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Masters of Scale

Futurist Amy Webb: Insights from her 2024 Tech Trends Report | Masters of Scale

As the founder and CEO of the Future Today Institute, Amy Webb helps global companies and governments see what comes next.

Amy WebbguestJeff Bermanhost

Dec 13, 202433mWatch on YouTube

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CHAPTERS

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Chapter summaries

A Tokyo phone in 2002: early clues about the distributed internet
Amy Webb recounts living in Tokyo in the early 2000s and seeing how mobile technology in Japan and Korea outpaced the U.S. From that experience, she inferred a future where internet access and content would become more distributed and many-to-many.
- •She describes having an internet-connected phone with a camera years before that was common in the U.S.
- •She contrasts her experience with the more limited U.S. phones of the era.
- •She explains why falling component costs made widespread camera phones feel inevitable.
- •She connects mobile capabilities to a shift from one-to-many content to many-to-many sharing.
What it means to be a futurist, and why Amy didn’t start in foresight
Jeff Berman asks how someone becomes a futurist, and Amy explains that strategic foresight is an academic discipline but people come to it from many backgrounds. She shares her own academic focus and an early ambition aimed at shaping the future through law.
- •She notes there are formal courses in foresight and she teaches one at NYU Stern’s MBA program.
- •She says she studied game theory and economics rather than foresight in university.
- •She shares her childhood dream of becoming solicitor general to shape lasting legal precedent.
- •She frames her throughline as wanting to “shape the future,” even before she had the futurist title.
The LSAT panic attack and the pivot toward a different way to shape the future
Amy describes a pivotal moment during the LSAT when she had her first panic attack and felt her law-school path collapse. She explains how she "played everything forward" and decided to find a new avenue to do meaningful work shaping the future.
- •She recounts acing early sections of the LSAT before blacking out at the start of the third.
- •She explains why averaging scores made retaking feel futile in her situation.
- •She says she didn’t have the pedigree and felt there was “no point” continuing on that track.
- •She emphasizes the decision to seek another route to impactful, intellectually stimulating work.
Japan as plan B: learning the language, teaching, and finding journalism through reporting
Without a plan B, Amy took an opportunity to work in a small town in northern Japan. She describes learning Japanese, doing community-focused work, and beginning a writing and reporting career that eventually brought her back to grad school and journalism.
- •A professor connected her to work in a small Japanese town, where she became “the town foreigner.”
- •She taught middle school English, did service work, and spent time in town hall.
- •She learned Japanese after arriving.
- •She began stringing for major outlets and later worked as a data-driven journalist.
Founding the Future Today Institute: strategic foresight as a data-driven discipline
Amy explains why she left journalism—because it focused on what had already happened—and founded the Future Today Institute. She defines strategic foresight and describes the firm’s mission to help organizations plan for growth and anticipate disruption.
- •She says journalism didn’t let her “play things forward.”
- •She defines strategic foresight as a data-driven way of seeing the future for better strategic decisions.
- •She describes serving businesses and governments globally and focusing on both growth and risk.
- •She notes the company had no investors and she came from a modest background without family money.
Motivation matters: skepticism of “build to exit” thinking
In response to Jeff’s question about starting without resources, Amy pivots to founder motivation. She argues that prioritizing an “exit” leads to distorted decision-making rather than building “the one great thing that must be created.”
- •She says she meets many founders whose sole desire is to exit.
- •She argues exit-focused motivation pushes people toward constant pitching rather than product creation.
- •She calls it a “terrible way to go through life,” because it’s misaligned with making the best decisions.
- •Jeff frames the audience’s “cold start problem,” setting up her perspective on what matters most early.
The 2024 paper and the “technology super cycle”: three general purpose technologies
Amy introduces her new paper and reframes the AI conversation around a broader set of forces. She identifies three general purpose technologies—AI, advanced sensors, and biotechnology—and argues the real story is their convergence into a reinforcing flywheel.
- •She says many people are focusing on AI and GPT, but highlights “general purpose technology” as a different meaning of GPT.
- •She defines a general purpose technology as one with profound, lasting economic impact (using engines, electricity, and the internet as examples).
- •She names the three technologies: artificial intelligence, advanced sensor technology, and biotechnology.
- •She argues businesses often discount emerging developments because they feel far away, repeating the AI pattern.
Advanced sensors explained: from public data to behavioral data and “large action models”
Asked to explain sensors simply, Amy describes the sensor-rich world around us and why sensor data matters beyond public internet text. She explains how sensor data enables behavioral and real-world understanding, feeding systems that predict what people might do next.
- •She says phones and everyday environments contain millions of sensors.
- •She contrasts LLM training on public data with the need for behavioral and real-world data.
