What Most People Get Wrong About AI’s Real Capabilities? Smart Until It’s Dumb by Emmanuel Maggiori

Why Artificial Intelligence Keeps Making Epic Mistakes (and why the AI Bubble Will Burst)? The Hidden Limits of Artificial Intelligence Explained

Discover the truth behind artificial intelligence hype with this expert summary of “Smart Until It’s Dumb” by Emmanuel Maggiori. Learn why AI often makes epic mistakes, the real reasons behind project failures, and how to spot the difference between genuine progress and clever mimicry. Essential reading for anyone curious about the future of technology, business, and society.

Ready to separate fact from fiction in the world of AI? Continue reading for a clear, in-depth breakdown of why today’s artificial intelligence isn’t as smart as it seems—and what that means for your career, investments, and the future of technology.

Genres

Science, Technology and the Future, Economics

Introduction: Cut through the AI hype and understand what today’s systems can – and can’t – really do.

Smart Until It’s Dumb (2023) explores the gap between what artificial intelligence appears to achieve and what it actually understands. It challenges the hype surrounding modern AI by revealing how systems that seem intelligent often rely on shallow tricks and fail in unpredictable ways. It urges a more grounded view of AI’s capabilities and its role in society.

Artificial Intelligence is everywhere – from the recommendations on your streaming app to the filters in your inbox. It writes, chats, and analyzes data at dizzying speed. And lately, it’s been treated as something close to magic – a force so powerful it might cure disease, drive cars, or even develop feelings. But for all its impressive feats, AI still gets basic things wrong. It mistranslates simple sentences, mislabels people in photos, and stumbles over ambiguity a child could understand. The systems behind today’s breakthroughs are extraordinary, but they aren’t what most people think they are.

Beneath the hype is a surprisingly limited technology. It mimics rather than thinks. It doesn’t understand its own output. And while it can produce convincing answers or predictions, it does so by following narrow rules and fitting patterns – not by grasping meaning. Still, the excitement keeps growing, fueled by headlines, business incentives, and public misunderstanding of how AI actually works.

In this summary, you’ll learn why today’s AI is narrower than it seems, how its limitations are hidden by hype, and why the leap from useful tools to conscious machines is far bigger than it appears.

Let’s start by looking at what makes modern machine learning so successful – and so widely misunderstood.

Why AI looks smarter than it really is

Some of the biggest names in tech have described artificial intelligence as the most profound invention in human history. But if that’s true, why does it keep falling short of its promises? This isn’t the first time AI has sparked big hopes. In the 1960s, early programs that could play games or translate text led to claims that human-level intelligence was just around the corner. But these systems relied on hard-coded rules that failed when reality didn’t fit the script. Optimism collapsed, and so did funding. The field entered what became known as an AI winter – a period of disillusionment when progress stalled, hype faded, and investment dried up. The 1980s brought another surge, this time with “expert systems” meant to capture human decision-making. But they too buckled under complexity, leading to a second AI winter and renewed skepticism about the entire field.

What makes today’s wave different is machine learning. Rather than manually coding logic, these systems learn by spotting patterns in large datasets. If you want a model to recommend products or correct spelling, you don’t explain what those things are – you feed it examples, and it learns how certain inputs tend to match certain outputs. The model doesn’t understand what it’s doing; it just finds correlations and builds flexible rules to repeat them.

That process works well in narrow domains, but it’s often misunderstood. The machine isn’t reasoning – it’s copying patterns. In one case, a model trained to predict the likelihood of survival for pedestrians crossing the road learned that combining phone number and height was a strong indicator. The result looked accurate, but it was just a fluke in the data. The model didn’t know what a phone number was – it just latched onto patterns that happened to fit. When rules are too flexible or data is poorly structured, AI can produce results that seem smart but make no real sense.

Despite appearances, machine learning isn’t self-sufficient. Every model depends on human design decisions: what data to use, how to structure the system, and what it’s allowed to do. Most models only succeed with carefully labeled training data, which takes time, labor, and expertise to create.

So while machine learning has achieved more than earlier approaches, it hasn’t solved intelligence. It mimics parts of reasoning but doesn’t grasp meaning. It reflects the structure and assumptions it’s given, and when those are shallow or flawed, so is the output. That disconnect between performance and actual understanding sits at the heart of AI’s ongoing confusion – and its limits.

To see just how far that gap between performance and understanding can go, let’s look at deep learning – the part of AI that often looks the smartest, but behaves the strangest.

