Intelligence Without Direction

Why Today’s AI is Brilliant - but Aimless.

We are living through a historic acceleration in artificial intelligence — not just in what machines can do, but in how quickly we are building them. Yet amid this extraordinary capacity, something essential is missing: direction. We are creating tools of astonishing power without a shared vision for what they are meant to serve. And so, in the absence of purpose, the system defaults to momentum. What gets built is what demos well. What gets funded is what scales quickly. What gets rewarded is what wraps borrowed intelligence in polished UI.

And nowhere in this frenzied progress do we see the hand of a nation that once shaped the world through giant, humanity-advancing objectives — from landing on the moon to decoding the genome. The ambition that once inspired entire generations now flickers as a marketing gimmick, as if we’ve traded civilization-building for slide decks and productized cognition.

What We’re Building Reveals What We’re Missing

Far from being an exception, this pattern has become the norm. Social media overflows with breathless headlines—“50 AI tools you need to know today,” or “launch a startup in seven minutes.” The most celebrated offerings on platforms like Product Hunt aren’t foundational technologies, but superficial utilities: mind map generators, tweet composers, pitch deck builders, and prompt-driven startup kits.

This is increasingly how companies are launched—not by solving hard problems or cultivating real insight, but by assembling a stack of trendy tools and riding the momentum. Instead of using AI to push the boundaries of what’s possible, we’re using it to reinforce what’s easy and immediately marketable. The result is an ecosystem shaped less by genuine innovation and more by aesthetics, speed, and the path of least resistance.

We’re not encouraging depth. We’re gamifying shallowness. These tools don’t teach people how to think. They teach them how to skip the part where thinking happens—and then we wonder why the outputs feel hollow. We’ve mistaken speed for insight, volume for strategy, and automation for understanding. A significant portion of what’s being funded (or at least heavily promoted) and built today isn’t innovation. It’s performance masquerading as progress—a kind of distribution theater, where the illusion of innovation is more important than its substance.

The Wrapper Economy

The vast majority of AI startups aren’t training models or building foundational capabilities. They’re stitching together workflows on top of someone else’s API. Resume builders like Kickresume and Rezi plug GPT into a form and generate bullet points—convenient, yes, but entirely replicable by anyone with a prompt template and a weekend. Meeting assistants like Fireflies.ai and Otter rely on existing speech-to-text engines and GPT summaries. Their only differentiation lies in calendar sync and email integrations. If Google or Microsoft bakes in the same features natively, these companies disappear. Email and sales copilots like Lavender.ai and Jasper tweak subject lines and reword outreach sequences through Chrome extensions—an overlay of prompt engineering and UI, easily cloned, easily commoditized. The same applies to legal tech tools that generate contracts or chatbots that simulate therapy. Most lack proprietary data, clinical rigor, or systemic integration. They provide the illusion of intelligence without the architecture to sustain it.

The reason these tools proliferate is clear. Wrappers are cheap to build, easy to demo, and fast to monetize—especially in a venture landscape obsessed with speed and scalability.

Founders chase short feedback loops. Investors chase liquidity. Platforms reward novelty. It’s not a mystery. It’s an incentive design problem.

Why Shallow AI Isn’t Just a Trend — It’s a Warning

We are misallocating capital, talent, and attention during one of the most consequential moments in technological history. Billions are being funneled into shallow tools that give the impression of progress while solving nothing fundamental. Meanwhile, the hard problems—climate resilience, education reform, institutional trust—remain under-resourced simply because they don’t demo well. We mistake what’s easy to build for what’s worth building.

And in the process, we are misusing our best minds. After the tech layoffs of 2022, 2023, and continuing into 2024, a wave of brilliant engineers, designers, and product thinkers entered the wrapper economy—not out of belief in the mission, but out of economic necessity. Rather than building long-term systems or reimagining critical infrastructure, they’re now optimizing pitch decks and tuning interfaces. The danger isn’t a lack of talent. It’s the misdirection of it.

Yet none of this is particularly new to Silicon Valley. The startup ecosystem has long thrived on speculative cycles. Venture capital clusters around an emerging space, inflates the narrative, floods the zone with capital, and exits early—often before the winners are clear or the dust has settled. It’s a well-worn playbook: reward momentum, ignore substance.

