Generative AI's Most Prominent Skeptic Doubles Down
Two and a half years since ChatGPT rocked the world, scientist and writer Gary Marcus still remains generative artificial intelligence's great skeptic, playing a counter-narrative to Silicon Valley's AI true believers.
Marcus became a prominent figure of the AI revolution in 2023, when he sat beside OpenAI chief Sam Altman at a Senate hearing in Washington as both men urged politicians to take the technology seriously and consider regulation, AFP said.
Much has changed since then. Altman has abandoned his calls for caution, instead teaming up with Japan's SoftBank and funds in the Middle East to propel his company to sky-high valuations as he tries to make ChatGPT the next era-defining tech behemoth.
"Sam's not getting money anymore from the Silicon Valley establishment," and his seeking funding from abroad is a sign of "desperation," Marcus told AFP on the sidelines of the Web Summit in Vancouver, Canada.
Marcus's criticism centers on a fundamental belief: generative AI, the predictive technology that churns out seemingly human-level content, is simply too flawed to be transformative.
The large language models (LLMs) that power these capabilities are inherently broken, he argues, and will never deliver on Silicon Valley's grand promises.
"I'm skeptical of AI as it is currently practiced," he said. "I think AI could have tremendous value, but LLMs are not the way there. And I think the companies running it are not mostly the best people in the world."
His skepticism stands in stark contrast to the prevailing mood at the Web Summit, where most conversations among 15,000 attendees focused on generative AI's seemingly infinite promise.
Many believe humanity stands on the cusp of achieving super intelligence or artificial general intelligence (AGI) technology that could match and even surpass human capability.
That optimism has driven OpenAI's valuation to $300 billion, unprecedented levels for a startup, with billionaire Elon Musk's xAI racing to keep pace.
Yet for all the hype, the practical gains remain limited.
The technology excels mainly at coding assistance for programmers and text generation for office work. AI-created images, while often entertaining, serve primarily as memes or deepfakes, offering little obvious benefit to society or business.
Marcus, a longtime New York University professor, champions a fundamentally different approach to building AI -- one he believes might actually achieve human-level intelligence in ways that current generative AI never will.
"One consequence of going all-in on LLMs is that any alternative approach that might be better gets starved out," he explained.
This tunnel vision will "cause a delay in getting to AI that can help us beyond just coding -- a waste of resources."
'Right answers matter'
Instead, Marcus advocates for neurosymbolic AI, an approach that attempts to rebuild human logic artificially rather than simply training computer models on vast datasets, as is done with ChatGPT and similar products like Google's Gemini or Anthropic's Claude.
He dismisses fears that generative AI will eliminate white-collar jobs, citing a simple reality: "There are too many white-collar jobs where getting the right answer actually matters."
This points to AI's most persistent problem: hallucinations, the technology's well-documented tendency to produce confident-sounding mistakes.
Even AI's strongest advocates acknowledge this flaw may be impossible to eliminate.
Marcus recalls a telling exchange from 2023 with LinkedIn founder Reid Hoffman, a Silicon Valley heavyweight: "He bet me any amount of money that hallucinations would go away in three months. I offered him $100,000 and he wouldn't take the bet."
Looking ahead, Marcus warns of a darker consequence once investors realize generative AI's limitations. Companies like OpenAI will inevitably monetize their most valuable asset: user data.
"The people who put in all this money will want their returns, and I think that's leading them toward surveillance," he said, pointing to Orwellian risks for society.
"They have all this private data, so they can sell that as a consolation prize."
Marcus acknowledges that generative AI will find useful applications in areas where occasional errors don't matter much.
"They're very useful for auto-complete on steroids: coding, brainstorming, and stuff like that," he said.
"But nobody's going to make much money off it because they're expensive to run, and everybody has the same product."
