The Old Shape of Software Teams
For a long time, building software at any meaningful scale usually meant building fairly large engineering teams as well.
Even smaller startups would often end up with some variation of:
- CTO
- backend engineers
- frontend engineers
- DevOps
- QA
- infrastructure support
Part of that was just reality. Modern software systems are complicated, and historically a huge amount of engineering time disappeared into repetitive implementation work.
Boilerplate APIs. Dashboard plumbing. Authentication flows. Infrastructure setup. Deployment pipelines. Admin panels. Documentation. Integrations. Testing.
None of it glamorous, but all of it necessary.
A lot of software development used to feel a bit like constructing scaffolding around the thing you actually wanted to build.
What AI Is Actually Changing
I think AI is shifting that balance quite significantly now, but maybe not in the way people sometimes frame it.
The important part is not really "AI writes code", it's leverage.
A capable engineer with good judgement can simply move much faster than before.
Not because the difficult thinking disappeared, it hasn't, but because a lot of the repetitive implementation work is becoming easier to accelerate, automate, or partially offload and that changes things quite a lot.
A single engineer can now prototype systems, generate scaffolding, wire interfaces together, explore architectural approaches, write docs, debug issues and validate ideas at a pace that would have been impossible a few years ago.
Smaller Teams, Bigger Reach
The knock-on effect is that much smaller teams can now build products that previously would have required far larger organisations.
That changes startup economics in fairly obvious ways.
A lean technical team can often:
- prototype faster
- test ideas earlier
- reduce initial funding pressure
- iterate more aggressively
- stay operationally lightweight for longer
Historically, a lot of genuinely good ideas never really got explored because the barrier to entry was simply too high and hiring burden could kill momentum before a product even existed.
Infrastructure complexity was another problem. Then long development cycles on top of that. By the time some startups reached market they'd already burned huge amounts of time and money just assembling the machinery needed to begin.
AI lowers some of those barriers.
Not all of them, obviously, but enough that the landscape is starting to feel noticeably different.
Engineering Still Matters, Possibly More Than Before
What's interesting is that AI doesn't really remove the need for engineering skill at all and if anything, it shifts where the difficult parts live.
Implementation is becoming cheaper but judgement is becoming more important because AI accelerates everything, including mistakes.
Bad architecture gets built faster. Technical debt accumulates faster. Overcomplicated systems appear faster. Security mistakes happen faster and weird abstractions multiply if nobody is paying attention.
The hard part of modern engineering increasingly feels like:
- keeping systems coherent
- making sensible trade-offs
- avoiding unnecessary complexity
- designing maintainable architecture
- understanding operational risk
- knowing when not to build something
The strongest engineering teams are probably not the ones generating the most code anymore but the ones making the best decisions consistently and have the best technical process and organisation.
The Shape Of Teams Is Changing
Because of that, startup structures are starting to shift a bit as well, you see fewer rigid silos forming early on and more engineers becoming product-oriented generalists.
Smaller, higher-context teams. Specialists brought in flexibly where needed instead of existing as permanent departments from day one and adding to yearly cost spiral.
The role of experienced technical leadership feels increasingly important too, but the nature of the role is changing slightly. Less pure implementation. More orchestration, validation, systems thinking, product alignment, reviewing direction before complexity spirals off into the woods somewhere (which software projects absolutely love doing).
That's probably one of the more interesting parts of this whole shift honestly.
More Startups Means More Opportunity
The bigger long-term effect may end up being less about the tooling itself and more about what the tooling enables. If smaller teams can build meaningful products with less upfront capital, then more startups become viable and accessible.
More experimentation becomes possible. More niche ideas get explored. More people can build around knowledge or expertise they already have without immediately needing a huge engineering organisation behind them.
That obviously creates more competition too, but I think it also creates far more opportunity overall. Most ideas still won't succeed. That part probably never changes.
But ambitious technical projects are becoming increasingly accessible to smaller groups and independent founders in a way that genuinely feels different from previous waves of software development. And honestly, it still feels like we're fairly early in that transition.