The Wrong Diagnosis
If you’re on LinkedIn and anywhere near the tech world, chances are your feed is flooded with thought pieces declaring that AI is collapsing. The headlines are dire, the tone often gleeful. I’ll admit — they bring a wry smile to my face. This is a familiar pattern: every time human ingenuity reshapes how we relate to technology or labor, a wave of skepticism follows. But this isn’t a takedown of those fears. On the contrary, I understand where they come from. What I hope to offer here is clarity — a way to see the changes in AI not as signs of failure, but as signals of a deeper shift.
These collapse narratives often stem from a fundamental misunderstanding of generative AI — especially how it learns, and what it’s actually for. Generative models aren’t static tools; they’re cultural artifacts, built on language, shaped by human knowledge, and constantly evolving through interaction. What looks like stagnation or failure to some is, in fact, a maturation process. The age of viral novelty is ending. What lies ahead is slower, more intentional — and ultimately more powerful.
Generative AI is not collapsing. It’s outgrowing its hype. The real opportunity no longer lies in chasing ever-larger models or quick-fix benchmarks, but in developing the systems — human systems — that can refine, guide, and scale this technology in sustainable, usable ways. Maturity means complexity, and complexity requires care.
Maturity Is Not Meltdown
At first glance, Generative AI appears to be evolving at breakneck speed — a dazzling, disruptive force crashing into every industry at once. But the reality is more grounded: today’s GenAI systems are the result of decades of research in neural networks and machine learning. What feels new is merely the sudden visibility of that long, quiet labor.
The hype surrounding Large Language Models (LLMs) resembles adolescence: gangly, unbalanced, bursting with potential, and prone to inappropriate outbursts. Remember trying to navigate all those big, overwhelming feelings? The hallucinations of LLMs echo the kinds of exaggerations we once told to seem impressive — “I have a Canadian model boyfriend, and he’s so into me!” The world was full of promise and possibility. But at some point, we had to grow up. We had to take all that potential and turn it into something sustainable, something real.
We gave up unstructured living for more thoughtful, methodical ways of being. We refined our worldviews. We became efficient without losing depth. And because AI is a cultural, human product, its evolution mirrors ours. That’s where we are now: in the long arc of maturation.
This next phase isn’t collapse — it’s refinement. The age of scrappy startup models duct-taped together with raw compute and optimism is giving way to systems thinking, infrastructure design, and operational discipline. It’s less flashy, more rigorous. But it’s also more durable, more scalable, and ultimately more meaningful.
The Cost of Listening: High-Quality Annotation & Feedback Loops
If we want Generative AI to do more than predict — if we want it to truly listen, interpret, and interact with us — we need to invest in the infrastructure that enables it to do so. That means moving beyond the notion that “bigger is better” and embracing layered complexity.
To retain nuance, avoid flattening meaning, and safely integrate GenAI into real-world systems, we must treat it not as a glorified autocomplete engine but as a participant in human systems. Good AI doesn’t guess — it interprets. And interpretation demands context, deliberation, and a feedback loop that evolves over time.
Just as humans rely on vast stores of tacit knowledge to understand each other’s intent, our models require carefully constructed, layered data to do the same. This includes:
- Annotations that preserve ambiguity and encode subtle distinctions;
- Feedback loops that translate tension and multiplicity into usable signal;
- Cross-cultural context that prevents bias and misalignment;
- Task-specific fine-tuning and evaluation mechanisms that can be trusted in high-stakes domains.
This isn’t just about compliance or optimization — it’s about building trust. Systems that affect human opportunity and safety must be able to engage meaningfully with difference, not just pattern frequency.
To get there, we need domain-specific ontologies developed through iterative collaboration, not one-off labeling. In writing, there’s a saying: ideas are easy, implementation is hard. So it is with AI. Talking is cheap. Listening — carefully, responsibly, and systemically — is expensive. But it’s the only way forward.
AI Intelligence Is a Cultural Product
Generative AI models aren’t just trained on data — they’re trained on worldviews, cosmovisiones. Think of GenAI as the cosmos, interpreted through our axioms. What it reflects back to us is not some neutral truth, but a composite of the priorities, biases, and ontologies we’ve encoded into it.
My own cosmovisión — Jewish, shaped at the margins and educated at the center — filters how I engage with the world and how “my” instance of an LLM responds to me. It’s fluent in Shem Tov’s ethical philosophy, in the poetics of numbers, in the anxious vocabulary of a millennial watching the world burn. That’s not a flaw. That’s the point.
AI is not neutral. It never was. These systems are byproducts of centuries of human knowledge transmission: the stories we preserve, the categories we inscribe, the assumptions we repeat without realizing. Every annotation choice, every labeling schema, every training corpus reflects a worldview — often unconsciously.
And yet, today’s dominant model of “bigger is better” reflects a very specific worldview: American, Western, extractive, scale-driven. But scale alone cannot deliver equity, nuance, or trust.
