Ethan Ding sees the last decade of SaaS as a historical glitch.
“In 30 years, we’re going to look back and say ‘oh, remember that period of time where those crazy software companies were selling seats for 90 to 95% margins?’” he said. “We’ll look back on that the same way we look back on mercantilism. ‘Remember when the East India Trading Company could move spice?’”
Ding—who went from growing up in a fishing village in China to spending years as a venture capital data junkie—has a unique vantage point on the so-called SaaSpocalypse. Ding is the CEO and cofounder of TextQL, a startup using AI agents to replace the laggard, consultant‑driven process of analytics with a speedy, plain language‑based interface. Data has long been at the center of the rise of enterprise software—and internal company data has always been difficult to wrangle in a way that AI is changing rapidly.
On one hand, AI is about to clean up messy enterprise data, says Ding. On the other hand, that means more people than ever are going to be asking questions about that data.
“Analytics is about to experience probably the most violent Jevons paradox anybody’s seen in a very long time,” said Ding, referencing the 161-year-old theory that more technological efficiency leads to more usage, not less. “Today, the cost of asking a question to seeing a chart that tells you what to do… is two weeks, a person, maybe $5,000–$10,000 of salary spent chasing those numbers down. With language models, with what we’ve built… you’re looking at an increase of five to six orders of magnitude, all‑in.”
Ding cofounded TextQL in 2022 with Mark Hay, and just closed $17 million in strategic investment led by Blackstone’s early-stage investment arm, Blackstone Innovations Investments, Fortune has exclusively learned. There’s a natural question here: Does Ding worry that what he’s doing with analytics can be easily replicated by the large AI labs? We’ve seen versions of this before, especially around coding: A few successful startups prove the market out, and OpenAI and Anthropic then swoop in with their own product (and the full force of their balance sheets).
“I’m vaguely self‑aware that Anthropic wants to have this ecosystem of companies building within all the verticals,” said Ding. “And if a vertical grows fast enough, they want to run after it.”
In the short-term, Ding says he’s not worried, that he believes the challenges will only start when AI analytics produces its own $100 million ARR winner. In the meantime, his plan is to build in low‑liquidity markets like financial services and healthcare. Blackstone CTO John Stecher believes that, while the large labs remain “critical enablers,” they aren’t necessarily equipped to handle the enterprise data problem.
“The challenge enterprises are running into isn’t model capability; it is making AI work reliably, securely, and cost-effectively on real, messy internal data with the right security, governance, and cost controls,” Stecher wrote to Fortune.
Ding brings up a good point, ultimately: That for all the talk around how data and AI are entwined, the foliage of the data jungle has proven much harder to slash through than people were anticipating even 18 months ago. But how we engage with asking questions of our data at work, inevitably, will look very different.
“What does analytics look like in 150,000 years?” he said. “It probably looks like some entity, probably not human, that asks: ‘Of all the Dyson spheres currently orbiting around Alpha Centauri, how much copper alloy was mined from Titan?’ It’s analytics running autonomously.”
See you Monday,
Allie Garfinkle X: @agarfinks Email: [email protected]
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