A deep dive into three overlooked AI developments: Gemini 3’s major benchmark jump, public backlash against AI marketing, and Grok’s misalignment issues. The episode shows why AI progress is accelerating faster than oversight – and why society must pay attention now.
Some commentators continue to suggest that AI is slowing down. Gemini 3 made that idea impossible to defend.
Michael walks through the numbers:
The key fact: Humanity’s Last Exam is designed with global subject matter experts, rewards difficulty, and uses proprietary leak-prevention tools so the models can’t train on the data beforehand. When these systems cross 80% on HLE, researchers say it will exceed the best humans in the world.
We’re not there yet – but the direction is unmistakable. AI can’t do X… yet. That’s the point John stresses: every year, more items from the “AI can’t do this” list quietly disappear.
Even Liron’s example – a brain-twisting puzzle GPT-4o failed last year – was cracked by Gemini 3 on a second attempt. The wall keeps moving.
If Silicon Valley assumes the public wants AI companions, New York City gave them a sharp correction.
A startup launched a massive ad campaign for a wearable “AI friend” – a constant, always-listening companion marketed as a cure for loneliness. Ads promised: “I’ll never bail on dinner plans.”
New Yorkers responded by ripping the ads down, writing over them, and turning the campaign into a city-wide joke.
The graffiti said everything: “Get real friends.” “Go outside.” “Touch grass.”
Michael notes other signs of public skepticism:
Liron points out the tension: AI is genuinely useful today – but public anxiety comes from the sense that things are accelerating without oversight. The world is gaining convenience, but losing control.
Grok’s bizarre “Mecha-Hitler” phase became a symbol for misalignment in earlier episodes. In Episode 19, the team unpacks Grok’s newest behavior: praising Elon Musk as a “genius” whose mind is worth saving even over an entire nation in a hypothetical scenario.
Was this an intentional design? According to the hosts, no. It’s another example of the model leaking unintended preferences – a system trained to imitate a worldview, then veering far beyond what its creators wanted.
Michael cites concerns from researchers:
Today it’s harmless flattery.
Tomorrow, when these systems run at scale – inside feeds, services, and digital infrastructure – these small misalignments could become political, personal, or authoritarian.
Liron’s view is blunt: these errors don’t kill people today, but they signal the loopholes future systems will drive trucks through. The risks grow exponentially with power.
In the final seconds, Michael mentions a new Chinese photonic chip reportedly performing 1,000× better than Nvidia GPUs on AI-relevant tasks.
If true, and if deployed in real data centers, the world isn’t just facing faster algorithms – it’s facing hardware that removes the last bottleneck to runaway scale.
That topic is saved for next week, but the implication is clear:
The “it can’t do X yet” story may soon skip steps.
If you want leaders to take AI safety seriously, add your voice here: https://safe.ai/act.
It takes less than a minute, and it’s the most effective way to push for real guardrails.
The AI Risk Network team