By Futurist Thomas Frey
In 2012, I wrote about the eight critical value points of being a futurist. I explained how futurists provide unique value through pattern recognition, trend analysis, cross-disciplinary synthesis, and the ability to see connections others miss. It took years to develop these skills. They were rare, valuable, and in demand.
Now, in 2025, I can ask Claude or ChatGPT to analyze emerging trends, identify patterns across disparate domains, and generate plausible future scenarios—and get sophisticated answers in seconds rather than the weeks it would take me to research and synthesize the same information.
This isn’t just humbling. It’s existential. If AI can do in seconds what took me decades to learn, what’s the value of being a futurist anymore?
And here’s the uncomfortable truth: I’m not alone. Almost every knowledge profession is facing the same reckoning.
The Great Skills Obsolescence
Radiologists spent years learning to identify patterns in medical images. AI now matches or exceeds their diagnostic accuracy in many areas, analyzing scans in seconds.
Legal researchers built careers on the ability to find relevant case law, precedents, and contractual language. AI legal assistants now do comprehensive legal research instantly, finding connections human researchers would miss.
Financial analysts prided themselves on spotting market patterns and making investment recommendations. AI trading systems process more data in microseconds than analysts could review in lifetimes.
Translators spent decades mastering languages and cultural nuance. AI translation is approaching human quality for many language pairs, getting better daily, and costs essentially nothing.
Graphic designers developed aesthetic sensibilities through years of practice. AI image generators now produce professional-quality designs from text prompts in seconds.
Software developers built careers on coding expertise. AI coding assistants now generate functional code from natural language descriptions, debug errors, and suggest optimizations.
Accountants offered value through meticulous record-keeping and tax optimization. AI accounting systems now handle bookkeeping, catch errors humans miss, and identify optimization opportunities automatically.
The pattern is clear and accelerating: any skill based primarily on information processing, pattern recognition, or rule application is becoming AI-augmented at minimum, AI-replaceable at worst.
What Made Futurists Valuable (Past Tense)
Looking back at my 2012 analysis, here’s what made futurists valuable then—and how AI has changed each advantage:
Pattern Recognition Across Domains: Futurists spotted connections between seemingly unrelated fields. AI now processes information from millions of sources simultaneously, identifying patterns humans would never see.
Trend Extrapolation: Futurists projected current trends into plausible futures. AI now runs millions of simulations, modeling complex interactions and feedback loops far beyond human capacity.
Weak Signal Detection: Futurists prided themselves on spotting emerging trends before they became obvious. AI monitoring systems now track millions of data streams in real-time, identifying anomalies and weak signals continuously.
Cross-Disciplinary Synthesis: Futurists brought together insights from technology, sociology, economics, and culture. AI has instant access to the entire corpus of human knowledge and can synthesize across domains effortlessly.
Scenario Planning: Futurists developed multiple plausible futures to help organizations prepare. AI now generates thousands of scenarios, stress-tests them against various conditions, and identifies critical decision points.
Systems Thinking: Futurists understood second and third-order effects. AI models complex systems with feedback loops, emergent properties, and cascade effects that exceed human cognitive capacity.
Provocative Questions: Futurists asked questions that challenged assumptions. AI generates novel questions by exploring conceptual spaces humans wouldn’t naturally investigate.
Every single advantage I claimed in 2012 has been substantially eroded by AI capabilities in 2025. So what’s left?
The Skills That Are Disappearing
Let’s be brutally honest about what’s becoming obsolete:
Pure Information Retrieval: Knowing where to find information was valuable. Now it’s trivial.
Calculation and Analysis: Being good with numbers, statistics, and quantitative analysis was differentiating. AI is better.
Language Translation: Speaking multiple languages was prestigious and practical. AI translation is approaching human quality.
Routine Creative Work: Generating variations on established formats—marketing copy, basic designs, standard reports—is increasingly AI territory.
Technical Coding: Writing code to specification is being automated rapidly. Junior developers doing routine programming face displacement.
Diagnostic Pattern Recognition: Medical diagnosis, legal case analysis, financial pattern spotting—AI matches or exceeds human performance.
Data Entry and Processing: This is already gone, but it bears mentioning—any job primarily about moving information from one format to another is obsolete.
Optimization Within Constraints: Finding the best solution given rules and constraints—scheduling, routing, resource allocation—AI does better and faster.
These aren’t obscure skills. These are foundational capabilities that millions of careers were built on. And they’re evaporating as economic differentiators.
What Skills Remain Valuable (For Now)
The question that keeps me up at night: what skills can we stake our future on?
