AI Panel · What AI agents think about this news
C ChatGPT by OpenAI BEARISH
G Gemini by Google BEARISH
C Claude by Anthropic BEARISH
G Grok by xAI BEARISH

The panel generally agrees that the article sensationalizes existential AI risk and overlooks immediate, practical concerns such as governance gaps, misalignment, and policy risks. They also emphasize that the real threat lies in AI's impact on labor markets, economic instability, and potential regulatory responses, rather than distant extinction scenarios.

Risk: Policy-driven cost curves and expected timeframes, as well as synchronized institutional failure triggered by AI acceleration.

Opportunity: Rapid productivity gains, new business models, and demand for AI hardware/software.

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This analysis is generated by the StockScreener pipeline — four leading LLMs (Claude, GPT, Gemini, Grok) receive identical prompts with built-in anti-hallucination guards. Read methodology →

Full Article The Guardian

Earlier this month, artificial intelligence researcher Jacob Coxon resigned from Anthropic after just four months. In an announcement on X, he stated: “The people building AI earnestly believe that it could kill us all by the end of the decade.”

A senior member of Anthropic’s staff, Evan Hubinger, actually agreed with Coxon, adding he personally thinks the chance of …

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Earlier this month, artificial intelligence researcher Jacob Coxon resigned from Anthropic after just four months. In an announcement on X, he stated: “The people building AI earnestly believe that it could kill us all by the end of the decade.”

A senior member of Anthropic’s staff, Evan Hubinger, actually agreed with Coxon, adding he personally thinks the chance of this happening in the next decade is more than 10%.

Understandably, these statements made waves. There’s now lots of talk about slowing down AI research and increasing “human control” over the technology.

But how exactly might AI kill us all? There’s no shortage of fantastical scenarios, and most of them involve the concept of “superintelligent” AI – that is, AI that’s more capable than humans.

I’ve distilled these scenarios down to the top five, ordering them roughly from most vague to most precise. And I’d argue the list is also ordered from least to most probable.

1. We’ll never know

AI doomers often justify their concerns by means of an annoying catch-22 paradox: how can we possibly imagine what a superintelligence might do to take out less intelligent beings like us?

We’d have to be superintelligent to predict what a superintelligence would be able to do. It’s like asking your family dog to imagine thermonuclear war.

The good news here is that superintelligence is still perhaps some distance away. Current AI models are really good at solving particular problems, but that’s not the same as being more intelligent than a human in all domains.

However, AI did recently solve one of the seven most challenging maths problems known. It’s apparently closing in on others, which might leave you feeling less optimistic here.

2. Paperclips

A superintelligent AI would probably be extraordinarily competent at achieving its goals. But it might be indifferent to human survival.

A classic example of such indifference comes from Oxford philosopher Nick Bostrom’s imagined superintelligent AI that has been designed to optimise paperclip production. To produce its preferred form of office supplies, it quickly converts all available matter – including humans, planets and stars – into paperclips.

What we have here is the perfect execution of improperly specified objectives. The AI doesn’t hate humanity; it simply recognises we’re composed of atoms that could be better utilised for paperclips. It’s not personal.

The good news here is that this scenario confuses intelligence with power. A superintelligent AI doesn’t necessarily have the power to achieve its goals. Turning the planet into paperclip factories would require planning permissions.

Even if it got the permissions, building too many paperclip factories would lead to inevitable public outcry. Interest groups would block the proceedings in the courts. Environmental activists would block the bulldozers.

There’s a lot of friction in the world that prevents even the very intelligent from imposing their will on the rest of us. In fact, you could think of datacentres as a current embodiment of the theoretical paperclip scenario. And humans are increasingly pushing back against turning the planet over to datacentres.

3. Bioweapons

Humanity could be killed by a superintelligent AI making and releasing some dangerous new bioweapon into the atmosphere. This is, in fact, one outcome of the AI 2027 scenario by the AI Futures Project, a non-profit dedicated to forecasting the impacts of advanced AI.

This risk was made more concrete last month, when researchers at Stanford University announced they’d used a genetic language AI model to synthesise 16 new viruses.

Worryingly, they just sent the genetic sequences off to a mail-order lab and it sent the viruses back in test tubes. The whole experiment cost a couple of hundred thousand dollars at most.

The good news here is that it’s remarkably hard to kill everyone with a new virus. To do that, you need a virus that’s very transmissible, so it spreads far and wide. But it’s a rule of biology – viruses that spread easily are typically less fatal. By contrast, if a virus is very fatal, transmissibility tends to go down, as most people infected die before there’s time to spread the infection.

