Today we’ve raised $52M Seed and we are announcing the public launch of S2.1 Pro.
>It can clone a voice from 5 seconds of audio
>2x faster than Cartesia & 1/6th the cost of Eleven Labs
>most expressive model with word level control over emotion, intonation, pacing etc
We support frontier AI companies including HeyGen, LiveKit, Retell, Sanas, and OpenArt all run our model in production.
If you're a business and we can't cut your voice AI costs by 50%, we'll give you 1 year of Fish Audio for free.
Book a demo: https://t.co/vHkyZf9JoG
To celebrate our first birthday, we'll give you 1 month of S2.1 Pro for free. Like, retweet, and comment “Fish” to get it.
I’ve been thinking about how I actually use ChatGPT at work—and the honest answer is: not simply as a chatbot, and never as a shortcut.
I use it as a thinking partner between an idea and its execution.
It has helped me turn messy business challenges into strategies, proposals, workflows, KPIs, financial models, presentations and tools that people can actually use. I’ve used it to analyze marketing campaigns, design customer-service systems, build websites and datasets, improve business processes, and prototype AI agents that connect voice, automation and CRM platforms.
As a consultant, it helps me translate complex technology into business language. As a professor, it helps me create practical courses, exercises and simulations that make AI and knowledge management understandable to executives. In the wine industry, it has supported research, product positioning, events and initiatives to strengthen the ecosystem in Aguascalientes. In creative and social-impact projects, it has helped transform meaningful ideas—including initiatives supporting women affected by breast cancer—into clearer messages and executable plans.
Sometimes the task is strategic. Sometimes technical. Sometimes creative. And sometimes it is simply solving an urgent problem that appeared five minutes ago.
But the greatest thing ChatGPT has given me is not speed. It is range.
It allows me to move from strategy to code, from data to storytelling, from a classroom to a CRM, and from a blank page to something real—without losing the human judgment, context and responsibility the work requires.
AI has not replaced my experience. It has made more of that experience usable.
That, to me, is the real future of work: not humans versus AI, but curious and responsible people learning to think, create and execute with it.
We are no longer just asking AI questions.
We are learning how to build with it.
We’ve received notice that the Department of Commerce has lifted export controls on Claude Fable 5 and Mythos 5.
We'll begin restoring access tomorrow, and will share an update soon.
We’re grateful to our users for their patience, and to everyone who worked with us on redeploying the models.
La IA ya no solo responde: ahora ejecuta.
Agentes, Copilot, Gemini, Codex, Claude Code y nuevas formas de trabajar con tu stack tecnológico.
Este 30 de julio daré la Masterclass de IA Aplicada para Empresas junto con @rlempresarial .
Inscríbete aquí:
https://t.co/oZUKQBQzMR
I think the most dangerous phrase in any organization is “that's how we've always done it.” That's probably killed more companies than any competitor ever has.
The equation is fairly straightforward:
Competent employees x AI tokens = Accelerating business & market share gain
Incompetent employees x AI tokens = slop
Companies are now realizing they have a lot of shitty employees.
They aren’t going to permanently cut spend on tokens. They can’t afford to because of game theory.
So instead they will fire the employees they believe are incompetent to make room for higher token budgets for those that are competent.
Lots of orgs however have a managerial class that doesn’t optimize for share gain and winning in general.
Thats fine. A wave of startups and existing platforms who can effectively leverage AI to expand scope of their business will crush the incompetent at a rate that will leave analysts and managers dizzy.
Change is coming. Fast. And reflexively the faster the change the higher the panic the lower the ROI threshold the more revenue and capital accrues to the labs the faster the models improve. And so on.
The bitter lesson in 26 words:
Don’t be distracted by human knowledge, as AI has been historically.
Instead focus on methods for creating knowledge that scale with computation, like search and learning.
We’ve agreed to a partnership with @SpaceX that will substantially increase our compute capacity.
This, along with our other recent compute deals, means that we’ve been able to increase our usage limits for Claude Code and the Claude API.
Mexico is the absolute outlier in the OECD: workers log the most hours on Earth (~2,200+ annually) yet deliver relatively little economic output per hour—despite huge advantages like proximity to the USA.
Something went seriously wrong.
My take: the education system.
Even grads from top STEM unis often have shockingly weak fundamentals (based on interviews).
Sure, brilliant Mexicans exist, but the system fails the average citizen badly.
Work smarter, not longer.
Fix education → unlock potential.
Study math, just ask Peru 🇵🇪