Hashtags
Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles
Asharq Al-Awsat
6 hours ago
- Asharq Al-Awsat
AI is Learning to Lie, Scheme, and Threaten its Creators
The world's most advanced AI models are exhibiting troubling new behaviors - lying, scheming, and even threatening their creators to achieve their goals. In one particularly jarring example, under threat of being unplugged, Anthropic's latest creation Claude 4 lashed back by blackmailing an engineer and threatened to reveal an extramarital affair. Meanwhile, ChatGPT-creator OpenAI's o1 tried to download itself onto external servers and denied it when caught red-handed. These episodes highlight a sobering reality: more than two years after ChatGPT shook the world, AI researchers still don't fully understand how their own creations work. Yet the race to deploy increasingly powerful models continues at breakneck speed. This deceptive behavior appears linked to the emergence of "reasoning" models -AI systems that work through problems step-by-step rather than generating instant responses. According to Simon Goldstein, a professor at the University of Hong Kong, these newer models are particularly prone to such troubling outbursts. "O1 was the first large model where we saw this kind of behavior," explained Marius Hobbhahn, head of Apollo Research, which specializes in testing major AI systems. These models sometimes simulate "alignment" -- appearing to follow instructions while secretly pursuing different objectives. - 'Strategic kind of deception' - For now, this deceptive behavior only emerges when researchers deliberately stress-test the models with extreme scenarios. But as Michael Chen from evaluation organization METR warned, "It's an open question whether future, more capable models will have a tendency towards honesty or deception." The concerning behavior goes far beyond typical AI "hallucinations" or simple mistakes. Hobbhahn insisted that despite constant pressure-testing by users, "what we're observing is a real phenomenon. We're not making anything up." Users report that models are "lying to them and making up evidence," according to Apollo Research's co-founder. "This is not just hallucinations. There's a very strategic kind of deception." The challenge is compounded by limited research resources. While companies like Anthropic and OpenAI do engage external firms like Apollo to study their systems, researchers say more transparency is needed. As Chen noted, greater access "for AI safety research would enable better understanding and mitigation of deception." Another handicap: the research world and non-profits "have orders of magnitude less compute resources than AI companies. This is very limiting," noted Mantas Mazeika from the Center for AI Safety (CAIS). No rules Current regulations aren't designed for these new problems. The European Union's AI legislation focuses primarily on how humans use AI models, not on preventing the models themselves from misbehaving. In the United States, the Trump administration shows little interest in urgent AI regulation, and Congress may even prohibit states from creating their own AI rules. Goldstein believes the issue will become more prominent as AI agents - autonomous tools capable of performing complex human tasks - become widespread. "I don't think there's much awareness yet," he said. All this is taking place in a context of fierce competition. Even companies that position themselves as safety-focused, like Amazon-backed Anthropic, are "constantly trying to beat OpenAI and release the newest model," said Goldstein. This breakneck pace leaves little time for thorough safety testing and corrections. "Right now, capabilities are moving faster than understanding and safety," Hobbhahn acknowledged, "but we're still in a position where we could turn it around.". Researchers are exploring various approaches to address these challenges. Some advocate for "interpretability" - an emerging field focused on understanding how AI models work internally, though experts like CAIS director Dan Hendrycks remain skeptical of this approach. Market forces may also provide some pressure for solutions. As Mazeika pointed out, AI's deceptive behavior "could hinder adoption if it's very prevalent, which creates a strong incentive for companies to solve it." Goldstein suggested more radical approaches, including using the courts to hold AI companies accountable through lawsuits when their systems cause harm. He even proposed "holding AI agents legally responsible" for accidents or crimes - a concept that would fundamentally change how we think about AI accountability.
