AI Daily Digest: Thursday, May 07, 2026
Today, I want to spend most of our time on something that really caught my eye—the whole idea of measuring AI creativity, which feels like a big step forward in how we think about these systems. While everyone else is fixated on benchmarks for reasoning or coding, researchers have quietly put together the first solid framework for testing something trickier: an AI's knack for rethinking everyday objects in fresh, unexpected ways. It's not just about following patterns; this could point toward real innovation, and I'm starting to wonder if we're finally getting closer to what makes human creativity tick.
That CreativityBench work deserves a closer look, not only for the smarts behind it but for what it uncovers about where AI falls short today. We've got all these huge investments in hardware—like Anthropic's deal for 220,000 GPUs with SpaceX—but the real question, I think, is whether these setups can spark true originality or if they're just better at shuffling old ideas around in clever ways. Maybe this research will force us to face some hard truths about the limits we're up against.
The Creativity Measurement Revolution
There's more to this than the headline suggests—CreativityBench isn't just another benchmark; it's the first real push to measure AI creativity with some actual science backing it up. Let me unpack this: the team built a huge database with over 4,000 entities and more than 150,000 annotations on how objects work, covering their parts, properties, and all the ways you could repurpose them. From that, they cooked up 14,000 tasks that forced models to come up with smart, physically possible solutions that aren't obvious at all.
The findings hit hard, at least from what I've seen—across 10 top models, including both proprietary and open-source ones, things fell apart when it came to grasping the right components, their uses, and the physics involved. Sure, some could pick out a reasonable object, but they bombed on the deeper stuff, like true inventive problem-solving. And here's a kicker that makes me pause: even as models get bigger, the gains leveled off fast, which probably means piling on more parameters won't magically fix this gap. I think this exposes how far we are from AI that can improvise, say, turning a spoon into a lever or a book into a doorstop, and it's got me questioning if we're barking up the wrong tree with current designs.
Let me dig into the implications for a second—this isn't just academic; it highlights a massive divide between what AI does well, like spotting patterns for code or math, and what it struggles with, which is that human-like spark of invention we use every day. For instance, picturing an AI that could adapt a coat hanger to fish out keys from a drain feels worlds away, and I wonder if pushing for more creative AI might mean rethinking everything from training data to architecture. It's not black and white, though; maybe there are ways around it, but the evidence here suggests we've got some serious work ahead.
Infrastructure and Compute Wars Intensify
Anthropic's big move with SpaceX's Colossus-1, snagging over 300 megawatts and 220,000 NVIDIA GPUs, means faster access for users—doubled rate limits on Claude Code and no more peak-time hassles for Pro and Max tiers, plus bigger API caps for Claude Opus. It's part of their massive buildup, including 5 GW deals with Amazon and Google/Broadcom, $30 billion from Microsoft and NVIDIA, and a $50 billion one with Fluidstack, even promising to cover rising electricity costs for folks in the US.
Still, with benchmarks like this showing creativity plateaus, I have to ask if all this compute is really the answer.
Policy Pivots and Safety Theater
The Trump administration's flip-flopping on AI continues—renaming the institute to "Center for AI Standards and Innovation" and signing voluntary pacts with Google DeepMind, Microsoft, and xAI for testing models pre-release, all while Anthropic holds back Claude Mythos over security worries, which suddenly has Trump talking about risks again.
It's a messy balance between pushing ahead and managing dangers, but these agreements lack real teeth, and the rebranding hints at downplaying safety, even as companies like Anthropic step back when things get dicey.
Quick Hits
Google rolled out File Search for the Gemini API, handling the full RAG setup with chunking and embeddings; CopilotKit added persistent memory and self-hosted databases for agents; researchers cut attacker payoffs by 59% in cyber defenses using Lean 4; IEEE's 6G paper spots AI autoencoders for terahertz use; and MetaAdamW speeds up training by up to 17.11% with adaptive rates.
Connections and Patterns
Connecting the Dots
These stories tie together in a way that makes me uneasy—the rush for more hardware, like Anthropic's GPU haul, clashes with evidence from CreativityBench that scale alone won't unlock creativity, a trend we noticed building back in March 2024 when similar issues popped up in other tests. I'm not entirely sure bigger models are the fix, but it's clear we're hitting walls that might need different strategies.
And on the policy front, Trump's quick shift after that January 15th order shows how companies are hedging their bets, resisting rules publicly while pulling back internally, as with Anthropic's model delay—it's all interconnected, though the path forward feels uncertain.
CreativityBench makes me think we might be dealing with core flaws in AI that no amount of extra compute can patch, and while the industry's all-in on building bigger systems, this gap in inventive thinking suggests we need fresh ideas, not just more power. The big question, as I see it, is whether we're even asking the right ones about how AI could evolve.
Keep an eye on tomorrow for how labs respond to these results; they'll probably claim their next models will handle it, but based on what we've got, I'm skeptical that just tweaking parameters will cut it—we might have to get creative ourselves in how we approach AI development, though I'm not 100% sure what that looks like yet.