Grainsman Joe

[Grainsman Joe @GrainsmanJoe]

If cost per task is lower, why does fable 5.1 burn through subscription usage at a far higher rate?

[Grainsman Joe @GrainsmanJoe]

Testing with Claude and Open AI to see if it’s possible to build in compounding understanding in the current framework. Would be a big step forward for usability across conversations and projects if it bares fruit.

[Grainsman Joe @GrainsmanJoe]

@minghuili14 Memory remembers the code.

Compounding understanding remembers why the code exists, what failed, and how that should change the next design decision.

That’s the difference between accumulating history and accumulating judgment.

[Grainsman Joe @GrainsmanJoe]

The next AI moat isn’t memory. It’s compounding understanding.

I’ve been thinking about where AI is heading after spending thousands of hours working with it over the last year.

Everyone talks about benchmarks, context windows, coding ability and agents.

I think they’re all important.

I also think they miss the point.

My current hypothesis is that the next major AI moat isn’t model capability.

It’s compounding understanding.

Memory isn’t the goal.

Everyone is building memory.

Remember my preferences.

Remember my projects.

Remember previous conversations.

That’s useful, but I don’t think it’s what people actually want.

People don’t want an AI that remembers what happened.

They want an AI that understands how the thinking got here.

Those are very different things.

What is compounding understanding?

Compounding understanding is where every interaction improves the next one.

Not because the AI remembers more facts.

Because it develops a better understanding of:

why decisions were made
which ideas were rejected
how your thinking evolves
where your blind spots are
where its own blind spots are

It’s not storing information.

It’s improving judgement.

That’s why context loss is so frustrating.

People say they hate losing context.

I don’t think they actually care about losing tokens.

They care about losing the collaboration.

You’ve spent days refining ideas together.

Built shared language.

Rejected dead ends.

Found principles that work.

Then you hit the context limit and suddenly you’re explaining everything again.

The frustration isn’t:

“It forgot my conversation.”

It’s:

“We lost our shared understanding.”

Benchmarks don’t measure this.

Benchmarks tell you how good a model is at solving isolated problems.

That’s useful.

But I don’t think it’s how expert users evaluate AI.

I don’t really care whether one model scores 92 and another scores 90.

I care whether, after six months, one of them consistently makes me think better.

Those are completely different metrics.

The real value is in the partnership.

If implementation becomes increasingly commoditized—and I think it will—then the value shifts somewhere else.

It shifts to the quality of the collaboration.

Can the AI challenge me?

Can it connect ideas I hadn’t linked?

Can it remember why we rejected something six months ago?

Can it stop me repeating mistakes?

Can it make me a better decision maker?

That’s where the value is.

The real moat

The real moat isn’t lock-in.

It’s accumulated understanding.

If an AI has spent years learning how I think, where I’m wrong, what changes my mind and how we solve problems together, replacing it becomes genuinely expensive.

Not because I can’t switch.

Because I’d be throwing away years of accumulated intellectual capital.

That’s a much stronger moat than another few benchmark points.

Final thought

Maybe this ends up being wrong.

It’s only a hypothesis.

But if I were building AI, I wouldn’t optimise for remembering more conversations.

I’d optimise for compounding understanding.

Because memory remembers the past.

Compounding understanding improves the future.

[Grainsman Joe @GrainsmanJoe]

Compounding Understanding

The next AI moat isn’t memory. It’s compounding understanding.

Memory stores information.

Compounding understanding is the process by which a human and AI continuously refine a shared mental model, where each interaction improves the quality of future reasoning and decisions.

The goal isn’t remembering what was said.

It’s understanding why previous decisions were made, which ideas were rejected, and how the thinking evolved.

If AI gets this right, I think it becomes much harder to replace—not because of lock-in, but because the collaboration itself compounds.

[Grainsman Joe @GrainsmanJoe]

@TradesByDave How about now ?

[Grainsman Joe @GrainsmanJoe]

@GrainsGorilla Well. Same idea here but seems they have the day.

[Grainsman Joe @GrainsmanJoe]

@Patrickcanion Quai Gustave-Ador 66, 1207 Genève, Switzerland

[Grainsman Joe @GrainsmanJoe]

@GrainsGorilla More buyers than sellers. Pretty simple stuff 😂

[Grainsman Joe @GrainsmanJoe]

@ArlanFF101 Looks like it’s happening now..

[Grainsman Joe @GrainsmanJoe]

@bofftrough @sentdefender Correct

[Grainsman Joe @GrainsmanJoe]

@sizov_andre Try again

[Grainsman Joe @GrainsmanJoe]

@GrainsGorilla Well done Chap. 🌾

[Grainsman Joe @GrainsmanJoe]

@GrainsGorilla Best guess on wheat is some dollar weakness followed by short covering basis charts. Beans and corn are a mystery.

[Grainsman Joe @GrainsmanJoe]

@GrainsGorilla Inverse Gorilla strikes again

[Grainsman Joe @GrainsmanJoe]

@sizov_andre CTA come in soon and funds are quite deployed. Probably need tariffs to lift before we see a move back to 490. (H)

[Grainsman Joe @GrainsmanJoe]

@GrainsGorilla Happy for you Chap.

[Grainsman Joe @GrainsmanJoe]

@GrainsGorilla Stop making me bullish..

[Grainsman Joe @GrainsmanJoe]

@GrainsGorilla Agree we are getting overdone on corn. Next move higher has to be destinations validating higher prices and commercials unwinding their short hedges.