The FOMO machine only accelerates this pattern. “Learn AI now before it’s too late.” “Don’t get left behind.” “Use these tools—we’ll make you an expert.” Add the looming anxiety that AI is coming for your job, and the hype engine kicks into overdrive. Urgency, insecurity, and ambition become a cocktail that fuels rapid but shallow adoption.

And frankly, I’m tired of the performative concern. The same handful of deep AI founders appear in endless social media clips or on panels, warning that job loss is inevitable—without taking real ownership for the narrative or the tools. If they know what’s coming, why not build for a different outcome? Why not steer this technology toward augmenting real work, supporting creative intelligence, and surfacing the dormant capacities within people themselves? (See: “The Singularity Might Be Human”)

I’m old enough to remember the dot-com bubble, and the parallels are hard to miss: inflated valuations, cloned business models, and a speculative gold rush of wrappers that promise scale without depth.

And for everyday users, the experience isn’t just noisy—it’s discouraging. Their first brush with AI is often a clunky chatbot, a generic email helper, or a note-taker that requires cleanup. These first impressions matter. If AI feels gimmicky today, the public may dismiss it tomorrow—just as the truly transformative use cases begin to emerge. The danger isn’t that AI underperforms. It’s that it underwhelms at the moment when it’s most needed.

This isn’t a condemnation of AI wrappers. Some of them are useful. Some will evolve. But what should worry us is that in a moment of unprecedented technological potential, we’re reverting to familiar playbooks. We’re treating intelligence like interface. We’re funding speed over significance. And in doing so, we risk getting an AI revolution that feels big — but leaves little behind.

Shaky Foundations

Even from a business standpoint, wrappers are fragile. If your product depends entirely on someone else’s API, you don’t own your core. If OpenAI changes its pricing or access terms, your company can vanish overnight. Dependency is not a moat. And dependency dressed as disruption isn’t a strategy. It’s a gamble—on someone else’s roadmap.

No Mission, No Map

Nowhere is this disconnect more visible than in the United States—a superpower advancing AI technology at breakneck speed while its institutions fracture beneath it. We face collapsing public trust, a cost-of-living crisis, and a talent surplus without a coherent national strategy. We are living through a time when the gap between our technological capability and our social imagination is widening, not narrowing. And into this moment, we are pouring the most powerful technology of our era—not to reinvent education or governance or health, but to rewrite LinkedIn bios and automate pitch decks. That’s not just a missed opportunity. It’s a failure of ambition.

Where is the governmental vision in all of this? Silicon Valley didn’t emerge from nowhere—it was catalyzed by a national mission to reach space. The Apollo program wasn't just about rockets; it mobilized universities, industry, and capital around a shared goal. It elevated the country and gave rise to purpose-driven innovation. We’ve seen it in other moments too: the interstate highway system, the Human Genome Project, even ARPANET. These were not market flukes. They were strategic decisions to pursue long-horizon breakthroughs in the public interest.

By contrast, AI today doesn’t seem to serve a purpose—because it serves every purpose. It’s marketed as universal, but in doing so, it risks becoming directionless. Without intentional guardrails and guiding questions, AI drifts toward whatever is most immediately profitable or most easily demoed. What we’re missing is not capacity—but conviction.

If Not This, Then What?

We should be building tools that help people think better, not just move faster. Tools that illuminate tradeoffs, support foresight, and amplify discernment. Cognitive infrastructure that supports wiser decision-making. Domain-specific systems that go deep in public health, education equity, and climate adaptation. Ethical interfaces that nudge not just for convenience, but for consequence. Platforms designed not for demo days, but for decades.

The Real Singularity Is Direction

The AI revolution won’t fail because it was too early. It will fail because it was too shallow. We’re not short on compute. We’re not short on capital. We’re not short on talent. What we’re short on is intention. If we keep building what is fundable instead of what is foundational, we’ll get a future where AI was real—but misdirected.

We also risk abandoning ethics in the name of speed. In our obsession with winning the race, we’ve stopped asking whether the race itself is the right metaphor. Maybe it’s not about getting there first. Maybe it’s about choosing the right direction—with care, courage, and consequence. The next chapter shouldn’t be about doing things faster. It should be about becoming wiser.

Let’s not just automate. Let’s elevate.