Why you should build an AI product if you’re non-technical
Counterintuitively, it's often easier + cheaper than "traditional" products
Many of the startups that come to us looking to build an AI product — especially if they are less technical teams — assume they should “start with the non-AI version” to keep costs/complexity down. It’s an understandable assumption, but it’s often wrong.
Being scrappy is great, but it’s just not true that AI products are inherently more expensive. In fact, AI products are often cheaper/easier to build for non-technical teams than “traditional” products.
You’re probably not building OpenAI
The reason people assume AI is so expensive/complicated is because they confuse AI products with AI infrastructure. They read stories about the billions of dollars OpenAI and Meta are spending training their models and assume they would need to do the same to build their AI-enabled marketplace or assistant or whatever.
If you aren’t a machine learning engineer, odds are high the initial version of your startup idea doesn’t need a machine learning engineer. One of the truly amazing things about AI right now is how much power you can get off the shelf from OpenAI and the sea of other tools that have been built over the past few years. Talented engineers that are using AI themselves can build pretty slick AI products in a single day.
AI products can actually be thinner
In many cases, AI can actually decrease the cost/complexity of an initial product. As one specific example, I talked to a marketplace founder that wants to recommend products to new users after onboarding. Their vision “for the future” was to collect some unstructured text from the user, extract a few categories from a list of potential options via AI, and then serve the user products from those categories. But “to keep costs down” they wanted to build a longer onboarding flow that asked structured questions and then fed those answers into a rules-based scoring system to output the best categories.
If the AI version of your product is something you can easily do in ChatGPT or Claude, it’s also something you can easily do in code! Asking the LLM to look at a block of text and assign categories from a list of options is so much simpler than building a long onboarding wizard and an algorithmic scoring system, not to mention being closer to the vision for the product.
Productize yourself first
Another mistake I see people making in AI is assuming that an initial product needs to be a website or app where the users/customers are interacting with the AI themselves. The main driver of cost/complexity in that case comes from needing to build a polished user facing app.
I wrote about “wizard of Claude” products last week but a really good and lean go to market for a lot of AI products right now is:
Sell the product
Deliver it manually to start
Use AI to automate/scale your own processes
Once you’ve validated the idea and found the right iteration, remove yourself from the process entirely and build the user facing version
On top of being cheaper, it’s just so much easier to iterate quickly in that setup. There are amazing low/no-code tools for steps 3 and 4 as well (I’ve become totally hooked on Stack over the past week especially given how easily you can compare models across entire flows side by side).
Of course if you want some help or advice building an AI product, we’d love to help. The roboagencies we work with are great at building AI products quickly/cheaply.
Other notes:
Very cool demo from Yuwen Lu of Misty which allows you to use inspirational screenshots as input for code revisions. Remixing is a much more natural form of creation for many (most) people.
Great side by side review of Rollout (new AI landing page builder) vs v0. Pretty clear that landing pages are going to be the first real chip to fall in terms of what you can build end to end with AI.
GitHub Spark has entered the ring of the AI-built product race. Interesting and unsurprising that they highlight revisions and human collaboration as a key differentiator.
If there’s something that caught your interest here you’d love me to cover in more detail, let me know!