- •She says sensor data helps build a “large action model” that predicts future behavior.
- •She notes most sensor data is controlled by OEMs and technology companies rather than being broadly public.
Biotech explained: generative biology, new materials, and the chicken-and-gloves examples
Amy describes synthetic and generative biology as a kind of biological prompting: specify a structure or function and get a formula. She illustrates why this matters with examples ranging from nitrile gloves and supply chains to cultured meat and its economic implications.
- •She describes a “biological equivalent” to prompting, producing molecular formulas based on desired functions.
- •She emphasizes biotech’s relevance beyond health because it creates optionality for materials.
- •She uses nitrile gloves to show how new production methods could reduce supply chain fragility.
- •She describes growing meat from a small tissue sample in a bioreactor and explains why that could reshape local economies and trade dynamics.
Convergence in practice: flywheels between AI, sensors, and biotech
Amy ties the three technology domains together, arguing progress in one accelerates the others. She points to new tools becoming available and explains how sensors and AI can support advances like cultured meat and potentially new non-plastic materials.
- •She says the convergence—not siloed technologies—is what many people miss.
- •She notes new tools became available recently, enabling faster progress.
- •She describes an AI-for-biology model and connects it to advances in creating new materials and foods.
- •She frames the interplay as a “flywheel effect,” where improvements beget improvements across domains.
Everyday sensorization and contradictions as signals: cycling data, enhanced games, and health trends
Using herself as an example, Amy describes how consumer activities like cycling are now deeply instrumented with sensors and remote coaching. She introduces the proposed “enhanced games” as a contradiction that can act as a signal, and connects weight-loss drugs to AI-biotech convergence.
- •She explains how her bikes and devices collect data like heart rate and performance metrics, shared with a coach remotely.
- •She introduces the proposed “enhanced games,” where pushing biological limits is encouraged and tracked.
- •She says contradictions help her track signals and think about emerging trends.
- •She connects weight-loss drugs to the convergence of AI and biotechnology.
Making foresight actionable: signals vs. trends vs. uncertainties, and scenario mapping
Jeff asks how founders and leaders can turn her research into action, and Amy outlines core capabilities of foresight. She emphasizes disciplined tracking, distinguishing real trends from hype, acknowledging uncertainty, and mapping scenarios rather than relying on biased intuition.
- •She says awareness of the technology super cycle isn’t enough; organizations need foresight capabilities.
- •She distinguishes trends from what’s merely “trendy,” and warns ideas can be biased or shaped by recency effects.
- •She defines signals and trends as what can be known, and uncertainties as what cannot be controlled or known.
- •She describes using these inputs to map scenarios for decision-making and strategy.
Storytelling to create urgency: Herman Kahn, climate communication, and behavior
Amy argues that leaders stuck in incrementalism change when the future becomes immediately relevant and visceral. She uses Herman Kahn’s Cold War scenario storytelling and contrasts it with abstract climate metrics, emphasizing the need to translate data into relatable narratives that drive decisions.
- •She recounts Herman Kahn shifting from probabilities to vivid post-event stories that military leaders could grasp.
- •She critiques climate communication centered on small numbers like 1.5°C that feel disconnected from catastrophic outcomes.
- •She suggests telling climate stories through everyday interfaces, like what people see in a weather app.
- •She emphasizes urgency without implying organizations must drop everything, focusing on relevance to the “consumer” of the information.
Regulating AI and beyond: why traditional regulation struggles and an alternative approach
Jeff asks about government’s role in regulating dangerous or misused tech, and Amy argues heavy-handed regulation often produces lawsuits and lags behind reality. She explains why enforcement is hard amid uncertainty—especially with biology—and points to alternative mechanisms and cross-industry learning, including a data trust framework she previously proposed.
- •She says regulation is typically reactive and hard to keep aligned with fast-evolving technologies.
- •She argues regulations require enforcement, monitoring, and precision; too-broad language becomes contested.
- •She says biology can’t be geo-fenced and countries differ on what’s acceptable, with strong economic incentives at play.
- •She describes a prior framework involving data ownership and a trust model with incentives for participation and economic exclusion for non-participants, and she urges looking beyond direct peers for workable frameworks.
What she’s excited about: a personal race and the openness of the future
In closing, Amy shares a personal goal—racing Unbound—and a broader source of optimism. She emphasizes that the future isn’t fixed and frames the present moment as a reset where good strategic moves and thinking ahead can change outcomes.
- •She shares excitement about participating in Unbound and choosing the 50-mile distance for her first attempt.
- •She notes the race can be brutal and unsupported, and she may try longer distances in future years.
- •She says the year has been tough for many people, but the future is not set in stone.
- •She uses a chessboard metaphor—pieces shaken up—and says thinking ahead and making good moves can lead to better outcomes next year.

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