Why Big Tech Is Divided on the Future of Artificial General Intelligence

Fifteen years ago, the founders of DeepMind—Sir Demis Hassabis, Mustafa Suleyman, and Shane Legg—set a bold goal: “Build the world’s first artificial general intelligence.” Their vision challenged the limits of traditional AI, which could excel at specific tasks like chess or data analysis but couldn’t think across domains.

Today, that dream of Artificial General Intelligence (AGI) has grown from a niche ambition to a global obsession.

What Makes AGI Different?

Unlike conventional AI, AGI isn’t just about speed or data processing. It promises systems that think, reason, and learn across a wide range of tasks like humans, or possibly even better. According to OpenAI’s Sam Altman, AGI could “turbocharge the global economy” and unlock new scientific frontiers. DeepMind’s Hassabis believes it could help solve climate change, extend human life, and discover new energy sources.

But defining AGI is a complex task in itself. While some experts refer to it as an AI that performs at or above the level of a skilled adult in most cognitive domains, others use terms like “artificial superintelligence” (ASI) or “powerful AI.”

Meta’s Yann LeCun, for instance, argues that human intelligence isn’t general either, so the term AGI lacks clarity.

Competing Visions of Intelligence

Image by TechTalks | OpenAI, DeepMind, and Meta race to shape the future of general intelligence in their own way.

There’s no single roadmap to AGI, which opens the door to wildly different opinions. Elon Musk claims it will arrive by the end of this year. Anthropic’s Dario Amodei bets on 2026. Altman predicts it will happen under Donald Trump’s next presidency.

While timelines differ, funding continues to pour in. With a staggering $300 billion valuation, OpenAI alone raised $40 billion in March.

Each tech company defines AGI to match its own agenda:

1. DeepMind – Emphasizes performance across a wide range of cognitive tasks, benchmarked against typical human abilities.

2. Meta – Prefers to use world models—systems that can learn perception and physics in instead of language—to pursue ASI.

3. OpenAI – Focuses on AGI’s potential to perform economically valuable tasks across many domains with little human intervention.

Even policymakers feel the pressure. The EU may delay its AI Act to avoid restricting innovation. Meanwhile, the UK and US are building safety institutes to prepare for AGI’s potential.

Why AGI Still Feels So Elusive

Despite the buzz, no AI model today qualifies as AGI—not even ChatGPT, Gemini, or Llama 2.

According to DeepMind’s Allan Dafoe, current models only meet the lowest level of emerging AGI. None have reached the 50th percentile of human performance across cognitive domains.

DeepMind’s own scale defines AGI in five levels:

1. Emerging AGI – Equal to or slightly better than an unskilled human.
2. Competent AGI – Matches 50% of skilled adults.
3. Expert AGI – Equal to the 90th percentile.
4. Virtuoso AGI – Equal to the 99th percentile.
5. Superhuman AI – Surpasses all humans.

This framework helps explain why AGI remains more hype than reality.

Building Blocks and Barriers

OpenAI believes large language models, like its new o3 “reasoning” model, bring AGI closer. These models tackle complex tasks in math, coding, and image analysis by solving problems step by step. Next on their agenda: AI agents that act independently, produce innovations, and eventually mimic human organizations.

Yet, critics warn against overpromising. Apple researchers recently found that these reasoning models crumble under complex tasks. Language alone may never replicate intelligence. Meta’s LeCun advocates for world models that use video and sensor data to create a more grounded understanding.

There’s also the data issue. Most of the internet has already been scraped for training. AI labs now seek licensing deals with content owners, like the partnership between OpenAI and the Financial Times. Meanwhile, researchers explore synthetic data—AI-generated content to train future models—as a potential solution.

Is AGI Really the Goal?

Several experts challenge the very pursuit of AGI. Margaret Mitchell from Hugging Face argues that “intelligence” is too vague a term to guide development. Instead, it becomes a marketing tool that justifies risky and unregulated technologies.

AI critic Nick Frosst sees the AGI narrative as a strategy to unlock vast investor capital. “If someone’s been promising AGI for years and has money riding on it, you should ask why they keep being wrong,” he says.

Investors see AGI as the next trillion-dollar market—not just for software, but for shaping economies. This allure keeps capital flowing, regardless of technical progress or clarity.

Ethical, Environmental, and Social Stakes

Instagram | @mandylionpress | The rise of AI reminds us of the hidden tricks behind the old Mechanical Turk.

Even if AGI arrives, it won’t come cheap. Training massive models uses enormous amounts of water and electricity. These technologies also fuel pollution-heavy sectors like oil and gas.

There’s also the exploitation of human labor. Behind the scenes, thousands label and rank AI outputs to make them seem “intelligent.” Often, artists and writers find their work in datasets without permission or compensation.

The ethical questions don’t stop there:

1. How do we regulate algorithms that discriminate?
2. Who is responsible when AI makes life-altering decisions?
3. Should AGI be developed without clear definitions or safeguards?

Extreme risks are warned of by experts such as Geoffrey Hinton and Yoshua Bengio. Some even say AGI, if left unchecked, could lead to human extinction. While that’s a fringe view, it underscores the urgency for proper oversight.

The Mechanical Turk of Our Time?

History offers warnings about tech that appears smarter than it is. In 1770, a machine called the Mechanical Turk amazed audiences by winning chess matches against Napoleon. But it was a hoax—operated by a hidden human inside. Today’s AI systems often pull off similar illusions, relying on human input while appearing autonomous.

So what’s really at stake? It’s not just about whether AGI exists. It’s about who benefits from the technology, who controls it, and how it shapes society.

The conversation around AGI isn’t just technical—it’s deeply human. AGI holds potential, but it also raises real concerns about power, equity, and truth. While some view it as the next revolution, others see a mirage shaped by hype and financial incentives.

Until there’s transparency, shared definitions, and global regulation, AGI remains more dream than destiny.