Pierre Schurmann

Brazilian serial entrepreneur and investor. Founder @ CEO @nuvinigroup, $nvni Latin America’s leading serial acquirer.

[Pierre Schurmann @Xurmann]

Contrats!

[Josh Woodward @joshwoodward]

🇧🇷World-changing AI companies are coming from Brazil.

That’s why we’ve officially expanded our Google AI Futures Fund to Brazil, partnering with venture capital leader Monashees to launch the Gama Fund.

We’re looking for an elite cohort of deep tech founders and will offer:

- Early access to Google DeepMind models
- Up to $2M in co-investment
- $350k in Google Cloud & Gemini credits
- Direct co-development with Google engineers at our new IPT Open campus hub

Apply today: https://t.co/8sv9seJLdB

[Pierre Schurmann @Xurmann]

@zenorocha @resend Congrats! You guys Rock!

[Pierre Schurmann @Xurmann]

I like the idea. We are working with a concept called ROT (return on tokens).

We look at that same vector ex ((475 hours saved * $58/hr - $10,000 AI tool/ $10,000 AI tool) * 100 = 174% ROI).

But also try to value the time value of money. A product shipped in 10 days instead of 6 months creates a competitive advantage that is not only about cost reduction. But also about value creation. If my competitor can launch 1 significant product interaction per year, and I can launch one per month (or per week), I should evolve my product exponentially in relation to him.

Savings have a floor (zero). Value creation is infinite

[Pierre Schurmann @Xurmann]

Im experimenting with trying to measure ROT (Return on Tokens). Created a very simple repo as a first draft. Would love to get feedback on how it could be improved. https://t.co/8AgjttkjIm

[Pierre Schurmann @Xurmann]

Imagine you own a farm. And horses helped you on the whole cycle of preparation of the terrain, seeding and even possibly harvesting. And a technology came along that increased farming productivity by 10x

It would be nice to say that you will use 10X horses
for that.

Fast forward 10 years. Horses are nowhere to be seen.

The productivity allowed tractors, that were " expensive " at 1x, to become economically viable at 5x. And incredibly better than horses at 1x

Now think about this in human terms. And imagine horses don't come to work with a bad temper

[Aaron Levie @levie]

Take whatever number of people you thought might be in jobs related to AI deployment in the enterprise and multiply it by 10. Then probably 10 again.

A major topic that keeps coming up in talking to CIOs across enterprises of all sizes and industries is the implementation gap for getting agents to work at scale and organizations on mission critical work.

As the task goes from implementing a chat system that’s basically an LLM plus search, to connecting to real production systems that both can deliver meaningfully better productivity gains but also introduces meaningfully more risk, a whole new set of work has to be done.

You have to ensure the right level of protection of data, updates to access control controls, migration of legacy systems to common modern platforms, create observability across what agents are doing, implement new workflows, figure out the human in the loop moments, drive the change management of the new workflows, and more.

Then, all of a sudden the model capabilities get updated and you have to do a set of the above steps over again. Half of what you’ve done is obsolete, and the other half needs to be upgraded to take advantage of new capabilities. Or, token budgets run hot and you have to peel off some of the workloads to lower cost models that will be more cost effective. But then you have to go through those same steps.

Enterprise are trying to figure out what is the right set of roles to go and implement the systems in their organization to ensure that the workflows are actually being executed properly, ensure it’s not just slop being produced, and to make sure their organization remains safe and secure.

Many companies are starting by repositioning existing IT talent in these functions, but there’s also a growing need for the equivalent of internal FDEs to go take on these tasks in an enterprise. The looks incrementally closer to software engineering than it does traditional IT implementation.

Next, almost all AI vendors (labs and the software players) will have some form of next-gen FDE or Applied AI architecture functions to help support these use-cases. The benefit here will be these companies have an incentive to make their capabilities work well so they can bring best practices from a range of customers they’re seeing and directly from the product innovation.

And finally, we’re seeing the rise of all new AI services firms or major parts of existing services firms move into AI implementation. Companies will often want to bring in ostensibly neutral players that can work across their tech stack but also have seen best practices across their vertical. There are going to be tons of new service providers that get launched to do this, and many will eventually go and disrupt (or get acquired) by the larger player.

Either way, all told, we’re in for years of AI diffusion, and along with it tons of new roles and areas of work to be done to deploy AI at scale.

[Pierre Schurmann @Xurmann]

@zenorocha @cpenned The ripple compounds @zenorocha. Force for good always multiplies

[Pierre Schurmann @Xurmann]

@zenorocha Awesome !!

[Pierre Schurmann @Xurmann]

Everyone's talking about the so-called SaaSpocalypse. Part of the concern is fair.

But in every evolutionary cycle there are survivors and there are predators that take out the weak. We've been positioning ourselves to be predators for a while now.

There are four layers where SaaS companies can (and probably should) be playing right now.

First: use AI to cut costs. The obvious one. If you haven't started, you're already on your way towards extinction.

Second: embed AI into the product itself to drive more revenue.

Third: look at where your customers' money is actually flowing and build integrated applications inside your business to capture a bigger slice of it.

Fourth. And this is the one most SaaS companies still haven't grasped: build products that combine what you already do with what companies "adjacent" to you do. And build things that were impossible before. If you have a deep, long-term relationship with your customer, you can become their "one stop shop" by combining software, services as software, and whatever else they're after. At marginal cost to you. Possibly at zero cost to them. That's the real risk for any SaaS that doesn't move.

Be smart. Be fast. Adapt. That image in your rearview mirror might be closing in faster than you think. And it might even be us.

[Pierre Schurmann @Xurmann]

@brunopinheiroms Não otimize pensando em tokens. Bezos não criou an Amazon pensando que ia entregar café d manhã em 1h ou free shipping. Desenvolva pelo que não é mas em breve poderá ser

[Pierre Schurmann @Xurmann]

@Jason Im in

[Pierre Schurmann @Xurmann]

Your ability to do that with people is limited. You can't hire 100kppl in a week and expect them to adapt that way. Compute scales in a way that your $ is the limit. The return, not so

[Pierre Schurmann @Xurmann]

people are thinking about the tokens/labor exchange wrong. If you hire 1000 people, they won't in a 1 week. They need to settle, learn, avoid internal politics. And then start. if you take the same $ and invest in tokens, They will deliver returns in 4 hours. however

[Pierre Schurmann @Xurmann]

@fseixas Boa. Eu estou mais para vibe everything that is digitally made and has a record (ja foi feito antes varias vezes com sucesso e insucesso). Then improve, accelerate and focus on ROT (return on tokens). Se >0, Go. Se >1, quadruple deploy

[Pierre Schurmann @Xurmann]

@pedroh96 @brexHQ Congrats! Parabéns!! Will try it now

[Pierre Schurmann @Xurmann]

@HeyGen CLI

[andrew chen @andrewchen]

prev startup advice: do things that don't scale

2026 update: do things that don't scale, then build AI agents to scale them

[Pierre Schurmann @Xurmann]

@fseixas @leonardodias Com sua experiência deveria fazer como eu e criar o seu