AI and quantum computing are not new Critical Skills. They are the most consequential new tools to which the Technology Skill must be applied.

I. A Gun Sight, a Terminator Line, and a Definition That Predicted Everything

It was April 1970, and three men were dying in space.

An oxygen tank had exploded aboard Apollo 13, crippling the service module upon which the command module depended. Power was failing. Carbon dioxide was building. Consumables designed to sustain two people for a day and a half now had to sustain three people for four days. And the whole world was watching on live television.

The most urgent problem came at reentry. The crew had to hit the earth’s atmosphere at precisely the right angle. Too steep and the command module would burn up. Too shallow and it would skip off the atmosphere like a stone on a pond — and keep going. Captain James A. Lovell, the mission commander, had no functioning computer guidance. What he had were two things: a gun sight mounted on the lunar module window, and the earth’s terminator line — the boundary between day and night visible from space.

In Lovell’s own words: “Knowing the structure of the lunar module I was aware that if I superimposed the horizontal line of my gun sight on the earth’s terminator I would place the lunar module engine in a position to increase the angle of entry and get the spacecraft back into the corridor.”

He selected the technology available. He applied judgment. He brought three men home alive.

What happened aboard Apollo 13 may be the most monumental demonstration of the Technology Skill in modern history — not because the technology was sophisticated, but precisely because it was not. Every Critical Skill fired simultaneously under mortal stakes with zero margin for error: precise Communication with Houston, the Production imperative to get the crew home, accurate Information-gathering from failing instruments, rigorous Analysis to determine burn length and approach angle, Interpersonal teamwork under mortal pressure, Time Management with no margin for error, and Technology — maximum use of whatever tools existed to solve the problem. The greatest engineering achievements of the twentieth century — the Manhattan Project, the moon landing itself — were feats of engineering. Apollo 13 was a feat of judgment. The technology was broken. The skill was what survived.

In 2015, in WANTED: Eight Critical Skills You Need To Succeed, I defined the Technology Skill this way:

The Technology Skill is simply the ability to select the appropriate technology that is most efficient and useful to accomplish a specific task.

That definition did not mention artificial intelligence. It did not mention quantum computing. It did not need to. Because the Technology Skill was never about any specific technology. It was about the judgment to choose.

AI is not a ninth Critical Skill. Quantum computing is not a tenth. Both are powerful new technologies — arguably the most consequential tools humanity has ever produced — to which the Technology Skill must be applied. The skill is the same. The landscape has changed beyond recognition.

What follows is the arc of that change: from the original definition through the AI explosion, across the quantum horizon, and into a practical framework for the judgment the modern world now demands.

II. The Technology Skill Was Always About Selection — Now Selection Has Exploded

The original chapter made a distinction that matters more now than it did then: the Technology Skill is not engineering expertise. It is not about building technology. It is about selecting the right tool for the task, understanding whether that tool is fit for purpose, and governing its use responsibly.

The examples in the book were straightforward. I shifted a school management system from FoxPro to Microsoft Access because Access offered better integration with Word, Excel, and PowerPoint. That was a Technology Skill decision — evaluating fitness for purpose, weighing compatibility, managing the transition. Earlier generations faced the same decision when typewriters gave way to word processors, when drawing instruments gave way to CAD software, when filing cabinets gave way to databases.

Each was a selection decision. The skill was always the same.

But the complexity of that selection has exploded. The original chapter dealt with choosing between two database platforms. Today the selection matrix includes cloud vendors with competing architectures, open-source models versus proprietary systems, embedded AI features that change quarterly, vendor training-data policies that determine what your tool has learned and from whom, latency and cost trade-offs that shift with every pricing update, and legal exposure that most organizations have not yet fully assessed.

The Technology Skill, in 2026, is more accurately described as Technology Judgment — the active, repeatable decision process the original chapter always taught, now expanded to include variables that did not exist a decade ago: model provenance, vendor trustworthiness, data-use terms, reliability metrics, and the cost of mistaken trust.

Selecting a technology has always been a judgment call. Today that judgment must include model provenance, vendor posture, and the cost of mistaken trust.

III. The AI Explosion: The Fastest Adoption in History

The original Technology chapter cited IBM’s Watson as the frontier of artificial intelligence. Watson beat Jeopardy! champions in 2011, assisted physicians in lung cancer treatment decisions, and served as an AI chef generating original recipes from available ingredients. These were impressive demonstrations of narrow AI — systems brilliant at a single task.

Then November 2022 happened.

OpenAI released ChatGPT, and the trajectory of technology adoption changed permanently. The platform reached one million users in five days, one hundred million in two months, and now serves more than 800 million users every week — with some estimates approaching 900 million by late 2025. The St. Louis Federal Reserve documented what this means in historical context: generative AI adoption hit 54.6 percent of U.S. adults ages 18–64 by August 2025, outpacing both the personal computer at 19.7 percent and the internet at 30.1 percent at equivalent points in their respective diffusion curves. Nothing in the history of consumer technology has seen broader adoption this fast.

