The Good-Enough Model Era

When models get good enough, the game stops being about IQ and starts being about product.

I think we are already close to that line for a lot of normal use cases.

Not everyone. Not every job. But for "help me write this email", "explain this code", "summarize this doc", "debug my script", "what should I cook tonight", the ceiling is starting to feel less like raw reasoning and more like friction: context, speed, trust, and the shape of the interface.

I hear the same question from friends a lot (and I ask it myself): are the latest flagship models basically there? If we assume open source catches today's SOTA in ~9-12 months, costs keep dropping, and latency keeps improving, what happens next?

"Good enough" is a moving target, but it has a cliff

One mental model I like is the good-enough frontier.

Below the frontier, capability is the bottleneck. Users bounce because the model whiffs too often.

Above the frontier, capability is not the bottleneck. Users bounce because it takes too long, it forgets what you told it yesterday, it cannot see your actual files/calendar/inbox, it is inconsistent in tone and preferences, and it is hard to verify.

If you are building for the median person, the frontier is lower than we think.

A lot of people do not need an AI that can derive some wild theorem. They need an AI that can take their messy context and turn it into a useful next step, quickly, every time.

The next "wow" is reliability, not brilliance

I am excited about ASI for the same reason I am excited about any new scientific instrument: it might let us see and solve problems humans cannot.

But I am also trying to be honest about product adoption.

Most users are not shopping for "more intellectual horsepower". They are shopping for lower variance (fewer faceplants), better grounding (less confident nonsense), memory that actually feels like a relationship, and fast, cheap, always-on help.

Not "smarter", but "more dependable".

If you have used a top model on a hard task, you have probably felt the same thing I have: it is not that it cannot think, it is that it cannot hold the whole situation.

Longer context windows help, but the bigger unlock is retrieval + state: knowing what matters, pulling it back at the right time, and updating its beliefs when reality changes.

Once intelligence commoditizes, the business model shows up

If open source catches up quickly, you get a weird two-sided pressure on pricing: downward pressure from competition, switching costs dropping, and commodity inference, plus upward pressure from subsidies ending, GPU scarcity, bundling wins, and "good enough" becoming table stakes.

My guess: base model inference keeps getting cheaper, but the product gets more expensive.

The value migrates to everything around the model: data flywheels (your docs, your repo, your workflows), distribution (it is already in your browser, editor, OS), safety/compliance/auditability, and reliability engineering.

This is where I could see prices rising even if raw inference costs fall: people pay for the whole system, not the tokens.

And if competitive pressure lessens (because a few platforms own distribution), then yes, the market can start to look less like "cheap electricity" and more like "app store tax."

Open source helps keep the floor honest, but it does not automatically solve UX, integration, and trust.

The real race is for context, not just capability

When I look at Claude Opus 4.5 / GPT-5.x / the next frontier models, I mostly see: "okay, the engine is strong."

Now the question is: where does it live?

In your editor, with real diff context and tests. In your inbox, with permissioned access and strong guardrails. In your company, with a memory layer that respects boundaries.

The winner might be the system that can reliably answer: "what should I do next, given everything I know about you and your constraints?"

That sounds less like a single model and more like an operating system: retrieval, tools, policies, and a lot of boring engineering.

ASI might be overkill for most people (and still worth it)

Two things can be true:

1. ASI could be one of the most important things humanity ever builds.
2. Most people will not personally need ASI to feel like AI changed their life.

For everyday users, the "life changing" threshold is surprisingly mundane: it remembers, it is fast, it does not embarrass you, and it does the boring parts without supervision.

If we get those, the adoption curve looks different. The model stops being a novelty and becomes infrastructure.

And that is the weird part: the future might feel less like "smarter chat" and more like "everything you already do, but smoother".

If you are building in this space, I would love to hear what you think the real bottleneck is: raw intelligence, or the product wrapper around it. You can email me or reply wherever you found this.