AI That Doesn’t Drive Revenue Deserves to Be Deleted

3 min read.

Author

Midas AI
August 07, 2025

AI is swiftly becoming table stakes in modern product strategy, but whether it drives real business value is another matter. In a candid post by Superhuman CEO Rahul Vohra, the email startup unpacks a critical insight, chasing artificial intelligence purely for prestige or novelty misses the point. The real value of AI lies in its ability to accelerate revenue, shorten cycles, and compound growth. If it’s not doing that, it might not be worth doing.

In a landscape where every digital product claims to be “AI powered,” Superhuman offers a sober and useful benchmark for builders, your AI should earn its keep.

Why Most AI Projects Miss the Mark

Over the past year, there has been an explosion of AI features across SaaS products, copilots, writing assistants, recommendation engines, but precious few of these additions tie directly to the bottom line. As Vohra explains in his article, many teams integrate AI defensively, to avoid being seen as behind the curve. They build for optics or for investor decks, not necessarily for ROI.

But building AI for aesthetics serves neither the user nor the business. Features become bloat, workloads increase, and the marginal gains rarely justify the engineering spend. Superhuman argues that a better starting question is, “Will this AI feature increase our revenue or reduce our costs?” If it doesn’t clearly answer yes, it may be worth skipping.

Designing AI for Revenue Acceleration

At Superhuman, the AI strategy is explicitly tied to the company’s growth engine. Features are evaluated less on novelty, more on how they accelerate revenue generation in measurable ways. One metric stands out, how quickly a user becomes a paying customer. AI that shortens the trial to subscription window without degrading satisfaction creates clear financial upside.

For instance, Superhuman launched an AI powered email summarizer. Unlike generic summarization tools, its function was specific: help new users move faster through cluttered inboxes, decrease overwhelm, and make them successful sooner. Faster success meant higher conversion rates during trials, and that translated to real dollars.

In this way, AI becomes an accelerant for customer acquisition and retention, not just a layer of automation on top of existing workflows. The feature works not because it’s “smart,” but because it contributes directly to outcomes the business cares about.

How to Build a Money First AI Strategy

Superhuman’s approach offers a few instructive lessons for founders and product teams thinking about AI.

First, start with a proven business engine. AI multiplies impact when plugged into elements already driving growth. Slower onboarding, leaky funnels, or inconsistent support responses are real issues that smart automations can improve, if you define success in operational terms, not just technical ones.

Second, measure AI success like you would any growth experiment. Look for uplift in cohort retention, funnel acceleration, or cost per conversion. If you can’t connect the dots from AI to cash flow or customer value, you won’t know whether to iterate, escalate, or scrap.

Finally, avoid the trap of “proprietary model” thinking. Vohra emphasizes that the real moat is not which LLM you use or whether your model is fine tuned. What defensibly compounds over time is the feedback loop between user outcomes and business metrics. Whoever learns fastest wins, because better AI usage, not better models alone, moves the financial needle.

The Bottom Line

The hype cycle around generative AI has lured many companies into shipping half baked features just to check the AI box. But the market is now moving toward more pragmatic expectations. Investors want to see margins improve and products differentiate in ways users will actually pay for.

Superhuman’s hard nosed view is a reminder that great AI doesn’t just feel futuristic, it feels valuable. It makes users stickier, sales cycles shorter, and operations leaner. If your AI does not strictly serve those kinds of levers, it may be a distraction.

Done right, AI should behave less like a product flourish and more like a multiplier on your existing business engine. Anything less than that doesn’t deserve the budget.