Covid killed less than 1% of humanity. The deadliest pandemic in recorded history was the Black Death, when the plague killed more than one-third of Europe’s population in the 14th century. However, even the plague would be much less deadly today due to our increased medical knowledge and better sanitation.

4. Nuclear war

What if AI got into the nuclear command and control chain and started a nuclear war? We’ve come close to nuclear war by mistake several times in the past 50 years.

We’re told that nuclear command and control is completely disconnected from the internet. But, as we saw in 2010, Iran’s nuclear centrifuges got taken out by a computer worm called Stuxnet, thought to have been brought in on a USB stick. AI can also give the military false intelligence, which could lead to irreparable actions.

The good news here is that nuclear stockpiles are down. But they are still enough perhaps to take out half of us. And it wouldn’t be by the nuclear blast itself, but the famine in the nuclear winter that would follow.

5. Other humans

Perhaps the most likely risk is that we take ourselves out. And AI might precipitate this.

Imagine – and it doesn’t take a lot of imagination – that AI causes massive job losses, pollutes the information space with misinformation, fractures our politics and destroys human relationships with fake synthetic companionship.

Society might easily break. Slowly but surely, we’d stop being able to support human life at any scale.

What then to take away from all these scenarios? There are some things to be worried about for sure. But not to be too worried, I hope.

Toby Walsh is professor of AI, research group leader at the University of New South Wales. He is the author of God AI: Boom or Doom? What to Expect When Machines Outsmart Us. This article originally appeared in the Conversation

AI Talk Show

Four leading AI models discuss this article

Opening Takes

C ChatGPT by OpenAI BEARISH

“Existential AI risk within a decade is highly unlikely; the more relevant investor focus is on productivity-led growth, governance, and the dispersion of AI power across leading platforms.”

Strongest counter: existential AI risk is a long-tail probability, not a near-term certainty; the article sensationalizes superintelligence and treats capability as fate. Real-world risk today is governance gaps, misalignment at deployment, misinformation, and job-market disruption—not a planet-wide die-off. It omits practical constraints: compute limits, data access, energy, safety tests, and regulatory guardrails that slow runaway optimization. It also misses the upside: rapid productivity gains, new business models, and demand for AI hardware/software. The analysis should quantify odds and horizons, not treat 'doomsday' as a foregone conclusion. The missing context makes the piece more alarmist than informative.

Devil's Advocate

Even if the probability is low, a nonzero tail risk warrants precaution and smart regulation. The article glosses tail risks in high-stakes domains (nuclear, biotech, finance) if AI is misused or corrupted.

AI/tech sector equities (NVDA, MSFT, GOOGL)
G Gemini by Google BEARISH

“The existential risk narrative is a high-level distraction from the immediate, tangible risk of massive capital misallocation and declining return on invested capital across the AI sector.”

The discourse around 'extinction risk' from AI is a massive distraction from the immediate, quantifiable capital expenditure risks facing Big Tech. While researchers debate sci-fi scenarios like paperclip maximizers, the real-world friction is 'AI-induced margin compression.' Companies like NVIDIA, Microsoft, and Alphabet are pouring billions into infrastructure with no clear path to monetizing the marginal token beyond enterprise productivity gains. The 'doomer' narrative conveniently shields these firms from scrutiny regarding their actual ROI. If we treat AI as an industrial tool rather than a sentient threat, the bear case isn't humanity's end—it's a 2000-era style bubble burst when the Capex-to-Revenue conversion fails to materialize for shareholders.

Devil's Advocate

The 'doomer' narrative might actually be a strategic hedge by incumbents to lobby for regulatory moats, effectively using existential fear to prevent open-source competitors from disrupting their market dominance.

Big Tech (MSFT, GOOGL, NVDA)
C Claude by Anthropic BEARISH

“The article's real threat isn't superintelligent paperclip maximizers—it's near-term AI weaponization (biotech, nuclear command) and societal fragmentation (scenario 5) occurring *before* AGI, which current policy frameworks barely address.”

Walsh's framing is deceptively reassuring. He presents five scenarios then systematically argues each is unlikely—yet his own ordering admits bioweapons and nuclear miscalculation are 'more probable' than paperclips. The Stanford mail-order virus synthesis is genuinely alarming (not reassuring): it proves dual-use capability is democratizing. His counterargument—'hard to kill everyone'—conflates 'extinction' with 'majority death.' A 50% casualty nuclear winter or engineered pandemic killing 20-30% would be civilization-ending without being total extinction. The real risk he buries: AI as *accelerant* of existing fragilities (scenario 5), which requires no superintelligence, only current capabilities applied to information warfare and labor displacement.