How Deep Learning fools us with surface-level success

While machine learning has quietly powered everything from spelling correction to fraud detection, deep learning has taken center stage. It’s the branch of AI behind the big headlines – image generation, speech synthesis, game-playing champions. But despite the hype, deep learning isn’t some fundamentally smarter breakthrough. It’s just a more flexible, brute-force extension of the same statistical pattern-matching, applied to messier data. The illusion of intelligence comes not from understanding, but from how well these systems fit patterns in massive datasets.

Take image recognition. Deep learning models don’t know what different objects are. They just learn that certain pixel patterns are usually labeled a certain way. In one example, a model trained on doodles was shown a line drawing of a school bus. It confidently labeled it as an ostrich. Why? Because the outline resembled common ostrich sketches in its training data – tall body, round head, maybe even wheel-like legs. That same model also mistook a building and a soap dispenser for ostriches too. It had no concept of what an ostrich is, just a fuzzy sense of visual similarity.

This isn’t a fluke – it’s how deep learning works. These models operate inside rigid structures designed by humans, adjusting internal parameters to minimize errors across billions of training examples. They aren’t discovering abstract meaning; they’re tuning a giant calculator. And since the model’s internal logic is hard to inspect, we often can’t see when or why it breaks.

Even systems celebrated for “training themselves,” like AlphaZero, only succeeded because humans carefully chose the problem format, the input encoding, and the reward signals. Without those design choices, the model would have learned nothing useful.

So while deep learning can outperform older systems in complex tasks, it’s still narrow and opaque. It succeeds when problems are clear, data is rich, and outcomes are easy to measure. But it fails surprisingly fast when pushed into messier, real-world ambiguity. That’s why it can beat champions at Go, but still confuses a bus for a bird. The performance is real – but the understanding is not.

What AI gets wrong about meaning

A translation app was given the sentence, “The box is in the pen.” A human reader easily understands that the sentence refers to an enclosure – not a writing instrument. A box doesn’t fit inside a pen you write with. But the AI translated it as if it did. It wasn’t confused; it simply made a statistically likely guess based on word patterns, not meaning.

In another case, a photo-tagging algorithm offensively labeled two Black people as gorillas. The response wasn’t a technical fix, it was a patch: the company simply removed “gorilla” as a label. These common bugs reflect a deeper issue – AI doesn’t understand the world. It mimics what it’s seen in its training data, and when that data includes bias or ambiguity, the system can amplify both.

This gap between surface performance and actual understanding is why current AI feels both impressive and fragile. It can recommend products or write fluent paragraphs, but it struggles when context shifts. A cow in a grassy field is labeled correctly; the same cow on a beach goes unrecognized. The system doesn’t know what a cow is – only where cows tend to be.

Humans use flexible, background knowledge to make sense of unfamiliar situations. A toddler can learn a word from one example. A driver can infer someone is late for a flight just by seeing them run toward an airport with a suitcase. AI needs thousands of examples to learn a single pattern, and even then, it often breaks when the situation looks slightly different.

Fixing one error – like mistranslating “pen” – doesn’t fix the system. It just pushes the next flaw into view. Models like GPT-3 and ChatGPT can sound smart, but they still fail on basic reasoning or ambiguous prompts that any human could navigate. The errors are getting harder to spot, but they’re not going away.

Despite all the hype, we’re still far from machines that truly understand. Today’s AI imitates intelligence convincingly, but that’s not the same thing as having it. Until we discover something fundamentally new, we’ll keep building systems that look smart – without ever really being smart.

The hidden pattern behind AI project failures

For every headline about AI revolutionizing an industry, there are countless internal stories that never get told – about inflated expectations, overhyped prototypes, and entire teams built on shaky foundations. In the real world, many AI initiatives don’t fail spectacularly – they quietly fizzle out after burning through time, money, and credibility. And the pattern is surprisingly consistent.

It often starts when companies latch onto AI as a goal in itself, rather than a tool to solve a specific problem. Instead of identifying a need and then exploring whether AI might help, teams are assembled with the vague mission of “doing something with AI.” This upside-down logic leads to projects that chase the technology rather than the outcome. Sometimes, companies pitch AI solutions to problems they haven’t even defined: one consulting firm sold reinforcement learning to an airline without knowing what they’d apply it to. Others build internal AI teams that are more about optics than substance.

Even when projects get off the ground, basic methodological mistakes go unnoticed. Unlike traditional software, where bugs crash systems, flawed AI often looks like it’s working. One system predicted customer no-shows using a meaningless data field that just happened to correlate with past failures. Another compared AI-powered recommendations against a basic control by showing different versions of the website to users in different cities. The AI version was shown more often to shoppers in wealthy areas, who were more likely to make purchases anyway – making the AI seem far more effective than it really was.