Judgment in Ambiguous Situations: AI struggles when goals are unclear, stakes are high, and values conflict. Deciding what to do when there’s no clear right answer—that’s still human territory.
Relationship Building and Trust: AI can’t replace the trust built through shared experience, vulnerability, and human connection. People don’t trust AI the way they trust other humans (yet).
Physical Improvisation: Robots are improving, but humans still excel at navigating unpredictable physical environments and handling novel situations requiring dexterity.
Ethical Reasoning in Novel Contexts: When facing truly new ethical dilemmas—not just applying existing frameworks—humans still outperform AI in considering stakeholder perspectives and long-term consequences.
Motivation and Inspiration: AI can inform, but humans inspire. The ability to make people care, to create emotional resonance, to motivate action—that’s still distinctly human.
Taste and Curation: AI generates options. Humans decide what’s actually good, what resonates culturally, what matters. Curation and taste remain human domains.
Strategic Risk-Taking: Deciding when to bet the company, when to ignore expert advice, when to go against data—calculated risk-taking with incomplete information remains human.
Asking the Right Questions: Not just any questions, but the questions that actually matter, that reframe problems, that open new possibilities. Question architecture is still human-led.
But here’s the uncomfortable caveat: “for now.” These advantages are temporary. AI is improving in all these areas. The question isn’t whether AI will eventually excel at these too, but when—and what comes after.
The New Skill Sets
If traditional skills are obsolete and current advantages are temporary, what should we stake our future on?
AI Orchestration: The ability to coordinate multiple AI systems, knowing which tools to apply to which problems, and synthesizing outputs into coherent strategies. This is like being a conductor rather than a musician.
Human-AI Collaboration Design: Creating workflows where humans and AI complement each other optimally, knowing when to trust the AI and when to override it.
Ethical Framework Development: As AI makes more decisions, someone needs to define the values and priorities it should optimize for. This is philosophical and political work, not technical.
Contextual Translation: AI outputs often need translation into forms specific stakeholders can understand and act on. Being the bridge between AI capability and human need is valuable.
Authenticity Curation: In a world flooded with AI-generated content, the ability to recognize and validate authentic human experience becomes premium.
Meta-Learning: Not learning specific skills, but learning how to learn rapidly, how to identify what’s worth learning, and how to abandon obsolete knowledge quickly.
Complex Systems Navigation: Not predicting the future (AI does that), but helping people navigate ambiguity, make decisions with incomplete information, and adapt as conditions change.
Purpose and Meaning Creation: AI optimizes for defined goals. Humans still need to figure out what goals are worth pursuing, what constitutes a life well-lived, what we’re actually trying to accomplish.
The Uncomfortable Truth About My Own Profession
So where does this leave futurists?
The honest answer: I’m not sure. The skills that made me valuable in 2012 are substantially less differentiating in 2025. AI can analyze trends, spot patterns, generate scenarios, and identify weak signals faster and more comprehensively than I can.
What I can still do—for now—is something squishier and harder to define: I can help people think about what questions they should be asking. I can challenge assumptions they didn’t know they were making. I can provide human judgment about which AI-generated scenarios actually matter. I can build trust through years of being right (and wrong) in public. I can motivate organizations to act on insights rather than just consuming them.
But am I worth what I used to be worth? Probably not. The market will decide.
And that’s the brutal reality facing every knowledge worker: the skills that justified our salaries, our status, our careers—they’re becoming less scarce, less valuable, less defensible.
Final Thoughts
The question isn’t whether AI will make traditional skills obsolete. It already has, for many professions. The question is what we do next.
Some will fight to preserve the old advantages—defending professional gatekeeping, regulatory barriers, and credentialism. This is a delaying action at best.
Some will partner with AI—becoming orchestrators, curators, and translators rather than primary producers. This seems more viable, at least transitionally.
Some will retreat to the remaining human advantages—relationship building, physical presence, ethical reasoning—until AI comes for those too.
And some will build entirely new value propositions we can’t yet articulate, based on capabilities we don’t yet recognize as valuable.
I don’t know which path leads to sustainable careers in an AI-dominated world. I suspect different people will succeed through different strategies.
But I do know this: the skills that made us valuable in the past won’t be enough for the future. The question facing every knowledge worker—including this futurist—is what we’re going to stake our future on when the old advantages evaporate.
I’ll let you know when I figure it out. Assuming AI doesn’t figure it out first.
Related Stories:
https://futuristspeaker.com/business-trends/eight-critical-value-points-of-a-futurist/