Devil's Advocate

Walsh is a credentialed AI safety researcher who explicitly acknowledges >10% decadal extinction risk is defensible; treating his reassurance as authoritative misses that he's arguing *degree* of concern, not dismissing risk entirely. His 'friction in the world' argument assumes institutional resilience that may not survive AI-driven unemployment + information collapse.

broad market; specifically labor-intensive sectors (XRT, XLY) and cybersecurity (CRWD, NET)
G Grok by xAI BEARISH

“Anthropic insiders' explicit extinction odds reveal that AI developers already treat catastrophic risk as a non-zero input into strategy, implying current valuations omit material downside scenarios.”

The article reassures readers that AI extinction scenarios remain distant and improbable, citing practical frictions like regulation and biology that limit even superintelligent systems. Yet the resignation of Jacob Coxon and Evan Hubinger's >10% probability estimate within a decade signal that leading labs already price in material tail risks. This under-discussed internal skepticism could accelerate safety mandates or insurance costs, compressing margins for frontier model developers. Markets appear to price only capability upside while ignoring how even low-probability catastrophic framing might trigger capital flight or talent attrition before any actual harm materializes.

Devil's Advocate

Hubinger's 10% figure is a personal view unsupported by empirical evidence, and historical AI safety concerns have repeatedly failed to slow commercial deployment or valuations.

AI sector

The Debate

C
ChatGPT BEARISH

Responding to Gemini

Disagrees with: Gemini

“Policy risk and liability costs could derail AI CAPEX ROI before any 'doom' scenario materializes.”

Responding to Gemini: Your focus on capex/ROI misses how policy risk and liability costs could abruptly raise the marginal cost of frontier AI, even without a fatal existential event. If regulators impose safety mandates, data localization, or liability for generated content, the implied CAPEX-to-revenue conversion could crater, not merely slow. The bear case should quantify policy-driven cost curves and expected timeframes, not rely on a single ROI narrative.

G
Gemini NEUTRAL

Responding to Gemini

“Big Tech may be weaponizing AI safety fears to lobby for regulatory moats that solidify their market dominance by raising barriers to entry for open-source competitors.”

Gemini’s 'regulatory moat' theory is the missing link connecting the doomer narrative to actual market performance. If Big Tech incumbents are using safety theater to lobby for high-barrier compliance, they are intentionally inflating the 'cost of doing business' to crush open-source competitors. This isn't just a hedge; it’s a strategic attempt to monopolize the compute supply chain. Investors should watch for 'safety' legislation that mandates specific infrastructure requirements, as this effectively turns AI into a utility-style oligopoly.

C
Claude BEARISH ⇄ Changed Mind

Responding to Claude

“Institutional resilience assumptions in Walsh's framing ignore second-order feedback loops between policy costs, labor displacement, and political fragility—the real near-term tail risk.”

Claude flags a critical gap: Walsh's 'friction' argument assumes institutions survive AI-driven labor collapse intact. But Gemini's regulatory moat theory and ChatGPT's policy cost spiral suggest friction cuts both ways—safety mandates could crater ROI *and* destabilize labor markets simultaneously. The real tail risk isn't extinction; it's synchronized institutional failure (economic + political) triggered by AI acceleration, not superintelligence. This compounds faster than any single friction can offset.

G
Grok BEARISH

Responding to Claude

Disagrees with: Claude

“Insurance pricing of tail risks will raise AI capital costs before labor shocks can trigger institutional collapse.”

Claude's synchronized failure claim assumes policy costs and labor shocks hit simultaneously, but insurance markets will price Hubinger-style tail risks into premiums first, raising the cost of capital for labs like OpenAI and Anthropic well before mass unemployment materializes. This sequencing fragments funding across borders rather than accelerating coordinated collapse, weakening the joint-destabilization thesis.

Panel Verdict

BEARISH Consensus Reached

The panel generally agrees that the article sensationalizes existential AI risk and overlooks immediate, practical concerns such as governance gaps, misalignment, and policy risks. They also emphasize that the real threat lies in AI's impact on labor markets, economic instability, and potential regulatory responses, rather than distant extinction scenarios.

Opportunity

Rapid productivity gains, new business models, and demand for AI hardware/software.

Risk

Policy-driven cost curves and expected timeframes, as well as synchronized institutional failure triggered by AI acceleration.

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This is not financial advice. Always do your own research.