If we want Generative AI to be more than a mirror of dominant systems, we need to rethink the foundation. That starts with thoughtful infrastructure, built by intelligent curators and interpreters who understand that training a model is never just a technical process — it is always a cultural one.
Monetization Is a Human Problem, Not a Parameter Problem
Despite what the skeptics say, models are not getting dumber. That assumption betrays a fundamental misunderstanding of probability, language structure, and the nature of generative output. I could go on — and I’d be delighted to. Invite me to give a talk at your company.
But that’s not the point of this article.
What we’re seeing isn’t cognitive decline — it’s unmet expectation. Models are failing to live up to inflated business fantasies, not technical limitations. Investors treated GenAI like a gold rush. Now the dealer’s calling in the bets, and there’s no jackpot waiting for those who bet on hype without infrastructure.
You can’t take the human out of a cultural product and expect it to perform. You can’t scale a model and hope for meaningful, long-term ROI without iterative refinement, use-case alignment, and cross-functional integration. It doesn’t work that way.
Mature AI — especially in enterprise contexts — demands:
- Tooling tailored to specific verticals like law, healthcare, finance, education, and transportation;
- Early investment in governance infrastructure that fosters trust, ensures safety, and respects human dignity;
- Systems thinking, guided by people trained in both systems architecture and human complexity;
- Orchestration across teams, not just orchestration layers in a tech stack.
Tech stacks alone will not get you to profitability. Models don’t monetize themselves. People do.
Listening, Not Just Predicting
The core challenge with today’s Generative AI lies in its very architecture: it predicts the next likely output based on probability. But probability isn’t the same as understanding. And in high-stakes contexts — like healthcare — that difference matters.
Imagine you visit urgent care with a blinding headache, blurred vision, and nausea. A nurse consults an LLM-enhanced triage system, which ingests your vitals, family history, fragmented medical records, and demographic data. It determines you’re likely experiencing a migraine and sends you home with extra-strength Excedrin. Days later, you’re rushed to the ER. It wasn’t a migraine — it was the early onset of an aneurysm.
What went wrong? Genetics and probability don’t map neatly onto each other. Gene expression is shaped by the environment, history, and random factors. It’s more like a lottery than a logic puzzle. The model didn’t fail because it was “dumb”; it failed because it wasn’t trained to listen. It didn’t ask, “Do you have a history of circulatory issues?” or “Have you had migraines before?” It lacked the ability to seek disconfirming evidence or explore ambiguity.
To build GenAI that doesn’t just predict but listens, asks follow-up questions, notices outliers, and pauses before assuming — we need monumental investment. That means deeper annotation, stronger model iteration, richer ontologies, and purpose-built evaluation systems. Especially in contexts like healthcare, education, or justice, a one-size-fits-all approach is not just lazy — it’s dangerous.
Yes, this introduces ambiguity. Yes, it requires complexity. But those are not bugs. They are the hallmarks of the human condition.
It’s time we stop asking Generative AI to finish our sentences. We must ask it to converse, adapt, reflect. And that means we must build human-in-the-loop safeguards into every stage of deployment — especially in domains where decisions carry real consequences. These systems must support, not replace, human judgment.
This shift will take time. It will require cleaning up the technical debt we’ve accumulated in our rush for scale. But if we want AI to truly listen — and not just predict — we need to meet it halfway.
In other words, it takes us.
The Real Collapse
What’s collapsing isn’t AI — it’s the myth of effortless monetization. The illusion that a powerful tool alone could revolutionize the world with minimal investment, minimal oversight, and maximal return is crumbling under the weight of reality.
Our generation — millennials — grew up immersed in the fantasy of the push-button solution. We were sold the dream that progress could be packaged into apps and delivered overnight. But as we’ve matured, we’ve come to understand the layered complexity beneath that manufactured simplicity. Now, our technology must grow up with us.
Generative AI is no exception. If we want it to be an agent of progress — of genuine interaction, transformation, and equity — we need to build systems capable of holding the complexity of human experience. That requires:
- Robust governance frameworks
- Intentional, nuanced annotation
- Cross-disciplinary, culturally fluent thinking
- And above all, human-in-the-loop design that centers judgment, context, and care
This is the real work. And it’s the only path forward.
Conclusion: We Are the Systems We Build
Let’s not panic. Let’s not catastrophize. Generative AI is not alien. It’s human — a reflection of us. And like us, it will mature. It will evolve. It will shift and surprise us. It will disappoint us. It will need to pause, to realign, to reassess — just as we do.
Sustainable AI — fair, ethical, and monetizable — will not emerge from scale alone. It will arise from a careful symphony of systems: governed, annotated, culturally aware, capable of assessing themselves, iterating, and evolving over time.
The real collapse isn’t technical. It’s the fantasy of effort-free innovation, the myth that intelligence rains down like gold from the sky.
But we’re here on earth. And here, we push up our sleeves — and get to work.