The original chapter cited Moore’s Law — transistor density doubling every two years — and Kryder’s Law — storage capacity doubling every eighteen months — as measures of technological acceleration. AI capabilities are advancing faster still. Not every two years. Every few months. The landscape of available AI tools now includes ChatGPT, Claude, Gemini, Copilot, Perplexity, DeepSeek, and scores of industry-specific AI agents, each with different strengths, different limitations, different data policies, and different price points.

Which one do you use? For what task? Under what governance? With what verification?

This is the Technology Skill written at industrial scale.

The U.S. Department of Labor made the connection explicit on February 13, 2026, when it released its AI Literacy Framework. The framework defines five foundational content areas: understanding AI principles, exploring AI uses, directing AI effectively, evaluating AI outputs, and using AI responsibly. Read that list carefully. It is not about building AI. It is not about programming neural networks. It is about selecting, deploying, evaluating, and governing AI as a tool — the exact definition of the Technology Skill, codified by the federal government for the modern workforce.

IBM defines AI literacy as “the ability to comprehend various aspects of artificial intelligence — including its capabilities, limitations, and ethical considerations — and to use it for practical purposes.” The Connors Group offered an analogy that echoes the original chapter precisely: “Similar to Microsoft Office in the early 1990s, AI literacy is becoming the new minimum standard in the professional world.”

The progression is identical. Typewriters to word processors. FoxPro to Access. Microsoft Office as a baseline expectation. And now AI literacy as the new floor. The Technology Skill predicted every stage of this progression, because it was never about the tool. It was about the judgment to choose.

IV. AI Amplifies Every Critical Skill — and Threatens the Most Important One

AI does not operate in isolation from the other seven Critical Skills. It amplifies each of them. And it threatens at least one of them in ways that should alarm every educator, leader, and professional.

• Communication. AI drafts emails, translates languages, and generates content at superhuman speed. But the writers, speakers, and leaders who understand structure, audience, and purpose leverage AI far more effectively than those who simply accept its first output. The Communication Skill does not become less important in an AI world. It becomes the differentiator. AI raises the floor. The Communication Skill raises the ceiling.
• Production. AI compresses the cycle from idea to reality. PwC’s AI Jobs Barometer found that industries most exposed to AI show higher revenue-per-worker growth — not because workers are replaced, but because the cycle from concept to execution shortens dramatically. “Making it happen” now happens faster. But human curation and quality judgment define whether what happens is worth producing.
• Information. AI processes vast datasets and retrieves information with extraordinary reach. It also generates plausible-sounding falsehoods with complete confidence. These are called hallucinations, and they are not bugs — they are inherent to how large language models function. The book’s insistence that information must be “judged or proved to be true” has never been more urgent. The Information Skill in the AI age is triage plus verification. Trust nothing an AI produces without checking it.
• Analysis — and this is where the danger lives. A study by Gerlich published in the journal Societies involving 666 participants found a significant negative correlation between frequent AI use and critical thinking ability. The more people relied on AI to do their thinking, the weaker their critical thinking scores became. The study’s author cautioned that the findings are correlational rather than causal, but the pattern is striking and consistent across age groups. Anthropic’s AI Fluency Index, published in February 2026, reinforced the concern from a different angle: when AI produces polished artifacts such as code, documents, or interactive tools, users become measurably less likely to question the model’s reasoning, verify facts, or identify missing context. Gartner’s Daryl Plummer delivered the warning bluntly at the firm’s 2025 IT Symposium: “AI is stealing your skills.” Gartner predicts that through 2026, atrophy of critical-thinking skills due to generative AI use will push 50 percent of global organizations to require “AI-free” skills assessments.

The risk of cognitive offloading — letting AI think for you — is the antithesis of everything the Critical Skills represent.

The antidote is exactly what the book has always prescribed: active engagement with every output. Challenge it. Verify it. Require yourself to articulate the reasoning before you accept the conclusion. If you cannot explain how you arrived at an answer, the answer is not yours.

• Interpersonal. The MIT Sloan School of Management’s EPOCH framework identifies five capability groups that AI cannot replicate: Empathy, Presence, Opinion and Judgment, Creativity, and Hope. As AI absorbs routine tasks, the premium on human connection, trust-building, and relational intelligence rises. The Interpersonal Skill becomes more valuable, not less.
• Time Management. AI automates routine triage, schedules, and administrative tasks, freeing time for the work that matters. But it does not — and cannot — replace the judgment of which work matters. The original chapter argued that effective professionals focus on the critical four tasks out of ten. AI gives you more time for those four. It does not tell you which four they are.
• Technology. The Technology Skill now governs all the others. Every selection decision about AI tools — which model, which vendor, which data-use policy, which reliability threshold — is a Technology Judgment decision. Get it right and every other skill is amplified. Get it wrong and you are building on a foundation you do not understand.
• Continuous Education. The World Economic Forum estimates that 39 percent of key job skills will change by 2030. Generative AI enrollments on Coursera surged past 3.2 million in 2024 alone. The rate of change in AI capability means that what you learn today about a specific tool may be obsolete in six months. Continuous Education is no longer a professional aspiration. It is a survival imperative.

