Virtually everything that everyone has been saying about AI has been misleading or wrong. This is not surprising. The processes of artificial intelligence and its digital workhorse, machine learning, can be mysteriously opaque even to its most experienced practitioners, let alone its most ignorant critics.

But when the public debate about any new technology starts to get out of control and move in dangerous directions, it’s time to clue the public and politicians in on what’s really happening and what’s really at stake. In this case, it’s essential to understand what a genuine national AI strategy should look like and why it’s crucial for the U.S. to have one.

The current flawed paradigm reads like this: How can the government mitigate the risks and disruptive changes flowing from AI’s commercial and private sector? The leading advocate for this position is Sam Altman, CEO of OpenSource AI, the company that set off the current furor with its ChatGPT application. When Altman appeared before the Senate on May 13, he warned: “I think if this technology goes wrong, it can go quite wrong.” He also offered a solution: “We want to work with the government to prevent that from happening.”

In the same way that Altman volunteering for regulation allows him to use his influence over the process to set rules that he believes will favor his company, government is all too ready to cooperate. Government also sees an advantage in hyping the fear of AI and fitting it into the regulatory model as a way to maintain control over the industry. But given how few members of Congress understand the technology, their willingness to oversee a field that commercial companies founded and have led for more than two decades should be treated with caution.

Instead, we need a new paradigm for understanding and advancing AI—one that will enable us to channel the coming changes to national ends. In particular, our AI policy needs to restore American technological, economic, and global leadership—especially vis a vis China—before it’s too late.

It’s a paradigm that uses public power to unleash the private sector, and transform the national landscape, to win the AI future.

A reasonable discussion of AI has to start by disposing of two misconceptions.

First is the threat of artificial intelligence applications becoming so powerful and pervasive at a late stage of their development they decide to replace humanity—a scenario known as Artificial General Intelligence (AGI). This is the Rise of the Machines fantasy left over from The Terminator movies of the 1980’s when artificial intelligence research was still in its infancy.

The other is that the advent of AI will mean a massive loss of jobs and the end of work itself, as human labor—and even human purpose—is replaced by an algorithm-driven workforce. Fear mongers like to point to the recent Goldman Sachs study that suggested AI could replace more than 300 million jobs in the United States and Europe—while also adding 7% to the total value of goods and services around the world.

Most of these concerns stem from the public’s misunderstanding what AI and its internal engine, Machine Learning (ML), can and cannot do.

ML describes a computer’s ability to recognize patterns in large sets of data—whether that data are sounds, images, words, or financial transactions. Scientists call the mathematical representation of these data sets a tensor. As long as data can be converted into a tensor, it’s ready for ML and its more sophisticated offspring, Deep Learning, which builds algorithms mimicking the brain’s neural network in order to create self-correcting predictive models through repeated testing of datasets to correct and validate the initial model.

The result is a prediction curve based on past patterns (e.g., given the correlation between A and B in the past, we can expect AB to appear again in the future). The more data, the more accurate the predictive model becomes. Patterns that were unrecognizable in tens of thousands of examples can suddenly be obvious in the millionth or ten millionth example. They then become the model for writing a ChatGPT essay that can imitate the distinct speech patterns of Winston Churchill, or for predicting fluctuations in financial markets, or for defeating an adversary on the battlefield.

AI/ML is all about using pattern recognition to generate prediction models, which constantly sharpen their accuracy through the data feedback loop. It’s a profoundly powerful technology but it’s still very far from thinking, or anything approaching human notions of consciousness.

As AI scientist Erik Larson explained in his 2021 book The Myth of Artificial Intelligence, “Machine learning can never supply real understanding because the analysis of data does not bridge to knowledge of the causal structure of the world [which is] essential for intelligence.” What machine learning does—associating data points with each other—“doesn’t ‘scale’ to causal thinking or imagining.” An AI program can mimic this kind of intelligence, perhaps enough to fool a human observer. But it’s inferiority to that observer in thinking, imagining, or creating, remains permanent.

Inevitably AI developments are going to be disruptive—they already are—but not in the way people think or the way the government wants you to think.

The first step is realizing that AI is a bottom up and not top-down revolution. It is driven by a wide range of individual entrepreneurs and small companies, as well as the usual mega players like Microsoft and Google and Amazon. Done right, it’s a revolution that means more freedom and autonomy for individual users, not less.

AI can perform many of the menial repetitive tasks that most of us would associate with human intelligence. It can sort and categorize with speed and efficiency; it can recognize patterns in words and images most of us might miss, and put together known facts and relationships in ways that anticipate development of similar patterns in the future. As we’ll demonstrate, AI’s unprecedented power to sharpen the process of predicting what might happen next, based on its insights into what’s happened before, actually empowers people to do what they do best: decide for themselves what they want to do.

Any technological revolution so sweeping and disruptive is bound to generate risks, as did the Industrial Revolution in the late eighteenth century and the computer revolution in the late twentieth. But in the end the risks are far outweighed by the endless possibilities. That’s why calls for a moratorium on large-scale AI research, or creating government entities to regulate what AI applications are allowed or banned, not only fly in the face of empirical reality but play directly into the hands of those who want to use AI as a tool for furthering the power of the administrative, or even absolute, state. That kind of centralized top-down regulatory control is precisely the path that AI development has taken in China. It is also the direction that many of the leading voices calling for AI regulation in the U.S. would like our country to move in.

Critics and AI fearmongers can’t escape one ineluctable fact: there is no way to put the AI gini back in its bottle. According to a company that tracks startup companies, Tracxn Technologies, at the end of 2022 there were more than 13,398 AI startups in this country. A recent Adobe study found that seventy-seven percent of consumers now use some form of AI technology. A McKinsey survey on the state of AI in 2022 found that AI adoption more than doubled since 2017 (from 20% to 50%), with 63% of businesses expecting investment in AI to increase over the next three years.