Ask-AI’s Binary Framework: The Only Two GTM Strategies That Work for AI Companies

Ask-AI’s Binary Framework: The Only Two GTM Strategies That Work for AI Companies

Most AI founders spend months perfecting their moat strategy. Alon Talmor will save you the time: there are only two paths, and the middle ground is where AI startups die. Go impossibly wide or find genuine data scarcity. Everything else is temporary advantage masquerading as strategy.

In a recent episode of Category Visionaries, Alon Talmor, CEO and Founder of Ask-AI, explained why AI’s generalist capabilities have destroyed traditional moat thinking—and what founders should build instead.

The Uncomfortable Truth About AI Moats

Alon doesn’t sugarcoat the challenge. “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.”

This is the defining strategic challenge of building AI companies. The technology that makes your product powerful also makes it easily replicable. Foundation models are accessible to everyone. Your clever prompt engineering? Reproducible in days. Your fine-tuned model? Someone with capital can build it in weeks.

Traditional SaaS moats—proprietary algorithms, unique features, workflow optimization—mean less when AI generalizes across use cases.

The Binary Choice

Alon’s framework is surgical. “Either go very wide or go very specialized. What do I mean by that? Because it’s not going to be a lot of moat in the future.”

Path one: go wide enough that you become a platform. This is Ask-AI’s strategy. They’re not building better customer support—they’re building the AI layer that replaces multiple enterprise tools. “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.”

The wide strategy shifts the competition. You’re not trying to be better at one thing—you’re doing enough things well that users stop needing separate tools.

Path two: go specialized enough that you control unique data.

The Data Scarcity Play

Alon’s insight about where moats exist in AI comes down to a simple equation. “To get great AI, you need two things. You need a lot of data and compute power. Compute power, we already have. Thankfully. What you may be missing is data.”

The strategic question becomes: where is data scarce? “Think about a place in which you don’t have an abundance of data.”

He uses robotics as the example that clarifies everything. “You don’t really see robots running around us collecting data, right? All the generative AI, the reason it became so great is because two things happened. Compute power became much better with GPUs. And the data we created became so huge. Like we all created data on TikTok on Instagram. These are all data points that are used to train the AI. The whole Internet is used to train the AI, but that’s not all the sensor data you can have in the world.”

This is the specialized path: find verticals where data collection requires unique access. Physical robotics, industrial sensors, proprietary medical equipment, specialized manufacturing processes—domains where you can’t just scrape the internet and train a model.

“And if you get that data, then you’d have a real moat, and that would be a great company, I think.”

Why the Middle Ground Kills Companies

The founders who die are building specialized AI in data-abundant domains. Sales email AI? The internet has billions of sales emails. Customer support for e-commerce? Millions of conversations already exist for training.

These companies might get traction. They might raise Series A. But their advantage evaporates when a well-funded competitor targets their vertical. There’s no data moat because the data exists everywhere. There’s no feature moat because AI capabilities generalize.

The middle ground—specialized tools in data-abundant domains—is temporary advantage at best.

How to Choose Your Path

Start with the data question: Is training data abundant or scarce in your domain?

If abundant, you can’t win on specialization. Go wide enough to become the platform. This requires capital and vision to build across multiple use cases before specialized competitors establish themselves.

If scarce, specialization becomes viable—but you need unique access to that data. A robotics AI company works if you have robots collecting data. A medical AI company works if you have hospital partnerships.

The framework also reveals when to pivot. If you started specialized in a data-abundant domain, you have two options: go wide fast, or pivot to a domain with genuine data scarcity.

What Ask-AI’s Choice Reveals

Ask-AI chose the wide path deliberately. Customer support is their entry point, not their destination. “The CRM even today only shows you part of the customer and not the whole customer, right? Not everything you want to know. By the way, companies like Gainsight, their whole thing to fame is to do that, to bring everything together so that you see your whole customer,” Alon explains. “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.”

They’re using customer support as the wedge to become the enterprise intelligence layer. The strategy isn’t to own support tickets—it’s to aggregate enough enterprise data across enough functions that they become the system of record.

The Brutal Honesty Founders Need

What makes Alon’s framework valuable isn’t just the binary choice—it’s the honesty about moat scarcity. Most AI founders are selling investors and themselves on defensibility that doesn’t exist. They’re building in the middle ground, hoping network effects or brand or first-mover advantage will protect them.

Alon’s message is clear: those advantages matter less in AI than in traditional SaaS. AI generalizes too well. Foundation models improve too fast. The middle ground collapses too quickly.

Choose wide or choose data scarcity. Everything else is hoping the rules don’t apply to you.