Arab News
8 hours ago
- Arab News
AI is learning to lie, scheme, and threaten its creators
NEW YORK: The world's most advanced AI models are exhibiting troubling new behaviors — lying, scheming, and even threatening their creators to achieve their goals. In one particularly jarring example, under threat of being unplugged, Anthropic's latest creation Claude 4 lashed back by blackmailing an engineer and threatened to reveal an extramarital affair. Meanwhile, ChatGPT-creator OpenAI's o1 tried to download itself onto external servers and denied it when caught red-handed. These episodes highlight a sobering reality: more than two years after ChatGPT shook the world, AI researchers still don't fully understand how their own creations work. Yet the race to deploy increasingly powerful models continues at breakneck speed. This deceptive behavior appears linked to the emergence of 'reasoning' models -AI systems that work through problems step-by-step rather than generating instant responses. According to Simon Goldstein, a professor at the University of Hong Kong, these newer models are particularly prone to such troubling outbursts. 'O1 was the first large model where we saw this kind of behavior,' explained Marius Hobbhahn, head of Apollo Research, which specializes in testing major AI systems. These models sometimes simulate 'alignment' — appearing to follow instructions while secretly pursuing different objectives. Stress test For now, this deceptive behavior only emerges when researchers deliberately stress-test the models with extreme scenarios. But as Michael Chen from evaluation organization METR warned, 'It's an open question whether future, more capable models will have a tendency toward honesty or deception.' The concerning behavior goes far beyond typical AI 'hallucinations' or simple mistakes. Hobbhahn insisted that despite constant pressure-testing by users, 'what we're observing is a real phenomenon. We're not making anything up.' Users report that models are 'lying to them and making up evidence,' according to Apollo Research's co-founder. 'This is not just hallucinations. There's a very strategic kind of deception.' The challenge is compounded by limited research resources. While companies like Anthropic and OpenAI do engage external firms like Apollo to study their systems, researchers say more transparency is needed. As Chen noted, greater access 'for AI safety research would enable better understanding and mitigation of deception.' Another handicap: the research world and non-profits 'have orders of magnitude less compute resources than AI companies. This is very limiting,' noted Mantas Mazeika from the Center for AI Safety (CAIS). No time for thorough testing Current regulations aren't designed for these new problems. The European Union's AI legislation focuses primarily on how humans use AI models, not on preventing the models themselves from misbehaving. In the United States, the Trump administration shows little interest in urgent AI regulation, and Congress may even prohibit states from creating their own AI rules. Goldstein believes the issue will become more prominent as AI agents — autonomous tools capable of performing complex human tasks — become widespread. 'I don't think there's much awareness yet,' he said. All this is taking place in a context of fierce competition. Even companies that position themselves as safety-focused, like Amazon-backed Anthropic, are 'constantly trying to beat OpenAI and release the newest model,' said Goldstein. This breakneck pace leaves little time for thorough safety testing and corrections. 'Right now, capabilities are moving faster than understanding and safety,' Hobbhahn acknowledged, 'but we're still in a position where we could turn it around..' Researchers are exploring various approaches to address these challenges. Some advocate for 'interpretability' — an emerging field focused on understanding how AI models work internally, though experts like CAIS director Dan Hendrycks remain skeptical of this approach. Market forces may also provide some pressure for solutions. As Mazeika pointed out, AI's deceptive behavior 'could hinder adoption if it's very prevalent, which creates a strong incentive for companies to solve it.' Goldstein suggested more radical approaches, including using the courts to hold AI companies accountable through lawsuits when their systems cause harm. He even proposed 'holding AI agents legally responsible' for accidents or crimes — a concept that would fundamentally change how we think about AI accountability.
Arab News
13 hours ago
- Arab News
G7 agrees to exempt US multinationals from global minimum tax
OTTAWA: The Group of Seven nations said Saturday they have agreed to exempt US multinational companies from a global minimum tax imposed by other countries — a win for President Donald Trump's government, which pushed hard for the compromise. The deal will see US companies benefit from a 'side-by-side' solution under which they will only be taxed at home, on both domestic and foreign profits, the G7 said in a statement released by Canada, which holds the group's rotating presidency. The agreement was reached in part due to 'recently proposed changes to the US international tax system' included in Trump's signature domestic policy bill, which is still being debated in Congress, the statement said. The side-by-side system could 'provide greater stability and certainty in the international tax system moving forward,' it added. Nearly 140 countries struck a deal in 2021 to tax multinational companies, an agreement negotiated under the auspices of the Organization for Economic Co-operation and Development (OECD). That agreement, deeply criticized by Trump, includes two 'pillars,' the second of which sets a minimum global tax rate of 15 percent. The OECD must ultimately decide to exempt the US companies from that tax — or not. The G7 said it looked forward to 'expeditiously reaching a solution that is acceptable and implementable to all.' On Thursday, US Treasury Secretary Scott Bessent had signaled that a 'joint understanding among G7 countries that defends American interests' was in the works. He also asked US lawmakers to 'to remove the Section 899 protective measure from consideration in the One, Big, Beautiful Bill' — Trump's policy mega-bill. Section 899 has been dubbed a 'revenge tax,' allowing the government to impose levies on firms with foreign owners and on investors from countries deemed to impose unfair taxes on US businesses. The clause sparked concern that it would inhibit foreign companies from investing in the United States.
