“Trump 2.0.” This coinage is often used to distinguish the current Trump administration from the first. The phrase is telling: it underscores that we’re in a new era and that behind the scenes, the people and forces driving this iteration of the Trump administration are different from those that came before. The second Trump administration is being driven to a greater degree by tech billionaires and their interests: self-dealing on public contracts; privatizing public services; expanding corporate power while crushing human rights; promoting crypto; removing guardrails against fraud, waste, and abuse; and pursuing personal vendettas.

The cadre of people driving today’s agenda is leveraging a set of interlocking ideologies related to technology to rhetorically justify and propel these changes. In this series, “Ideologies of Control: A Series on Tech Power and Democratic Crisis,” we asked expert contributors to name and dispel the myths and ideologies that animate their actions. A number of ideological projects prepared the ground for today’s assault on American institutions; we will focus on those that run through data, AI, and the tech sector.

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The ideological agenda behind the headlines positions technology and its architects as power brokers in an increasingly illiberal environment. Their changes are bolstered by the narrative that AI is a force that will inevitably reshape society. To make informed choices about how to respond to this aggressive reshaping of the US government, we must examine these animating ideas. We approached authors for this series who have deep knowledge about the unusual views of reactionaries in the tech industry: whether it be fantasies of libertarian paradises built on defunct sea-based oil rigs, the vision of company towns in Texas and eventually Mars, enthusiasm for debunked 19th Century ideas about race science and eugenics, or the goal of replacing the US dollar as the global reserve currency, these more esoteric backstories can be disorienting to non-specialists. But, seemingly overnight, understanding the ties that bind these projects together—however strange they may seem—has become essential information for all of us to understand and grapple with.

Tech CEOs, futurists, and venture capitalists describe artificial general intelligence (AGI) as if it were an inevitable and ultimate goal for technology development. In reality, the term is a vague signifier for a technology that will somehow lead to endless abundance for humankind — and conveniently also a means to avoid accountability as tech moguls make off with billions in capital investment and, more alarmingly, public spending.

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The second issue is closely related to the first: claims of “AGI” are a cover for abandoning the current social contract. Instead of focusing on the here and now, many people who focus on AGI think we ought to abandon all other scientific and socially beneficial pursuits and focus entirely on issues related to developing (and protecting against) AGI.

What the Apple paper shows, most fundamentally, regardless of how you define AGI, is that LLMs are no substitute for good well-specified conventional algorithms. (They also can’t play chess as well as conventional algorithms, can’t fold proteins like special-purpose neurosymbolic hybrids, can’t run databases as well as conventional databases, etc.)

In the best case (not always reached) they can write python code, supplementing their own weaknesses with outside symbolic code, but even this is not reliable. What this means for business and society is that you can’t simply drop o3 or Claude into some complex problem and expect it to work reliably.

Worse, as the latest Apple papers shows, LLMs may well work on your easy test set (like Hanoi with 4 discs) and seduce you into thinking it has built a proper, generalizable solution when it does not.