Five years ago, building a product meant spending time and money before anyone saw it working. AI agents now generate the UI, write the code, and test the output, and a small idea can be running in minutes.
That moves the work rather than removing it. In episode 13 of the AI ThoughtMakers podcast, Sarika Gautam, V P Engineering at GeekyAnts, talks through what the product studio looks like now- what AI takes over, what stays with people, and where founders get caught out.
Watch the full episode here:
What AI Actually Takes Over
Founders keep repeating that AI can replace 70% of software development work. For repetitive tasks, that holds.
Take development in any tech stack. The syntax is common and the patterns repeat, which is exactly what these tools handle well. The part they cannot reach is the idea underneath. Capturing business requirements takes a discussion with the product owner, and a founder chatting directly with an AI interface often does not get what they wanted, because describing the idea accurately to a tool is a skill of its own.
So the developer's job moves up a level. System design, describing the problem to the tool, translating requirements into executable steps- the role looks closer to an architect than an executor.
Speed is Settled. Cost is the Next Question.
Everything still requires a plan.
The early excitement was about generating an entire codebase, and the tools do it fast. Getting something usable out of them depends on detailing the requirement properly first.
Now that the speed is proven, the second question is what it costs. Tokens get consumed on every regeneration, and a build with no plan behind it turns expensive quickly.
Why Startups Should Prototype First
If you are starting up today, put the prototype before the strategy work.
Ideation to prototype is a quick move with AI tools, and time matters when you are trying to reach the market or just validate that the idea works. Getting something in front of people early is the better approach.
This is also what changed economics. An idea used to go through design, then development, with the prototype arriving late- after real money and effort were already committed. A small idea can now be seen in action in minutes, so founders get something to play around with before the budget goes out. Ideas that were not viable two years ago are viable now.
Every company is heading the same way. Some are already AI companies and the rest have it on the roadmap, for the same two reasons: better productivity and faster delivery.
Where Founders Get Caught Out
The biggest mistake is treating the prototype as production ready. Something working in a prototype does not make it the production goal, and the point keeps needing to be made.
The biggest lie sold alongside it is that AI can replace humans. It replaces repetitive work. The pitch suggests it can do anything, and the limits only show up when you probe the tool or adopt it for a real use case.
There is a longer-term version of this too. By 2030, the market looks saturated. App stores already carry any number of apps in the same domain serving the same need, and a new release gets met with "not another app." Wider adoption of AI tools means more people building in the same fields, and saturation follows.
What Happens to the People
Repetitive tasks go. Documentation used to need dedicated writers and is now generated by AI tools, and repetitive coding goes the same way.
Juniors do not go with them. If time replaces the current generation, someone has to step in, which means continuing to hire juniors and giving them the experience to take over when the point arrives. Skipping the pipeline solves a cost this year and creates a gap later.
The skill that gains value is product-level thinking. Anything you dictate can be generated, so the weight shifts to deciding what should be built. A product has to work for a larger audience than the person describing it, what looks correct to you may not be accepted by others, and how non-technical people adapt to the product is the part that needs clear thinking.
The same logic applies to whole industries. Adapt to change or become outdated- traditional industries using AI to improve efficiency are not going out of business. Outsourcing shifts rather than ends, because work that used to need a team can now be handled by a single developer with the right skills, at least on smaller projects.
The Rapid-Fire Round
The episode closes with a rapid-fire round.
AI or human creativity? Human creativity.
Startup or enterprise? Startup for early ideation, enterprise as the stable approach.
GPT or open source? Open source.
Coding or prompting? Prompting.
The first and last answers sit together. Prompting is the fastest way to produce the work, and creativity decides whether the work is worth producing.
What this Means for your Next Build
Change has always been the way of life, and the people who move with it benefit the most. AI does not wait, and the companies experimenting with it now are the ones seeing the returns.
Use the speed where it pays: ideation, prototypes, validation, and the repetitive build work. Keep the planning, the requirement discussion, and the product-level thinking with the people who own the outcome.
That gap between a working prototype and a product is where our AI-Powered Product Engineering practice works- a structured studio to ideate, validate, and build products that hold up after the demo. If you have a prototype and are deciding what it takes to ship it, talk to us.
