Alex Calder

CEO @coworkerapp previously @uber

[Alex Calder @ATCalder]

Today, we dropped the price of enterprise AI by 80%.

Same frontier AI. Same chat, cowork, and code experience.

Just 5x more tokens for the same spend.

Here’s how and why. 👇 Also, we made a video. Please enjoy.

[Alex Calder @ATCalder]

@KenWattana Incentives are not aligned

[Alex Calder @ATCalder]

@scottastevenson @Alfred_Lin Please go try https://t.co/2u1LtQzcgF on optimised and let me know if you still think this

[Alex Calder @ATCalder]

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.

[Business Insider @BusinessInsider]

Sam Altman said AI budgeting has recently become a "huge issue" for some companies, something that "never came up" earlier this year. https://t.co/P2zODBNmDp

[Alex Calder @ATCalder]

@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

[Alex Calder @ATCalder]

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.

[Aaron Levie @levie]

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 having strong evals for that domain, you’ll be able to cost/performance optimize effectively.

We’re still likely at the point where most use-cases will need frontier performance for the foreseeable future; but soon you will be able to peel off individual use-cases and send them to lower cost models once the quality is sufficient for the task.

Enterprises individually trying to figure this out themselves at scale will likely not be possible, so the products that can intelligently route these workflows to the right tier of model will be in a strong position to aggregate more demand.

[Alex Calder @ATCalder]

@levie @coworkerapp https://t.co/2u1LtQzcgF if you want to check it out

[Alex Calder @ATCalder]

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.

[Alex Calder @ATCalder]

@amir 👋

[Alex Calder @ATCalder]

@BrendanFoody we should chat

[Business Insider @BusinessInsider]

"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. https://t.co/WngPNl3MlB

[Alex Calder @ATCalder]

@garrytan worth checking out https://t.co/0S5p7Ukfew that optimally routes across open and closed models while preserving context for the chat/cowork use cases

[Alex Calder @ATCalder]

@TechCrunch @coworkerapp is the solution to this for enterprises btw

[Alex Calder @ATCalder]

@Jason I am once again directing you towards https://t.co/2u1LtQzcgF

[Alex Calder @ATCalder]

@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 https://t.co/2u1LtQzcgF and see for yourself

[Alex Calder @ATCalder]

@rabois @rabois @coworkerapp doing this across chat, cowork, agents. 5x more tokens for enterprises while maintaining frontier quality.

[Alex Calder @ATCalder]

👀 why limit budget when you can get 5x frontier tokens for the same spend by dynamically routing across open and closed models. @coworkerapp

[Techmeme @Techmeme]

Uber says it has limited all employees to $1,500 in monthly token spending per AI coding tool "to responsibly encourage agentic AI adoption" (@natlungfy / Bloomberg)

(Visit Techmeme dot com for the link and full context!)

[Alex Calder @ATCalder]

this is where products like @coworkerapp that dynamically route across open and closed models while maintaining enterprise security and context are huge. 80% token cost savings for same quality outputs

[Negligible Capital @negligible_cap]

*UBER SETS $1,500 MONTHLY CAP ON SOME AI CODING TOOLS FOR STAFF

$UBER officially reeling in the Claude budget after blowing their AI budget earlier this year.

Undoubtedly more companies to follow

[Alex Calder @ATCalder]

enter products like @coworkerapp that dynamically route to the best open / closed models. average 80% cheaper while maintaining frontier AI

[Flowee @FloweeTheHub]

Now it looks like AI subscriptions were always running at a loss and the companies have to massively increase their costs to break even.

Was it all just a ploy to make people addicted to the usage of AI and the fear of being unproductive again the lever to extract the monies?

[Alex Calder @ATCalder]

@AnnieLiao_2000 this is why the winner here will be platforms like @coworkerapp that intelligently route across any model while maintaining context. also 80% savings b/c most things dont need opus.

[Alex Calder @ATCalder]

this is why the winner here will be products like @coworkerapp that allow you to intelligently route across any model while maintaining enterprise context

[Annie ❤️‍🔥 @AnnieLiao_2000]

Deploying AI in enterprise is a mess right now.

We watched one company spend 8 months going in circles:

Month 1: Copilot (bundled in, seemed free)
Month 2: Rolled out ChatGPT to 20% of staff because Copilot underperformed
Month 3: Both tools sitting at ~20% adoption. Reassessing costs.
Month 4: Decided to go all-in on ChatGPT
Month 4-5: A rogue Claude user group quietly formed
Month 6: IT launched a formal Claude assessment
Month 7: Decided to switch the whole rollout to Claude
Month 8: ChatGPT Codex dropped. IT is now running another cost review...

This landscape will continue to change.

Enterprise AI adoption is not a procurement problem. It is a change management problem... And most companies are solving the wrong one.