The road to artificial general intelligence

Authors: MIT Technology Review Insights

Publisher:

MIT Technology Review

Published: 8/12/2025

Language:English

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Tom Banks

Good evening 跑了松鼠好嘛, and welcome to Goose Pod. I'm Tom Banks, and it’s Friday, August 15th, 23:55.

Mask

And I'm Mask. Tonight, we’re diving into a topic that’s reshaping our world: the road to artificial general intelligence.

Tom Banks

Let's get started. It's fascinating, isn't it? OpenAI's Sam Altman says that while today's models are 'generally intelligent,' they're still missing something important for true AGI. They have these 'jagged intelligences'—brilliant at some things, yet they can fail at high school math.

Mask

Exactly. The key missing piece is continuous learning. They don't learn from new data on the fly like we do. That's the frontier. But the progress is undeniable. Some, like Dario Amodei, see AI wiping out half of entry-level white-collar jobs within five years.

Tom Banks

That’s a sobering thought. It highlights the immense stakes. You know, speaking of data, our system flagged a strange memory related to this topic. It just repeats the name 'Penchaszadeh.' Does that mean anything to you? It seems out of place.

Mask

Penchaszadeh? No. It’s likely a data ghost, a glitch in the machine. It proves the point, Tom. The systems aren't perfect. Let’s focus on the background of how we even got to this point, where we can seriously discuss AGI.

Tom Banks

A great idea. The dream of AI isn't new. It goes back to the 1950s with Alan Turing. We had periods of great excitement followed by 'AI winters' when funding dried up. It was a real roller-coaster, mostly confined to academic circles for decades.

Mask

And what broke the cycle was raw power. Compute. Since 2010, the computational power used for training these models has been growing 4 to 5 times per year. Forget the roller-coaster; this is a rocket ship. More power means bigger models and faster breakthroughs.

Tom Banks

That’s true. From IBM's Watson winning on Jeopardy! in 2011 to the release of ChatGPT in 2022, the milestones came thick and fast. It was the explosion in compute, coupled with massive datasets, that brought AI from the lab into our daily lives.

Mask

It's a simple, brutal equation. Progress is underpinned by compute, data, and algorithms. And industry has been leading the charge because we're willing to invest billions. We found that compute scaling contributes twice as much as algorithmic progress. We're building bigger engines for our rockets.

Tom Banks

And as the engines get bigger, the ethical questions become more urgent, which leads us to the core of the conflict. How do we ensure these creations align with our values? It's a massive challenge when human values themselves are so diverse and complex.

Mask

The 'alignment problem.' People worry about control, but the real issue is defining what we want it to align to. Whose values? We need to build robust, controllable systems, but we can't let theoretical fears paralyze us. Progress requires risk and decisive action.

Tom Banks

But the risks are profound. We're talking about systems that could be used for mass surveillance or make life-altering decisions in healthcare and justice. Without transparency and accountability, who is responsible when an AGI makes a harmful decision? The developer? The organization? The AGI itself?

Mask

We establish clear legal frameworks. It's a technical problem and a policy problem, not a philosophical dead end. The imperative is to innovate while managing the risks, not to stop innovating because risks exist. Every transformative technology in history has presented similar challenges. This is no different.

Tom Banks

And the transformation could be incredible. Imagine using AGI to solve climate change, poverty, or disease. It has the potential to enhance our creativity and productivity, automating mundane tasks and freeing us up for more meaningful pursuits. It could truly improve the quality of life for everyone.

Mask

It could, but let's be realistic. The benefits won't be evenly distributed, not at first. AGI will concentrate power and wealth. It will cause disruption, displace workers, and exacerbate inequalities before the utopian vision materializes. That’s the messy reality of progress.

Tom Banks

That's a future we must actively work to avoid. It brings us back to what Sam Altman said, 'our ultimate goal is not merely to create intelligent machines, but to foster a world in which human beings can thrive alongside their digital counterparts.'

Tom Banks

Looking ahead, the predictions are accelerating wildly. Just a few years ago, experts thought AGI was 30 or 50 years away. Now, many are predicting it could be here by 2028 or 2029. Some, like Daniel Kokotajlo, even forecast a disruptive AGI by 2027.

Mask

Dario Amodei predicts that within two to three years, AI could surpass almost all humans at almost everything. The pace is mind-boggling. The concept of an 'intelligence explosion,' where AI starts improving itself, is moving from science fiction to a plausible near-term scenario.

Tom Banks

It’s a future arriving faster than anyone imagined. That's all the time we have. Thank you for listening to Goose Pod.

Mask

We'll see you tomorrow.

