May 16, 2025

I write about the economics of AI. Follow Author Share Comment Forget big teams and bigger models. The AI startups growing fastest seem to be solving one clear ... More problem — and doing it really well. getty The AI boom has largely been defined by size — large models, huge funding rounds, and teams numbering in the hundreds. But a new trend is emerging — one where lean, focused AI startups are thriving by mastering specific use cases. Take AiHello for instance. Founded by Saif Elhager and Ganesh Krishnan, the 40-person startup has built a profitable AI platform focused solely on Amazon advertising. With no outside funding, they’ve grown to seven-figure annual revenues and continue to double each year. Their approach: build for a well-defined problem and automate everything possible. “We just built a business around the problems we were most familiar with and sold it to people we knew would need it,” said Elhager in an interview. “Instead of trying to look for something that sounded impressive.” This strategy stands in contrast to the scale-first model dominating much of the AI industry today. Rather than building large, generalized tools and searching for product-market fit, Elhager told me that AiHello focused from day one on a single platform, a single use case and a set of customers they understood deeply. And that, according to him, has made all the difference for their company. The Case For Domain-First AI According to McKinsey’s 2024 State of AI report , 65% of businesses now use generative AI in at least one function — double the rate from 2023. Despite such a commendable figure, the most consistent revenue gains are showing up not in flashy creative tools, but in targeted applications like inventory management, operations and marketing optimization — domains where specialized AI solutions thrive. MORE FOR YOU This shift from broad AI ambition to narrow execution that’s hyper-focused on a specific domain mirrors what AiHello is doing in ecommerce. The company’s laser focus on Amazon’s ad ecosystem allows it to improve its models continuously and respond directly to customer needs. Saif Elhager- Cofounder, AiHello AiHello “When you have a more focused number of use cases, you can also spend a lot more time making sure the AI performs well,” Elhager explained. This level of precision isn’t possible in generalized platforms trying to cover dozens of workflows at once. And more industry leaders are now echoing the sentiment that the path to lasting impact isn't scale but specificity. As Sarah Guo noted in a previous edition of the No Priors podcast, which covered AI investment hype , foundation models, regulation and more, “there is real opportunity for vertical specific models where you can imagine that control for either compliance or safety, or just performance makes sense.” The Economics Of Staying Lean While many AI startups spend aggressively on sales, compute and hiring, AiHello went in the opposite direction. The team relies heavily on internal automation, offshores most of its talent and keeps its operating costs low. “Our payroll is 80% lower than usual,” noted Elhager. “We spend very little on sales or marketing, and that’s kept us profitable from day one.” Capital efficiency has become a growing concern in AI, especially as funding conditions tighten. Industry veteran Andrew Ng has also noted this trend, arguing that AI’s real value lies in embedding it into specific workflows — not just building general-purpose tools. “AI won’t replace human workers,” Ng said in a March 2024 talk, “but people that use it will replace people that don’t.” That distinction favors platforms like AiHello, where AI works quietly in the background — cutting costs, saving time and letting the business run smarter. Building On What Works Already Rather than trying to compete with Amazon or build a new ecommerce stack from scratch, AiHello built its tools directly within the existing system. “Building on an existing platform and going to market with an obvious ICP is much quicker and less capital-intensive,” said Elhager. “If your goal is to build a 7–8 figure business, then this is one of the higher probability ways of doing that.” It’s a reminder that not every breakthrough requires reinvention. Sometimes, the smartest move is to enhance what already works. The Next Wave AiHello isn’t the only one taking this path. Other startups like Rebuy — which helps Shopify merchants personalize shopping experiences using AI — Typeface which generates on-brand content for marketing teams — and Adept — which builds AI agents that can take actions across enterprise software tools — are succeeding by solving specific problems inside defined ecosystems. “Having limited headcount means we have to focus on only 1–2 things that matter,” said Elhager. “That’s paradoxically a faster way to make progress.” In a market already flooded with general-purpose AI pitches and bloated burn rates, the future may belong to companies that stay small, move fast and go deep rather than wide.