Category: AI

This is from Brian Roemmele – Founder + Editor at Read Multiplex.

There is only one way.

Meet the competition on that level.
Do what they set out to do: make the AI open source.

Why?
Because the market will be moved by them at the lowest entry levels—and grasped by them at the highest. Ecosystems will form at kitchen tables, in garages, and in campus rooms around free access to performative models. That access creates a stickiness to the brand that can last a lifetime.

Instead, they cling to the idea that revenue must be extracted from the lowest entry point into their system.
This is short-term thinking.

It is lazy thinking.

It comes from people with naïve business experience and even thinner life experience.

Since the opening of the iPhone App Store, a generation of founders and venture capitalists has lived to replay the same tired success story: renting server time.

Exactly like the mainframe era of the 1970s.
That era is long dead.

OpenAI and Anthropic never got the memo.

There are dozens of far more substantial monetization systems that can be built around this new epoch of AI access.

I will not list them here (hire me and I’ll tell you).

They exist.

The tragedy is that the talent stack at both companies almost guarantees they will never see them—even when they ask their own models for solutions.

There is no moat that uniquely protects the base models we currently call LLMs.

A simple reality: every AI model will become good enough for 99% of use cases and collapse into low commodity access and pricing.

I predict this with clarity:

If they refuse to open-source the full weights for the entry-level market, they will be forced to subsidize usage below the cost of the electricity just to keep people on the platform—hoping someone buys the fries and the shake with the hamburger.

Let me make it so simple even an AI executive can understand it:

It makes better sense for the user to burn their own RAM, their own processor, their own GPU, and—most importantly—their own electricity running your model.

Read that again.
Read that again.
And now think.

I am not guessing.

This is the way it is going to be.

The only question is how fast the world catches up.

The magic of this period I call the Interregnum will belong to the new companies that form abstraction layers on top of what is now the electricity of computing: AI.

AI is electricity.

What we connect to electricity today is not just the lightbulb Edison imagined.

It is hundreds of trillions of motors—large and small—that power hundreds of trillions of systems, that also power hundreds of trillions of transistors we call computers… and yes, also lightbulbs.

I have had these conversations with people in the upper echelon of these companies.

They can’t understand the concept.

Yet they still grasp at a $20-a-month subscription from the common person, hoping that drop in the bucket will somehow monetize multi-billion-dollar investments.

It never will.

Hire the right people—people with real experience.
Encourage creativity instead of “yes, sir” to the CEOs.

Do that, and their names might still be known by their grandkids.

From AI Copium: NVIDIA reportedly just invested $5 billion into Ilya Sutskever’s secret AI company, Safe Superintelligence (SSI), after receiving rare access to its research.

So… what did they see?

In this video, we break down everything we know about SSI, Ilya’s recent interview with Dwarkesh Patel, the clues he gave about the future of AI, and why this could be one of the biggest AI stories of the year.

This is a refreshingly honest look at the state of AI based development, and a lesson in how to assess risk BEFORE any action. Don’t just give Claude access to your stuff. From Security Fest…

This is from Satya Nadella, CEO of Microsoft. This text is all from his post on X.com.

I’ve been thinking a lot about the future of the firm in an AI-driven economy.

This transition is different than any previous platform shift. In the past, we used digital systems to enhance human capital. This is the first time we can create a real cognitive loop between people and digital systems. That is a mind-bender, because it changes how we even conceptualize work inside an enterprise.

What is at stake is not some digital tool or system and its use, but how organizations continue to learn, build IP, differentiate, and thrive in a world where AI models can continuously absorb the expertise of humans and organizations and commoditize it.

Every company is going to have to build what I think of as human capital and token capital. Human capital comprises the knowledge, judgment, relationships, ingenuity, and pattern recognition of its people, while token capital is the firm’s AI capability it builds and owns.

Importantly, human capital does not become less valuable as token capital grows. It only becomes more valuable! I believe human agency will be the driver of token capital growth. Humans will set ambitious goals, connect dots across domains, build relationships, and recognize patterns that matter most. Without human direction, you have compute running in circles.

This means the real opportunity is not in picking the best model but instead in building a learning loop on top of models where human capital and token capital compound. You can offload a task, or even a job, but you can never offload your learning. The future of the firm is the ability to compound that learning across people and AI.

This requires a new architectural approach where every business is able to build agentic systems that improve over time, while still retaining control over their IP. A company should be able to switch out a “generalist” model without losing the “company veteran” expertise built into their learning system. This is the key “test” of your control and sovereignty in the era ahead.

Companies need to turn their workflows, domain knowledge, and accumulated judgment into AI systems that improve with each use. Private evals should capture whether a model is actually improving against outcomes that matter to the business (not just external benchmarks!). Private reinforcement learning environments should let models grow stronger on real traces from inside the organization. Its knowledge base makes institutional memory queryable and use of tokens more efficient.

This loop becomes the new IP of the firm. I think of it as a hill climbing machine. And unlike most assets, it compounds. Every improved workflow generates better training signal, which accelerates the accumulation of tacit knowledge unique to the firm. The companies that build this early will have an advantage that is hard to replicate, regardless of any new individual model capability.

The last thing any of us want is a world where every company across every sector is ceding value to a few models that eat everything they see. If all the value is accrued by only a few models, the political economy will simply not tolerate it. There is no societal permission for an AI future that hollows out entire industries.

Think about what happened in the first phase of globalization where entire industrial economies were hollowed out by outsourcing. The GDP numbers looked fine on the surface, but the displacement was real and the consequences are still being felt. Let us not bring that dynamic into the AI era, with a small number of AI systems capturing all the economic returns, while entire industries find their knowledge commoditized right out from underneath them.

In my view, our priority has to be building a frontier ecosystem, not just a frontier model, so value flows broadly across every company, every industry, and every country. One where every organization can own the learning loop that encodes its institutional knowledge, compounding its human and token capital.

This is the ethos I’ve grown up with where platforms enable more value on top than is captured inside, and where every company can continuously innovate and build value of its own.

When that happens, companies will create value for themselves and for the economy around them. Employees will see their expertise amplified and their judgment become part of systems that make it replicable and scalable and the benefits accrue to the companies and communities around them.

That is how companies drive value for themselves and the broader economy. And it is the stable equilibrium we should build together.

Just watch the video. Local AI but done better. Not for Mac or Windows but that’s great. Fire up some Ubuntu.

Sound familiar? Kinda scary….

Mo Biter, one of my online AI buddies (we don’t know each other) is venting a bit, and I think he’s right. It’s worth a listen.

Why are people shocked that this is going badly? Have you met people? I mean, seriously…people can NOT be trusted.

Using any version of an AI model is fine, but CHECK YOUR WORK, especially as a LAWYER.

I really like this channel, maybe you should too!