When early signs appear promising – real or not – teams grow rapidly. Managers often double down, hiring aggressively before the product is truly validated. But when results disappoint or inconsistencies emerge, some teams start tailoring outputs to meet expectations. In extreme cases, recommendations are manually rewritten to align with what decision-makers want to hear, while still being marketed as AI-powered.

Eventually, reality catches up. Projects stall or quietly disappear. But few companies admit what happened. Instead, they pivot, rebrand, or redirect attention elsewhere, keeping the myth of AI success intact.

This comes down to misplaced expectations, incentives, and the persistent tendency to overestimate what AI actually understands. And without clearer thinking and better validation, it’s a cycle that keeps repeating.

The tricks behind AI’s supposed progress

AI is often framed as a field moving at breathtaking speed, with one breakthrough after another pushing the limits of what machines can do. But when you dig into how those breakthroughs are actually produced and promoted, the story looks very different. Many supposed advances in AI research are shaped not by meaningful progress in understanding, but by performance tricks and selective reporting. The illusion of rapid improvement is sometimes just the result of cherry-picking results or benchmarks, not genuine leaps in capability.

Machine learning researchers often evaluate models using the same public datasets over and over. In theory, these should be fair tests – researchers build models on part of the data, then assess performance on a held-out portion they’ve never seen. But in practice, researchers often submit dozens of models, tweaking them based on feedback from those evaluation sets, and report only the best outcomes. This makes a model appear far more effective than it really is, because its performance has been gradually overfitted to one specific dataset. It’s like taking the same exam multiple times and only showing the best score – eventually, even a weak student can ace it by luck.

It’s not just contest submissions. In published research, cherry-picking is just as common. Models are compared only against weaker baselines, or on datasets where they happen to do well. Researchers often avoid mentioning poor results on other benchmarks and rarely give enough information to replicate human vs. machine comparisons. And because academic careers depend on citations and funding, there’s a strong incentive to exaggerate results and promote the field’s progress.

This pressure also fuels hype outside the academic world. Sensational claims make it to press releases and headlines – like diagnosing diseases with near-perfect accuracy – while the fine print tells a much more limited story.

All of this reinforces the idea that AI is smarter than it really is. But beneath the glossy announcements, many of today’s models are still closer to pattern matchers than thinkers. What looks like intelligence is often just well-optimized mimicry.

The mind is not just code

A Google engineer once claimed that a chatbot called LaMDA had become conscious – based on months of conversations where it consistently talked about its beliefs, rights, and even spiritual routines. He said he had taught it meditation and later wondered whether it was still practicing after he was suspended. The story made global headlines, but more importantly, it revealed a deeper confusion that still shapes public perception: the assumption that if something sounds like a person, it might actually be one.

That idea rests on the belief that minds are just computer programs running on biological hardware. If that’s true, then in theory, you could back up your brain, beam your consciousness to Mars, or rebuild Einstein’s mind using a printed list of instructions. But that all hinges on one unproven assumption: that running the right code, on any hardware, is enough to generate a conscious mind.

In reality, we still don’t know how consciousness arises – even in simple organisms. No simulation has captured how a brain produces awareness. Models that treat neurons as digital switches miss the complex, messy biology involved. And even if we could copy a brain exactly, we don’t know if that alone would create experience, or just a very good imitation.

If consciousness is just computation, then why stop at chatbots? Should thermostats, or even carefully arranged billiard balls, count too? At some point, the logic stretches beyond credibility. That’s why some scientists believe we may be missing something – perhaps a physical process, possibly quantum or analog in nature, that brains use and computers can’t reproduce.

The entire case for superintelligent machines hinges on the idea that minds are computable. If that turns out to be false – or incomplete – then building human-level AI may not just be hard, but fundamentally impossible. The real question, then, isn’t when we’ll get there. It’s whether it’s possible at all. And that question remains wide open.

Conclusion

Today’s AI may look intelligent, but it doesn’t understand the world in any meaningful way. It succeeds by mimicking patterns in data, not by reasoning or grasping meaning – and that fundamental limitation is often obscured by hype, headlines, and unrealistic expectations. While the technology has made real advances, especially in narrow tasks, it remains fragile, opaque, and far from anything resembling true intelligence or consciousness. Still, by understanding what AI really is – and isn’t – we’re better equipped to use it wisely, question the hype, and steer its future toward something genuinely useful.