7 Go-To-Market Lessons from the Founder Who Built His AI Company Before ChatGPT Existed

7 Go-To-Market Lessons from the Founder Who Built His AI Company Before ChatGPT Existed

The conventional wisdom says you should rush to market when you spot an opportunity. Alon Talmor did the opposite—he spent four years earning a PhD while the technology wave he’d eventually ride was still forming in research labs.

In a recent episode of Category Visionaries, Alon Talmor, CEO and Founder of Ask-AI, shared the unconventional go-to-market strategy that positioned his enterprise AI platform ahead of the November 2022 ChatGPT explosion. His approach challenges nearly every startup playbook written in the past decade.

Lesson 1: Sometimes the Best Market Prep Is Academic Positioning

Most founders avoid PhDs like career poison. Alon used one as market intelligence. After selling to Salesforce in 2012, he attended a 2015 lecture that changed everything.

“He said, there’s a professor called Michael Jordan. It’s not the same Michael Jordan. And he said in 2015 that there’s a problem that if an AI is able to solve a question, something like, what’s the second biggest city in the US that has a river next to it, like some complex question. If you’re able to solve that, there’s a market with billions of dollars there.”

That observation led him to pursue a PhD in reasoning for question answering between 2016 and 2020. “We were astounded to see this whole revolution unfolding.”

By the time Ask-AI launched in 2020, Alon had four years of front-row access to the generative AI revolution—before most founders knew it was coming.

Lesson 2: Recognize When Your Advantage Is Temporary

Alon offers brutal honesty about AI company moats. “There’s not a lot of moat as well because the AI is such a good generalist that it’s hard to find something that only you can do and no one has access to be able to do that.”

His framework is surgical: “Either go very wide or go very specialized.”

Wide means building a platform comprehensive enough that point solutions become irrelevant. Specialized means finding genuine data scarcity. “Think about a place in which you don’t have an abundance of data. Because to get great AI, you need two things. You need a lot of data and compute power. Compute power, we already have. What you may be missing is data.”

If you’re building where training data is abundant, your advantage evaporates the moment a competitor raises capital.

Lesson 3: Start Where Data Flows Freely

Ask-AI’s entry point reveals sophisticated thinking. They started with customer support not to own that vertical, but because it’s where unstructured conversational data lives.

“We’re actually building an enterprise AI platform starting from customer support that in our vision, eventually would disrupt SaaS deeply. We feel that AI would pretty much make SaaS dead and consolidate many of the SaaS solutions, including the system of record.”

Customer support conversations contain signals about customer health and revenue risk—but that intelligence typically dies in siloed tools. By starting there, Ask-AI becomes the aggregation layer that eventually replaces fragmented SaaS infrastructure.

Lesson 4: Sell the Category Shift, Not the Feature

Ask-AI doesn’t position as “better customer support AI.” They’re selling the death of an entire software category. “The CRM even today only shows you part of the customer and not the whole customer, right? But AI would just start that. Like it would bring in all your company data, bring in all the channels, see kind of a 360, and that will be your real CRM record for the account.”

The vision challenges decades of SaaS design. “One AI assistant to roll them all.” He pushes further: “You think about opening a lot of tabs and doing things in different tabs, but you need to ask yourself, how do I get to this point? Why do you have so many tabs?”

Selling category replacement changes the entire sales conversation. You’re not competing on features—you’re selling a different way of working.

Lesson 5: Use Psychological Clarity to Find Bigger Problems

The post-exit depression Alon experienced became strategic. “It pretty much changes your life. And sometimes it could be, even though that sounds strange but a bit depressing because you put your goal as kind of, I want to sell a company, I want to succeed in high tech. If you feel that pretty much happened, sometimes you feel you have no so what’s your goal right now?”

After a year of travel, he realized what drives founders. “A lot of founders need to be driven by some kind of goal, like seeing some kind of far ahead target and trying to reach it.”

The clarity: he needed something bigger than his last company. That led him to look for “big disruptional changes, like what would be a big kind of technology shift that would happen next.”

Most founders rush into the next company without this clarity. Alon used the discomfort to find a technology shift big enough to matter.

Lesson 6: Understand Discovery Versus Invention

Alon’s perspective on generative AI reveals sophisticated timing. “Generative AI is not an invention, it’s more of a discovery. Like the fact that we can produce these models and they know to do what they do. We didn’t anticipate that.”

They knew something big was coming but not exactly what. “We already realized that something big is going to happen industry, not just in academia. I think a few early generative AI companies, like OpenAI itself, realized that, and we just didn’t realize how huge it’s going to be.”

When riding a discovery rather than an invention, timing becomes about positioning—not predicting. You can’t know when the market explodes, but you can ensure you’re in the right place when it does.

Lesson 7: Build for the Five-Year Future

Ask-AI’s vision extends beyond current pain points. “We would be happier. We won’t be replaced. We’d just be happier because we would do stuff we like. We’d be building relationships. We would be thinking about important stuff, and all the repetitive stuff would just go away.”

They’re not optimizing for the current SaaS landscape—they’re building for the post-SaaS world where AI consolidates dozens of tools into a single intelligent layer.

The risk: you might be too early. The advantage: when the market catches up, you have years of head start.

Alon’s journey reveals that the fastest path to market isn’t always the quickest one. Sometimes it’s the path that looks like a detour.