The AI Paradox: Why Capability Doesn't Equal Throughput

I have been thinking about this a lot lately. Everyone keeps saying AI makes coding faster. And yeah it does. But the reality is way more complicated than people are making it out to be. Let me walk through what I have actually seen happen.

What Actually Changed

Before AI the bottleneck was always that translation step. You had an idea but turning it into code meant you needed someone who knew the syntax, understood the frameworks, could handle edge cases, and knew the tooling. That limited who could build stuff and it limited how fast even good engineers could move.

AI changed that. Now a decent engineer can just describe what they want instead of worrying about all the mechanics. Skip the boilerplate, jump between stacks way faster, and prototype in hours what used to take weeks.

And at the same time AI lowered the floor. People who aren't engineers can now make something that actually runs. Sometimes pretty quickly.

But here is the thing. If everyone is more capable then why doesn't output just explode? That is the weird part.

What Happens to Engineers

When an experienced engineer first gets their hands on AI tools there is this honeymoon phase. You are like "I can finally build everything I have always wanted." Your velocity spikes. Side projects everywhere. Confidence goes way up. It is real.

Then you think "why don't I just do this myself?"

So you start a company.

The Early Phase

At first AI feels like having a small team in a box. One person can design, code, write copy, build infra, and ship MVPs. This phase is intoxicating and honestly it is real. You are genuinely more productive than you have ever been in history probably.

But then the bottleneck shifts.

Where the Bottleneck Actually Goes

AI doesn't remove these constraints:

• Choosing the right thing to build
• Distribution
• Trust
• Timing
• Market saturation
• Long-term maintenance
• Differentiation

So instead of "can I build it" the bottleneck becomes "why would anyone care?" Why this over the thousand other AI-assisted projects? How do I get attention?

This is where a lot of solo engineer companies just stall. Or fail quietly.

Why Non-Coders Don't Actually Replace Engineers

Non-coders can build a thing that runs. They can build a demo. They can build a shallow product.

But they struggle with debugging emergent behavior, architecture decisions, scaling, security, performance tradeoffs, and knowing when the AI is confidently wrong.

They can get in the game but they rarely dominate it without pairing up with someone technical or learning deeply over time. So engineers are still advantaged. Just not exclusive anymore.

What Actually Happens

Inside companies fewer engineers can do the work of many. Management often misunderstands this and just piles on work instead of increasing quality. Teams shrink or stay flat while scope grows. Senior engineers become "force multipliers" and juniors struggle more. It is not great.

Outside companies there is an explosion of small narrow products. A lot fail quietly. A few succeed because of timing, niche insight, or distribution. Most don't though.

Why Productivity Doesn't Feel Like It Explodes

Because AI mostly increases option space and not throughput.

You can try more things but attention is finite. Markets saturate faster. Competition increases at the same rate as tooling power.

So relative advantage stays surprisingly stable.

Where This Probably Settles

Engineering becomes less about typing code and more about judgment, systems thinking, taste, and restraint.

Companies shift toward fewer builders, more product clarity pressure, and faster iteration. Which means faster failure.

Solo engineers who succeed build very small and very specific tools. They own distribution through audience, trust, or niche access. They treat AI as leverage and not identity.

The engineer who "can do anything" eventually learns that "I can build anything but I can't make everything matter."

That is the real new bottleneck.