( September 15, 2025, 10:44 GMT | Official Statement) — MLex Summary: The EU aims to enhance the prospects of digitalization and artificial intelligence in research, the European Commission said on Monday in a strategy for a stronger European research and technology ecosystem. Among the five actions, it also wants to ensure data access for researchers. The EU seeks to remain a global leader in research, innovation and critical technologies and is encouraging scientists to choose Europe, the commission said.Statement follows. …
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Ray Dalio calls for ‘redistribution policy’ when AI and humanoid robots start to benefit the top 1% to 10% more than everyone else
Legendary investor Ray Dalio, founder of Bridgewater Associates, has issued a stark warning regarding the future impact of artificial intelligence (AI) and humanoid robots, predicting a dramatic increase in wealth inequality that will necessitate a new “redistribution policy”. Dalio articulated his concerns, suggesting that these advanced technologies are poised to benefit the top 1% to 10% of the population significantly more than everyone else, potentially leading to profound societal challenges.
Speaking on “The Diary Of A CEO” podcast, Dalio described a future where humanoid robots, smarter than humans, and advanced AI systems, powered by trillions of dollars in investment, could render many current professions obsolete. He questioned the need for lawyers, accountants, and medical professionals if highly intelligent robots with PhD-level knowledge become commonplace, stating, “we will not need a lot of those jobs.” This technological leap, while promising “great advances,” also carries the potential for “great conflicts.”
He predicted “a limited number of winners and a bunch of losers,” with the likely result being much greater polarity. With the top 1% to 10% “benefiting a lot,” he foresees that being a dividing force. He described the current business climate on AI and robotics as a “crazy boom,” but the question that’s really on his mind is: why would you need even a highly skilled professional if there’s a “humanoid robot that is smarter than all of us and has a PhD and everything.” Perhaps surprisingly, the founder of the biggest hedge fund in history suggested that redistribution will be sorely needed.
Five big forces
“There certainly needs to be a redistribution policy,” Dalio told host Steven Bartlett, without directly mentioning universal basic income. He clarified that this will have to more than “just a redistribution of money policy because uselessness and money may not be a great combination.” In other words, if you redistribute money but don’t think about how to put people to work, that could have negative effects in a world of autonomous agents. The ultimate takeaway, Dalio said, is “that has to be figured out, and the question is whether we’re too fragmented to figure that out.”
Dalio’s remarks echo those of computer science professor Roman Yampolskiy, who sees AI creating up to 80 hours of free time per week for most people. But AI is also showing clear signs of shrinking the jobs market for recent grads, with one study seeing a 13% drop in AI-exposed jobs since 2022. Major revisions from the Bureau of Labor Statistics show that AI has begun “automating away tech jobs,” an economist said in a statement to Fortune in early September.
Dalio said he views this technological acceleration as the fifth of five “big forces” that create an approximate 80-year cycle throughout history. He explained that human inventiveness, particularly with new technologies, has consistently raised living standards over time. However, when people don’t believe the system works for them, he said, internal conflicts and “wars between the left and the right” can erupt. Both the U.S. and UK are currently experiencing these kinds of wealth and values gaps, he said, leading to internal conflict and a questioning of democratic systems.
Drawing on his extensive study of history, which spans 500 years and covers the rise and fall of empires, Dalio sees a historical precedent for such transformative shifts. He likened the current era to previous evolutions, from the agricultural age, where people were treated “essentially like oxen,” to the industrial revolutions where machines replaced physical labor. He said he’s concerned about a similar thing with mental labor, as “our best thinking may be totally replaced.” Dalio highlighted that throughout history, “intelligence matters more than anything” as it attracts investment and drives power.
Pessimistic outlook
Despite the “crazy boom” in AI and robotics, Dalio’s outlook on the future of major powers like the UK and U.S. was not optimistic, citing high debt, internal conflict, and geopolitical factors, in addition to a lack of innovative culture and capital markets in some regions. While personally “excited” by the potential of these technologies, Dalio’s ultimate concern rests on “human nature”. He questions whether people can “rise above this” to prioritize the “collective good” and foster “win-win relationships,” or if greed and power hunger will prevail, exacerbating existing geopolitical tensions.
Not all market watchers see a crazy boom as such a good thing. Even OpenAI CEO Sam Alman himself has said it resembles a “bubble” in some respects. Goldman Sachs has calculated that a bubble popping could wipe out up to 20% of the S&P 500’s valuation. And some long-time critics of the current AI landscape, such as Gary Marcus, disagree with Dalio entirely, arguing that the bubble is due to pop because the AI technology currently on the market is too error-prone to be relied upon, and therefore can’t be scaled away. Stanford computer science professor Jure Leskovec told Fortune that AI is a powerful but imperfect tool and it’s boosting “human expertise” in his classroom, including the hand-written and hand-graded exams that he’s using to really test his students’ knowledge.
