What AI changed in software development and globally

AI is now in almost every part of how software gets made, and most of you have noticed. Your PM drafts specs with it. Your marketing lead ships landing pages with it. Your cousin built an app over the weekend with it. Here is the thing I keep seeing from the client side: the rules of building good software have not changed. Requirements still need to be understood. Systems still need to be designed. Code still needs to be correct, secure and maintainable. What changed is the volume of output, and with it, the load on whoever has to sort the good from the garbage.

More builders, thinner understanding

More people than ever are creating digital products. That part is genuinely good: the barrier to a working prototype dropped close to zero. But the number of people who understand what they are building did not grow at the same rate. The experienced engineers are still roughly the same crowd. They are now a smaller share of a much larger pool. Expertise did not disappear; it got diluted. The result is a lot of software that runs, demos well, and that nobody involved can fully explain.

AI amplifies whatever you bring

AI is a multiplier. Multiply real engineering judgment and you get more good work, faster. Multiply the absence of it and you get more bad work, faster. That is why people without an engineering background now produce more garbage with AI than they did without it. Not because they got worse, but because before AI their limits stopped them early. Now nothing stops them until production does: the leaked API key, the N+1 query that melts the database at 200 users, the payment flow that double-charges on retry. For the average buyer, this makes the market very hard to read. A low-effort product and a solid one look the same in a demo. Same clean UI, same confident README, same “enterprise-grade” on the landing page. The difference lives in places a non-technical founder does not look: error handling, data model, test coverage, how it behaves under load and on the bad day.

Why outsourcing shops are losing clients

Classic outsourcing companies sold capacity: a team of hands, billed by the hour. AI made raw capacity cheap. So clients are doing the math differently. Instead of renting a team of five, they hire one or two strong people in-house who know how to use AI tools well. They keep the knowledge, cut the coordination overhead, and move faster.

The shops that survive this will be the ones selling judgment and accountability, not headcount. The ones still selling seats are competing with a subscription. The middle is collapsing; the top is pulling away Junior, middle, senior used to be fairly readable from the output. Not anymore. A beginner with a good AI setup now produces code that looks like a mid-level developer wrote it. On a take-home task or a short trial, the two are often indistinguishable.

Where the grades still separate is everything the AI does not do for you: • Knowing which problem is actually worth solving • Spotting that the generated code is plausible but wrong • Designing a data model that survives the next three feature requests • Owning the outage at 2 a.m. and knowing where to look

That is why demand at the top is growing. Someone has to steer, review and take responsibility for the flood of output. Meanwhile the middle of the market is full of noise: CVs, portfolios and agencies that all look alike.

Proof still works. People just skip it.

The tools for telling good from bad never stopped working. Code review. Tests. A track record of systems in production. References from people who lived with the result for two years, not two weeks. What changed is how often they get ignored. When output is fast and looks polished, checking feels like friction. So teams merge without reading, buyers hire on a demo, founders trust a portfolio of screenshots. The cost of skipping proof has not gone down. It has just been deferred, and it usually arrives with interest.

How to cut through the noise

If you are a founder or PM hiring a developer, an agency or a technical advisor, a few checks go a long way:

1. Ask them to explain, not to show. Pick a decision in their past work and ask why it was made that way. Real experience comes with trade-offs and regrets; generated work comes with a nice screenshot.
2. Look for systems that lived. Ask what they have kept running in production for a year or more, and what broke along the way.
3. Give them broken code, not a blank page. Anyone can generate something new. Finding the bug in plausible-looking code is where skill shows.
4. Ask how they use AI. A strong engineer has a clear answer: where it saves time, where they do not trust it, and how they verify its output.
5. Keep the knowledge close. Whoever builds your product should leave it documented and understandable. If only the AI and one contractor know how it works, you have a risk, not an asset.

The game is the same

AI tools are available to everybody, and that is fine. But access to a tool was never the bottleneck. Understanding was. The game still rewards the same things it always did: knowing what to build, building it so it holds, and being accountable when it does not. The only real change is that there is now much more garbage to manage on the way there. Pick the people who can manage it, and check that they actually can.