Are AI Leaders Building Tools for Us or for Themselves?

Every time a leader at one of the major tech companies describes how they use artificial intelligence in daily life, a fresh wave of online criticism follows. The problem isn't the technology itself, but the glaring gap between what these executives consider a "pain point" worth solving and what the average user actually experiences. Today, that gap has crystallized into a fundamental question for a multibillion-dollar industry: are these tools being built to serve everyone, or to serve a narrow slice of executives whose calendars are packed with meetings?

Examples That Sparked Controversy

The debate intensified after a series of statements from prominent figures in the sector. Marissa Mayer, the former CEO of Yahoo who now leads an AI startup, spoke about using an AI tool to write a description of her daughter that the school had requested from her. On another occasion, she floated the idea that her "AI" might coordinate schedules with her husband's "AI."

Meanwhile, Sam Altman, CEO of OpenAI, suggested that parents create a custom podcast for their children to listen to on the way to school, stressing that he can't imagine the experience of parenthood without "ChatGPT." Mark Zuckerberg went even further, predicting that meeting social and emotional needs through AI will become socially acceptable over time. And in the world of dating apps, the idea has been raised of an "intelligent agent" going on a date on its owner's behalf — if the two agents click, the humans are then introduced to each other.

A Marketing Problem or a Product Problem?

The question posed by "TechCrunch" editors in an extended discussion was direct: is the flaw in the way these products are talked about, or in the products themselves? Their conclusion: both, to varying degrees.

On the marketing side, the problem arises when leaders of companies valued in the hundreds of billions gather in one room to discuss the "everyday problems" worth solving with AI. The resulting list is full of examples like booking flights and arranging restaurant reservations. These are things people do, of course, but the average user doesn't book a flight every week, nor do they see reserving a dinner table as a challenge that drains their mental energy.

On the product side, turning human relationships into "automatable" tasks assumes that human interaction is a burden to be eliminated rather than something valuable in its own right. A parent observing their child and writing a description of them isn't an exhausting administrative chore; it is the essence of the relationship itself. When AI is presented as the solution to that, the pitch comes across as detached from reality rather than innovative.

The Difficult Balance Facing Leaders

There's a clear economic reason behind these statements. Companies that have poured enormous sums into AI infrastructure need to convince users that the technology applies to every aspect of life, not just office work. Yet the wider the proposed circle of use becomes, the less appealing the final picture looks — and sometimes the closer it edges toward a dystopian scenario: a world in which conversations are replaced by automated proxies.

The irony is that part of this shift is already happening, quietly. Many people today find it psychologically difficult to call a restaurant to order a meal, because ordering through an app has become the default alternative that requires speaking to no one. The fear is that AI will expand this pattern until basic human interactions are pushed out of daily life — not because they're useless, but because they're "inefficient" by the metrics of efficiency.

What Excessive Optimization Loses

The logic of "efficiency" is persuasive in many contexts, but it fails when applied to things that were never designed to be efficient. Dating, for instance, is inherently inefficient: many dates lead nowhere, and plenty of evenings feel wasted. But delegating the decision to a system that supposedly knows you better than you know yourself carries a double risk:

• An unproven assumption: that the model understands your deepest preferences better than you do.
• Reducing the experience: turning a process of learning and self-discovery into a data-sorting exercise.
• Skill loss: every social skill that gets delegated atrophies over time.

What Does This Mean for the Market?

This debate doesn't stay confined to cultural commentary. As major players in the sector move toward going public, and as financial markets begin to reassess their enthusiasm for massive AI spending, clarity about "who exactly is the target user?" becomes a decisive factor in valuation. Companies that excel at identifying real, tangible problems — in education, healthcare, professional productivity, and digital accessibility for people with disabilities — will build sustainable value. Those selling solutions to problems no one outside the boardroom feels will face a gap between their promises and actual usage.

The practical lesson for founders and companies: build from the real needs of users, not from a reflection of your own day. The true value of AI lies in expanding human capability, not in removing humans from the equation.

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