impressive how far ahead @brian_armstrong and the team have been on AI for a 'non AI company' - whether that was building out shared company context before MCP was a thing or pushing way ahead on coding agents. 99% of companies don't have the time or capability to roll this themselves, despite still having the same burning token cost problem. This is why we built @coworkerapp. We're seeing a huge shift in our customers' use towards open weight models securely hosted in the US.
What @coinbase did is impressive but 99% of companies can't do this. @coworkerapp let's any company do this out-of-the-box
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
Most of the convo on open weight models has centred on a handful of AI-native companies post-training models on their data (Fin, Cursor etc), and this being the path others will follow. The vast majority of companies won’t do this. Yet they’ll still have strong incentives to adopt open models alongside anthropic/openai (since open models are 10x cheaper at or near frontier performance and also to avoid lock-in). This is where we’re going to see an explosion of companies buying context and routing layers: platforms that let them generate portable context across their apps, agents, and employees’ AI use and route it across open, closed and even on device models. And the Fable convo shows that this isn’t just about maximising token budgets or providing an easy switchboard across the latest models. What’s even more important to companies is their data and IP sovereignty: ensuring that they own the incredibly valuable shared context layer that captures their company’s workflows, IP and how employees work with and alongside AI and agents. Companies just can’t risk a single AI lab owning that piece of infrastructure and being blocked or price gouged on something that’s becoming existential to their IP and how they do business.
The rebel alliance wins if I recall correctly
For the first time in ~3 years, it feels like the AI table has been flipped over. Yes, the labs and hyperscalers will have the highest chance of resetting it before everyone else, given their vast capital, frontier model, and compute advantages. But there is now a window for a
Worth reading
If you understand my post this means I am bullish the inference providers like @AskVenice Disc. Long VVV
@scottastevenson @Alfred_Lin Please go try app.coworker.ai on optimised and let me know if you still think this
Enter companies like @coworkerapp that pair every task with the right context and model to do the job at frontier quality for the lowest cost.
Sam Altman said AI budgeting has recently become a "huge issue" for some companies, something that "never came up" earlier this year. bit.ly/4uxIGnv
@sqs I am not sure this ‘aggregate token cost is still greater’ argument still holds with Kimi k2.6, especially when paired with a sonnet/opus advisor it can call if need be
When you ask AI to do something, you’re contracting out work. Today there are 1-2 contractors. Open models launching at frontier quality, eg kimi k2.6, is going to make this a giant marketplace problem.
As token budgets take on a larger part of operating expenses over time, model routing is the inevitable conclusion. This is also one of the biggest areas of differentiation for the applied AI layer over time. By understanding the different work patterns in your domain, and
@levie @coworkerapp app.coworker.ai if you want to check it out
Agree, except for ‘we’re still likely at the point where most use-cases will need frontier performance for the foreseeable future’ - at @coworkerapp we route chat, cowork, agent tasks across open and closed models depending on task (or even subtask). Our optimised mode is 75% cheaper than opus while maintaining frontier quality.
@BrendanFoody we should chat
"Right now we're spending more on tokens for our internal agents than we are on employee head count," Mercor's CEO Brendan Foody said. bit.ly/4uObeKg
@garrytan worth checking out coworker.ai that optimally routes across open and closed models while preserving context for the chat/cowork use cases
@TechCrunch @coworkerapp is the solution to this for enterprises btw
@Jason I am once again directing you towards app.coworker.ai
@buythedipagain @negligible_cap @coworkerapp 'economies of scale' - Opus 4.8 at $5/m in and $25/m out and 2.5 chars/token... go try app.coworker.ai and see for yourself
@rabois @rabois @coworkerapp doing this across chat, cowork, agents. 5x more tokens for enterprises while maintaining frontier quality.
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