For this story, Fortune used generative AI to help with an initial draft. An editor verified the accuracy of the information before publishing.
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To realize AI’s full potential, organizations must be in it for the long game; a pursuit that requires patience, persistence, and strategic alignment. While quick wins are important, they won’t stand alone in delivering meaningful value; agile experimentation is a necessity, execution requires iteration, and early challenges are inevitable.
Protiviti’s inaugural global AI Pulse Survey highlights a compelling correlation between AI maturity and return on investment (ROI) as well as a disconnect between expectations and performance for many organizations in the early stages of AI adoption. The survey, which had more than 1,000 respondents, categorizes organizations from more than a dozen industry sectors into five maturity stages:
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Stage 1: Initial — Recognizing AI’s potential but lacking strategic initiatives.
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Stage 2: Experimentation — Running small-scale pilots to assess feasibility.
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Stage 3: Defined — Integrating AI into business processes.
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Stage 4: Optimization — Enhancing performance and scalability with data feedback.
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Stage 5: Transformation — AI drives significant business transformation.
Expectations from AI Investments
As organizations progress through these stages, their satisfaction with AI investments improves. In fact, of the 50% of survey respondents who indicated that they are in the early stages (initial or experimentation) of AI adoption, about 26% reported that AI investment returns fell below expectations.
Of course, not all AI experimenters are experiencing poor returns. Indeed, a majority report ROI meeting expectations, but the results showed a higher concentration of slightly exceeded or significantly exceeded ROI expectations among groups in the middle to advanced stages of AI adoption.
In reviewing what differentiates successful experimenters — those in the experimentation stage of AI adoption who reported exceeding ROI expectations — from those that did not, we find three compelling attributes:
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Focus on balanced key performance indicators (KPIs) and measuring success using a mix of financial and operational indicators, such as employee productivity, cost savings and revenue growth;
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Report fewer challenges with skills and integration, as they tend to invest in training, upskilling and cross-functional collaboration;
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Seek diverse support, including strategic planning assistance and data management tools, not just training.
One more thing: These successful experimenters also emphasized financial and operational outcomes more evenly, while others focused more narrowly on cost savings.
Challenges AI Experimenters Face
Many AI experimenters are struggling not because of unrealistic expectations, but more likely due to unclear objectives or misunderstood value potential. This challenge and difficulties with integrating AI into existing systems are the two biggest hurdles faced by organizations in the early stages of adoption (stages 1 and 2).
Integration issues peak in the middle stages of AI adoption, but they begin in the early stages. Interestingly, the challenge related to understanding the most impactful use cases is most acute in the earliest stage, dips in the middle stages, and resurfaces even at the highest levels of maturity, albeit for different reasons.
The AI experimenters, of course, are unsure how to apply AI strategically and technical compatibility remains a hurdle, unlike the more mature companies. Compounding these issues are unclear or conflicting regulatory guidance and difficulties with data availability and access, a foundational issue for effective AI deployment.
It is the lack of structured approaches, unclear project objectives, and unreliable data that often lead to underwhelming ROI for these companies in the early stages.
Redefining AI Success
In another interesting finding from the survey, we see that as organizations progress to stages 3 to 5, their success metrics evolve from cost savings and process efficiency to revenue growth, customer satisfaction and innovation.
The good news is that organizations starting out on their AI journey can course-correct by focusing on these success metrics. It starts with redefining AI success, which means moving beyond short-term wins to sustainable transformation.
Having a clear understanding of what you’re trying to accomplish with AI is critical from the outset. Without clarity on what AI is meant to achieve, and how value will be measured, they will struggle to unlock its full potential.
Early experimenters should seek to build a solid foundation by:
Asking Why? Why are you adopting AI? What specific problems are you solving?
Investing in data infrastructure is critical. This step should involve auditing existing data systems and implementing robust data governance frameworks. Organizations will be well served in considering cloud-based platforms for scalability.
Developing a robust integration strategy early. Many existing systems were not originally designed to support AI. To overcome this deficiency, organizations should be proactive in assessing and modernizing infrastructure to handle AI workloads in the initial phases. They are likely to find greater success if IT, data and business teams collaborate and there’s shared ownership of AI initiatives to ensure alignment and adoption.
Aligning AI strategies with business objectives and organizational culture: This is not just a technical step. It involves ensuring organizational readiness and managing cultural and operational changes effectively.
Turning AI Trials into ROI Triumphs
The research is clear: there’s tremendous ROI potential for early-stage companies that can test, learn and scale AI use cases swiftly. Yet, while speed is crucial to capturing value, it’s important to recognize that AI experimentation is ongoing, requiring continuous iteration.
To win, think big, act swiftly, and continuously evolve — never stop.
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