Like most tech leaders, I’ve spent the last year swimming in the hype: AI will replace developers. Anyone can build an app with AI. Shipping products should take weeks, not months.
The pressure to use AI to rapidly ship products and features is real. I’ve lost track of how many times I’ve been asked something to the effect of, “Can’t you just build it with AI?” But the reality on the ground is much different.
AI isn’t replacing engineers. It’s replacing slow engineering.
At Replify, we’ve built our product with a small team of exceptional full-stack engineers using AI as their copilot. It has transformed how we plan, design, architect, and build, but it’s all far more nuanced than the narrative suggests.
What AI is great at today
It can turn some unacceptable timelines into a same-day release. One of our engineers estimated a change to our voice AI orchestrator would take three days. I sanity-checked the idea with ChatGPT, had it generate a Cursor prompt, and Cursor implemented the change correctly on the first try. We shipped the whole thing in one hour: defined, coded, reviewed, tested, and deployed.
Getting it right on the first try is rare, but that kind of speed is now often possible.
It’s better than humans at repo-wide, difficult debugging. We had a tricky user-reported bug that one of our developers spent two days chasing. With one poorly written prompt, Cursor found the culprit in minutes and generated the fix. We pushed a hot fix to prod in under 30 minutes.
Architecture decisions are faster and better. What used to take months and endless meetings in enterprise environments now takes a few focused hours. We’ll dump ramblings of business requirements into an LLM, ask it to stress-test ideas, co-write the documentation, and iterate through architectural options with pros, cons, and failure points. It surfaces scenarios and ideas instantly that we didn’t think of and produces clean artifacts for the team.
The judgment and most ideas are still ours, but the speed and completeness of the thinking is on a completely different level.
Good-enough UI and documentation come for free. When you don’t need a design award, AI can generate a good, clean use interface quickly. Same with documentation: rambling notes in, polished documentation out.
Prototype speed is now a commodity. In early days, AI lets you get to “something that works” shockingly fast. Technology is rarely the competitive moat anymore, it’s having things like distribution, customers, and operational excellence.
Where AI still falls flat
It confidently gives wrong answers. We spent an entire day trying to get ChatGPT and Gemini to solve complex AWS Amplify redirect needs. Both insisted they had the solution. Both were absolutely wrong. Reading the docs and solving “the old-fashioned way” took two hours and revealed the LLMs’ approaches weren’t even possible.
Two wasted engineers, one lost day.
You still need to prompt carefully and review everything. AI is spectacular at introducing subtle regressions if you’re not explicit about constraints and testing. It will also rewrite perfectly fine code if you tell it something is broken (and you’re wrong).
It accelerates good engineering judgment. It also accelerates bad direction.
Infra, security, and scaling require real expertise. Models can talk about architecture and infrastructure, but coding assistants still struggle to produce secure, scalable infrastructure-as-code. They don’t always see downstream consequences like cost spikes or exposure risks without a knowledgeable prompter.
Experts still determine the best robust solution.
Speed shifts the bottlenecks. Engineering moves faster with AI, so product, UI/UX, architecture, QA, and release must move faster, too.
One bonus non-AI win helping us here: Loom videos for instant ticket creation (as opposed to laborious requirement documentation) result in faster handoffs, fewer misunderstandings, more accurate output, and better async velocity.
So what does this mean for startups?
- AI lets great engineers become superhuman: Small teams can now ship at speeds that used to require entire departments.
- The bar for engineers goes up, not down: Fewer people, but they must be excellent.
- Technology alone is no longer a reliable moat: Everyone has AI. Your defensibility is things like distribution, network, brand, operational excellence.
- AI won’t 10x everything: Some parts will fly. Others still depend on time, people, and judgment.
- Leaders must be hands-on with AI and technical strategy: Without that, AI only introduces new bottlenecks and issues.
The reality check
AI isn’t replacing engineers. It’s replacing slow feedback loops, tedious work, and barriers to execution.
We’re not living in a world where AI writes, deploys, and scales your entire product (yet). But we are living in a world where a three-person team can compete with a 30-person team — if they know how to wield AI well.
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