90 Days to $1.5M: A Company Selling a Problem That Doesn't Exist Yet
Deep thoughts on AI and aspirations —— ByteThink Circle
A company called Gushwork recently raised a $9 million seed round, led by Susquehanna, with Lightspeed and B Capital participating. Let’s start with the numbers: 90 days to reach $1.5 million in annual recurring revenue, 50% to 80% monthly growth rate, over 300 paying customers, generating more than 1,000 sales leads per month for clients. Average customer subscription is $800 to $900 per month, 95% are in the US, mainly the most traditional B2B businesses like industrial distributors, contract manufacturers, and professional services firms.
What they’re selling is simple to describe: getting these companies to appear in AI search answers.
Buyers Have Moved, Sellers Are Still at the Old Address
The starting point for B2B buyers doing supplier research is shifting from Google to ChatGPT, Claude, and Perplexity. And they’re asking very specific questions: technical specs for particular use cases, industry-specific compliance requirements, side-by-side comparisons of suppliers. All these inquiries happen before filling out contact forms or scheduling sales calls. By the time you get that inquiry call, their research is already done.
The problem is on the other end. According to Gushwork, 90% of companies are completely invisible in these AI answers. When a potential customer asks “what’s the best precision bearing industrial distributor in the Midwest,” you’re probably not in that answer, not even in the conversation. This isn’t about ranking higher or lower—it’s about existing or not existing. The data confirms the scale of this migration: OpenAI disclosed in July 2025 that ChatGPT receives approximately 2.5 billion prompts daily.
My assessment is that the most valuable characteristic of this market right now is that buyers and sellers aren’t on the same timeline. Buyers have already migrated, sellers haven’t awakened yet. This kind of misalignment has happened once before in history: in the early days of search engines, the first companies to do optimization captured over a decade of dividends. By the time everyone acknowledged SEO was important, the dividends had already been distributed. The length of the window depends on how quickly the majority acknowledges the problem, and acknowledgment is precisely the slowest step.
One more data point from the shovel-seller themselves: according to Gushwork’s statistics, traffic from AI search accounts for only 20% of their clients’ total website traffic, but contributes 40% of lead conversions. The logic makes sense—users who find you through AI search have already completed deep research, have stronger purchase intent, and shorter decision cycles. But this is the company’s own claim; how much to discount it, we’ll discuss later.
Not Selling a Tool, But Packaging an Agency Into Software
Gushwork’s product is called AI Feeds, which can be broken into two parts.
One part is seven specialized AI agents: Memory maintains company profiles to ensure information consistency, Research scans thousands of AI search queries to identify content gaps, Strategy clusters questions into content blueprints, Content generates landing pages and guides, Publishing handles deployment, Backlinking acquires backlinks from a network of partner sites, and Auto-Update automatically refreshes content as models change. The other part is a content library hosted on the client’s own domain, specifically architected for AI crawler ingestion patterns. Plus a backlink network containing approximately 200 to 300 partner websites, from which each client gets 10 to 20 backlinks.
From this setup, I draw two conclusions.
First: the real asset of companies like this isn’t agent orchestration. Orchestration capabilities will commoditize—model providers could turn similar functionality into APIs at any time. What’s truly hard to replicate is two things: the backlink network requires years of accumulation and can’t be bought via API; the practical feel for “what content gets cited by AI” has to be learned client by client. The test for what constitutes a moat is simple: which part can’t model companies provide? That’s the part that matters.
Second: the very existence of the Auto-Update component acknowledges the instability of the rules. Once enough companies batch-optimize content, AI engines will inevitably tighten their citation and ranking rules. Tactics that work today will likely fail in two years. So when companies buy this type of service, they’re essentially buying a subscription for continuous adaptation, not a one-time optimization project. Anyone who treats it as a project will discover they’ve bought an expired can when the rules change.
Metrics Anchored on “Leads,” Not Traffic
Every page generated by AI Feeds includes tracking and lead capture. Whether prospects arrive from Google or ChatGPT, as long as they take action on the content, leads flow directly into the client’s inbox and dashboard. Which keywords, which content, which channels are driving the sales pipeline—it’s all one unified data framework.
This design deserves attention from everyone doing B2B services. Half the arguments in the marketing industry stem from inconsistent metric frameworks: reporting impressions, rankings, traffic—buyers and sellers always talk past each other, three teams fighting over credit for the same conversion. Align metrics to the layer clients can directly convert to money, and negotiations immediately simplify. This wave of AI service companies generally needs to learn this lesson—whoever learns it first gets the orders.
Three Places to Apply Discount
There are three holes in this company’s story that investors have asked about, and service buyers should ask even more.
| Issue | Specifics |
|---|---|
| All data is company-reported | 20% traffic contributing 40% leads, a client closing $200K to $350K contracts—all from company statements, no third-party verification |
| Education costs underestimated | 90% invisibility corresponds to 90% unawareness. Going from 300 to thousands of clients means explaining “you don’t exist in AI” to people who don’t acknowledge the problem exists. This type of sales cost curve is often steeper than expected |
| Platform risk | All optimization tactics depend on AI engine ranking mechanisms. When the mechanism changes, promised results get completely reshuffled |
The first hole means the direction is credible, the magnitude is questionable. The second hole determines this business’s ceiling: self-aware clients are always just a small fraction; the rest need one-by-one education from sales, and educational selling is both the most expensive and hardest to scale. The third hole is unavoidable—you can only hedge it through continuous adaptation, which is also the real bet behind the entire product format.
An Action You Can Take This Week
Whether or not you buy this type of service, there’s one zero-cost thing worth doing immediately: take the ten most core buyer questions in your business and ask ChatGPT, Claude, and Perplexity—see if you’re in the answers.
If you are, study why you are, solidify the reason into a content strategy, and protect that position. If you’re not, congratulations too—at least you know where your customer acquisition budget should go next. The logic doesn’t need to be complex: wherever your customers make decisions is where your visibility should be built, and right now that location is migrating en masse.
As for entrepreneurs looking for direction, this kind of misalignment—“buyers have migrated, sellers haven’t awakened”—is worth pursuing more than any single-point technology. Because it comes with a countdown timer. The day sellers collectively awaken, first-mover advantage zeros out. The countdown is the true source of first-mover advantage.