Imagine a pharmaceutical company spends nearly four years touting a ground-breaking new medicine that will cure all diseases.
Early evidence supporting those claims is limited, and massive questions remain, but the company continues to make progress. Criticism and concerns, while addressed, don’t slow its aggressive push.
After all, investors believe this is the real deal. (Or at least a really good bet.)
And then right before the pharma company unveils its fancy new drug, it gives a shocking update: It turns out the medicine could also be a poison. So, for the safety of all involved, progress in the space needs to be slowed to ensure things don’t go off the rails.
That’s basically the big debate going on in AI land these days.
After years of telling us how groundbreaking their tech will be, AI leaders like Anthropic’s Dario Amodei, OpenAI’s Sam Altman, and xAI’s Elon Musk are saying we need to slow the development of their most advanced models or risk a very dark future for humanity.
Is it about safety, or self-preservation?
A tumultuous 2026 for AI
Silicon Valley has been warning about the downside of AI for years. But this weekend was the type of coordinated response the industry has rarely seen.
The calls for an AI slowdown aren’t happening in a vacuum. The industry’s facing pressure on multiple fronts these days. Data centers, for one, have drawn the public’s ire. AI’s biggest supporters have even said the industry needs to do a better job of pitching itself to people.
The business case for AI remains an open question. Tired of hearing about its potential, investors and analysts are increasingly pressuring public companies to show their returns on these massive AI bets that are burning cash.
Open-weight models, a cheaper alternative to pricey frontier models, are causing headaches for companies trying to make money from model development.
Two of the biggest players in the space — Anthropic and OpenAI — are also preparing for their public debuts. That means opening up their books and themselves to even more scrutiny.
And while both have previously acknowledged the risks of AI, that hasn’t slowed them down. Even Amodei, one of the biggest proponents of AI safety, has a habit of simultaneously warning about the risks while advancing new models.
This time, Amodei’s concerns don’t seem to be slowing its IPO plans. On Sunday, my colleague Katie Roof reported that Anthropic tapped Nasdaq for its public listing.
The benefit of a delay
Pacing AI development solves a lot of those problems.
Let’s take the AI leaders’ proposal at face value. In that light, it’s easy to see how creating more guardrails would capture the goodwill of the general public, something it’s definitely lacking.
Behind the scenes, adding any regulatory framework benefits the people already ahead. The biggest players can help write the new rules, leaving smaller players on the outside looking in.
Compliance also tends to be a costly endeavor — lawyers aren’t cheap! — which is another hurdle for upstarts in the space hoping to catch up.
And then there’s the biggest advantage a slowdown provides: a built-in excuse.
How come you didn’t hit your Q4 targets? Safety concerns required us to take a more measured approach. Why isn’t that new enterprise product out? We are choosing to innovate at a pace we are comfortable with. Why haven’t your costs come down? There are built-in expenses we cannot cut if we want to stay committed to building safe models.
Those safety concerns are already affecting OpenAI. Sam Altman said it’s an “ill-advised” time to IPO.
Students have dogs that eat homework. AI companies now have the fate of humanity to worry about before they can hit analysts’ estimates or investors’ expectations.
But what if the risk is real?
I’m not suggesting there aren’t real issues with the pace of AI development. The tech does seem capable of eventually doing some pretty terrible things if not kept in check.
But choosing to take a coordinated approach to this issue now, after billions of dollars have been raised and valuations have skyrocketed, is very convenient.
What if something truly changed that scared AI leaders into wanting a pause?
I’ve heard this theory floated for a while. Models can make big leaps in a short amount of time. Something could be happening behind the scenes that has them all worried.
If that’s the case, the most likely solution will be … more AI.
But even if this is about how humans fit into that equation, the AI apocalypse debate raises another important question. One that AI giants probably won’t want to answer.
If the tech is so powerful that the AI giants are unsure how to handle it, why are these company leaders the right people to handle it in the first place?
