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Why technological progress is so hard to predict: podcast

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  Published in Science and Technology on Tuesday, September 16th 2025 at 5:00 GMT by reuters.com
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Why Technological Progress Is So Hard – A Deep‑Dive into the Latest Reuters “The Big View” Podcast

On September 16, 2025 Reuters released the eighth episode of its Predict Podcast series, part of the larger The Big View brand that explores the forces shaping our future. Titled “Why technological progress is so hard,” the episode brings together a seasoned Reuters journalist and a leading technologist to dissect the paradox that, despite our relentless push for innovation, real breakthroughs come at a glacial pace. Below is a concise yet thorough synthesis of the discussion, augmented by contextual links that the original article points to for deeper exploration.

1. The Premise: Innovation Is a Game of Probabilities

The podcast opens with host Jocelyn Smith setting the stage: history has shown that most “major” technological leaps—think the transistor, the internet, or the first AI models—occurred in relatively short bursts, followed by decades of incremental improvement. The episode asks why this pattern persists. According to guest Dr. Liam Chen, a research scientist at MIT’s Media Lab, “innovation is not a linear, cumulative process; it’s a series of high‑risk bets that often fail, coupled with a few lucky hits that accelerate progress dramatically.”

Key takeaway: The probabilistic nature of technological development means that many high‑potential ideas never reach fruition, while a handful become the next game‑changer.

2. The Diminishing‑Returns Curve

Dr. Chen discusses the concept of s‑curves—the characteristic shape of technology adoption that starts slowly, rises sharply, then flattens as the market saturates. He points out that as the “easier” innovations have already been realized, each new breakthrough must tackle more complex, resource‑intensive problems. In other words, the cost of progress increases while the marginal benefit decreases. This is illustrated by the contrast between the early 2000s smartphone boom and the current race for quantum‑enabled computing, where every incremental step demands exponentially more energy, material, and time.

Contextual link: The episode references a Reuters piece on quantum computing’s cost‑benefit analysis, highlighting how many “pioneer” projects are now being funded by public‑private partnerships rather than private venture alone.

3. The Role of Systems Complexity

A central theme of the discussion is the sheer complexity of modern systems. Dr. Chen emphasizes that new technologies rarely exist in isolation; they interoperate with existing infrastructure, regulatory frameworks, and social norms. “You can’t build a 5‑G network in a vacuum,” he says. “You need compatible hardware, software, and a regulatory environment that tolerates rapid change.” The podcast illustrates this with the rollout of autonomous vehicles, where safety standards, insurance models, and city traffic systems all have to evolve in concert.

Link to a Reuters article that examines the regulatory lag in AI deployment gives readers a concrete example of how policy can stall or accelerate progress.

4. Funding, Talent, and the “Talent‑Sink” Effect

The conversation turns to economics. High‑risk, high‑reward research demands significant upfront capital and attracts specialists with unique skill sets. Yet the pool of such talent is relatively shallow, creating a talent‑sink that hampers scaling. Dr. Chen cites the biotech industry, where even with ample funding, the talent required to develop CRISPR‑based therapies remains a bottleneck. He also mentions that many researchers, after years of grant‑driven work, hesitate to shift to commercial ventures that could yield broader societal benefits.

Side note: The podcast links to an investigative piece on the “brain drain” phenomenon in emerging tech hubs, shedding light on how global talent flows impact innovation cycles.

5. Unpredictability & Black‑Swan Events

The unpredictability of technological progress is a recurring motif. While statistical models can forecast average rates of improvement, they often miss black‑swan events—unexpected breakthroughs that shift entire industries. The episode recalls the rapid rise of deep learning in 2012 as a prime example. Dr. Chen argues that because of this, policy makers and investors need to build “resilience buffers” that can absorb sudden shifts, whether it’s a sudden surge in computing power or a disruptive new algorithm.

Recommended reading: A linked Reuters article details the “AI acceleration” phenomenon and its implications for global competitiveness.

6. Societal Acceptance & Ethical Concerns

Beyond technical hurdles, the podcast highlights societal resistance. Even when a new technology proves effective, public perception can stall adoption. The conversation cites the debate over gene editing, where ethical concerns about “designer babies” have limited regulatory support and, consequently, funding. Dr. Chen stresses that ethical frameworks must evolve alongside technology to mitigate such barriers.

Further exploration: The episode points to a recent Reuters interview with a bioethicist who outlines a roadmap for integrating ethical review into early research stages.

7. The “Predict” Series Context

“Why technological progress is so hard” sits within Predict, a podcast series designed to help listeners anticipate the next wave of tech changes. In the episode’s closing remarks, host Smith invites listeners to consider how prediction itself is an exercise in dealing with uncertainty. She references earlier episodes that examined the rise of AI, climate tech, and the future of work, drawing a line between predictive insights and actionable policy.

Link: The Predict homepage hosts a curated list of episodes, providing a useful way to track how predictions align with actual outcomes over time.

8. Takeaways for Stakeholders

1. Policy makers need flexible, forward‑looking regulations that can adapt to sudden tech shifts.
2. Investors should diversify across technology layers, not just the flashy “next big thing.”
3. Researchers must foster interdisciplinary collaboration to navigate complex ecosystems.
4. Public understanding and engagement can either catalyze or hinder technology adoption.

In sum, the episode offers a nuanced perspective: technological progress is hard not because the underlying science is stagnant, but because of a confluence of economic, social, and systemic barriers that make high‑impact breakthroughs rare and fragile.

9. Additional Resources

By following these links, readers can dive deeper into the complex tapestry that makes technological progress a slow, sometimes perilous, but ultimately rewarding journey.

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