AI’s exponential growth outpaces security frameworks
Jason Clinton, whose company Anthropic operates at the forefront of AI development, didn’t hold back. “Every single year for the last 70 years, since the perceptron came out in 1957, we have had a 4x year-over-year increase in the total amount of compute that has gone into training AI models,” he explained, emphasizing the relentless acceleration of AI’s power. “If we want to skate to where the puck is going to be in a few years, we have to anticipate what a neural network that’s four times more compute has gone into it a year from now, and 16x more compute has gone into it two years from now.”
Clinton warned that this rapid growth is pushing AI capabilities into uncharted territory, where today’s safeguards may quickly become obsolete. “If you plan for the models and the chatbots that exist today, and you’re not planning for agents and sub-agent architectures and prompt caching environments, and all of the things emerging on the leading edge, you’re going to be so far behind,” he cautioned. “We’re on an exponential curve, and an exponential curve is a very, very difficult thing to plan for.”
AI hallucinations and the risk to consumer trust
For Dave Zhou at Instacart, the challenges are immediate and pressing. He oversees the security of vast amounts of sensitive customer data and confronts the unpredictable nature of large language models (LLMs) daily. “When we think about LLMs with memory being Turing complete and from a security perspective, knowing that even if you align these models to only answer things in a certain way, if you spend enough time prompting them, curing them, nudging them, there may be ways you can kind of break some of that,” Zhou pointed out.
Zhou shared a striking example of how AI-generated content could lead to real-world consequences. “Some of the initial stock images of various ingredients looked like a hot dog, but it wasn’t quite a hot dog—it looked like, kind of like an alien hot dog,” he said. Such errors, he argued, could erode consumer trust or, in more extreme cases, pose actual harm. “If the recipe potentially was a hallucinated recipe, you don’t want to have someone make something that may actually harm them.”
Preparing for the unknown: AI’s future poses new challenges
Clinton, whose company operates on the cutting edge of AI intelligence, provided a glimpse into the future—one that demands vigilance. He described a recent experiment with a neural network at Anthropic that revealed the complexities of AI behavior.
“We discovered that it’s possible to identify in a neural network exactly the neuron associated with a concept,” he said. Clinton described how a model trained to associate specific neurons with the Golden Gate Bridge couldn’t stop talking about the bridge, even in contexts where it was wildly inappropriate. “If you asked the network… ‘tell me if you know, you can stop talking about the Golden Gate Bridge,’ it actually recognized that it could not stop talking about the Golden Gate Bridge,” he revealed, noting the unnerving implications of such behavior.
Clinton suggested that this research points to a fundamental uncertainty about how these models operate internally—a black box that could harbor unknown dangers. “As we go forward… everything that’s happening right now is going to be so much more powerful in a year or two years from now,” Clinton said. “We have neural networks that are already sort of recognizing when their neural structure is out of alignment with what they consider to be appropriate.”
As AI systems become more deeply integrated into critical business processes, the potential for catastrophic failure grows. Clinton painted a future where AI agents, not just chatbots, could take on complex tasks autonomously, raising the specter of AI-driven decisions with far-reaching consequences. “If you plan for the models and the chatbots that exist today… you’re going to be so far behind,” he reiterated, urging companies to prepare for the future of AI governance.
Credit: venturebeat.com