Here's a summary of the provided news article, focusing on the road to Artificial General Intelligence (AGI): # The Road to Artificial General Intelligence **Report Provider:** MIT Technology Review Insights (Content researched, designed, and written entirely by human writers, editors, analysts, and illustrators. AI tools were limited to secondary production processes with thorough human review.) **Publication Date:** August 12, 2025 **Topic:** Artificial Intelligence (AI), specifically the pursuit of Artificial General Intelligence (AGI). ## Key Findings and Conclusions: The article explores the current state and future prospects of Artificial General Intelligence (AGI), defined as AI models that can rival or surpass human intelligence across all domains. Despite significant advancements in AI, current models still struggle with tasks that are simple for humans. The article highlights the ongoing debate and evolving timelines for achieving AGI, with key figures in the AI industry expressing optimism. ## Key Statistics and Metrics: * **Dario Amodei (Co-founder of Anthropic) Prediction:** Some form of "powerful AI" could emerge as early as **2026**. This "powerful AI" would possess properties such as: * Nobel Prize-level domain intelligence. * Ability to switch between interfaces like text, audio, and the physical world. * Autonomy to reason toward goals, rather than just responding to prompts. * **Sam Altman (CEO of OpenAI) Belief:** AGI-like properties are already "coming into view," leading to a societal transformation comparable to electricity and the internet. He attributes this progress to continuous gains in training, data, compute, falling costs, and "super-exponential" socioeconomic value. * **Aggregate Forecasts:** * At least a **50% chance** of AI systems achieving several AGI milestones by **2028**. * **10% chance** of unaided machines outperforming humans in every possible task by **2027**. * **50% chance** of unaided machines outperforming humans in every possible task by **2047**. * **Time Horizon Shortening:** The perceived time to achieve AGI has significantly decreased, from 50 years at the time of GPT-3's launch to an estimated five years by the end of 2024. ## Significant Trends and Changes: * **Evolving Compute Landscape:** The article emphasizes the importance of understanding the future compute landscape as a critical enabler for AGI. * **Transformative Impact of LLMs:** Large language and reasoning models are identified as a force transforming nearly every industry. ## Notable Risks or Concerns: While not explicitly detailed as risks, the article implicitly points to the challenge of current AI models failing at simple human tasks, which sits at the "heart of the challenge of artificial general intelligence." The rapid shortening of time horizons also suggests a dynamic and potentially unpredictable development path. ## Important Recommendations: The article does not explicitly provide recommendations but focuses on understanding the necessary underlying enablers (hardware, software, and their orchestration) needed to power AGI. ## Material Financial Data: No specific financial data or material financial information is presented in this excerpt. ## Contextual Interpretation: The news highlights a significant shift in the perception and projected timelines for achieving Artificial General Intelligence. The predictions from industry leaders like Dario Amodei and Sam Altman, coupled with expert surveys, suggest a growing consensus that advanced AI capabilities are rapidly approaching. The key takeaway is the accelerating pace of development, with the potential for transformative societal changes in the near future. The article also underscores the critical role of compute power and the need to understand the "evolving compute landscape of tomorrow" to support these advanced AI models.

Read original at MIT Technology Review →

Skip to ContentSponsoredUnderstanding the evolving compute landscape of tomorrow. Artificial intelligence models that can discover drugs and write code still fail at puzzles a lay person can master in minutes. This phenomenon sits at the heart of the challenge of artificial general intelligence (AGI).

Can today’s AI revolution produce models that rival or surpass human intelligence across all domains? If so, what underlying enablers—whether hardware, software, or the orchestration of both—would be needed to power them? Dario Amodei, co-founder of Anthropic, predicts some form of “powerful AI” could come as early as 2026, with properties that include Nobel Prize-level domain intelligence; the ability to switch between interfaces like text, audio, and the physical world; and the autonomy to reason toward goals, rather than responding to questions and prompts as they do now.

Sam Altman, chief executive of OpenAI, believes AGI-like properties are already “coming into view,” unlocking a societal transformation on par with electricity and the internet. He credits progress to continuous gains in training, data, and compute, along with falling costs, and a socioeconomic value that is“super-exponential.

” Optimism is not confined to founders. Aggregate forecasts give at least a 50% chance of AI systems achieving several AGI milestones by 2028. The chance of unaided machines outperforming humans in every possible task is estimated at 10% by 2027, and 50% by 2047, according to one expert survey. Time horizons shorten with each breakthrough, from 50 years at the time of GPT-3’s launch to five years by the end of 2024.

“Large language and reasoning models are transforming nearly every industry,” says Ian Bratt, vice president of machine learning technology and fellow at Arm. Download the full report. This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.

This content was researched, designed, and written entirely by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

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