V. The Quantum Horizon: The Next Great Selection Challenge

While AI dominates the present conversation, a second technological revolution is approaching that will expand the Technology Skill’s scope yet again.

Quantum computing is not magic, and it is not science fiction. It is a fundamentally different class of computational power that exploits the principles of quantum mechanics — superposition, entanglement, and interference — to solve specific categories of problems that classical computers cannot approach in any practical timeframe. Those categories include molecular simulation, complex optimization, advanced sampling, and a particularly consequential one: breaking the encryption that protects virtually all modern digital security.

The milestones are no longer theoretical. In December 2024, Google’s Willow quantum chip achieved the first “below threshold” error correction — a benchmark in which the quantum processor solved a problem in under five minutes that would take the most powerful classical supercomputer longer than the age of the universe to complete. This is not incremental progress. This is a category change.

IBM’s public roadmap targets fault-tolerant quantum computing by 2029 with its Starling processor at 200 logical qubits, scaling to 2,000 logical qubits by 2033 or beyond with the Blue Jay architecture. Microsoft and Quantinuum have demonstrated advances in logical qubit reliability. The trajectory is clear, even if the exact timeline remains uncertain.

The conservative view — and this article maintains a 70/30 ratio of conservative to futuristic projections throughout — is that quantum computing’s near-term impact will be felt in specialized domains: drug discovery and molecular simulation, financial portfolio optimization, materials science, and cryptographic security. Quantum processing will be delivered as a cloud service, integrated into existing computational workflows. Most professionals will encounter it not as a laboratory curiosity but as an option in their technology selection matrix — another tool to evaluate, another fitness-for-purpose judgment to make.

The cybersecurity dimension demands immediate attention. Shor’s algorithm, running on a sufficiently powerful quantum computer, can break the RSA and elliptic curve encryption that protects banking transactions, medical records, government communications, and virtually every secure digital interaction in modern life. Nation-states are already engaged in “harvest now, decrypt later” strategies — collecting encrypted data today with the intention of decrypting it when quantum capability matures. NIST released its first set of post-quantum cryptography standards (FIPS 203, 204, and 205) in 2024. Organizations that have not begun migration planning are already behind.

For the Technology Skill, quantum computing adds another dimension to the selection decision. Not about understanding quantum physics — about knowing when quantum-enhanced solutions deliver genuine advantage over classical approaches, and when they do not. The judgment is the same. The variables are new.

VI. The 30% Future: When AI and Quantum Converge

What follows is the futuristic 30 percent — projections grounded in trajectories already in motion, but dependent on breakthroughs that are not yet certain. They are included because responsible Technology Judgment requires awareness of what is possible, not merely what is present.

The relationship between AI and quantum computing is bidirectional, and it feeds on itself. AI stabilizes quantum systems through neural-network error correction, improving the reliability of quantum processors. Quantum computing, in turn, offers native parallelism that could accelerate AI training and inference in ways classical hardware cannot match. Each makes the other more powerful.

The practical implications, if these trajectories mature, are staggering. Quantum processors could dramatically compress the training cycles of large language models while reducing energy consumption. Quantum-enhanced sensors guided by AI could advance molecular-scale imaging, detect earthquake precursors days in advance, or enable wearable brain-imaging technology that today exists only in research laboratories. Fusion energy optimization — a problem that is inherently quantum-mechanical in nature — could become tractable. Climate modeling at molecular resolution could move from aspiration to capability.

The most provocative possibility is recursive: AI is already designing better quantum hardware. Better quantum hardware could make AI more powerful. More powerful AI could design even better quantum systems. If this feedback loop materializes, it represents the most significant technological acceleration in human history — and the most consequential Technology Judgment challenge ever faced.

The physicist Seth Lloyd has calculated that the universe has performed approximately 10¹²⁰ quantum operations since the Big Bang. His provocation is worth sitting with: quantum computing may not merely be a faster calculator. It may be the native computational language of reality itself.

Even in this visionary scenario, the Technology Skill endures. In fact, it becomes more critical, not less. The skill shifts from selecting among tools you can fully understand to exercising judgment about when to trust — and when to question — outputs from systems more powerful than any single human mind can comprehend. That is not a diminishment of human agency. That is the highest expression of it.

VII. The Technology Judgment Playbook: Five Steps for the Modern Professional

Theory matters. Practice matters more. The following is a compact, repeatable decision framework that operationalizes the Technology Skill for the AI and quantum era. It applies whether you are selecting an AI writing assistant, evaluating a quantum-enhanced optimization service, or deciding that the right tool for the task is a pencil and a legal pad.

• Step 1: Define Success Criteria and Constraints. What does the task require in terms of accuracy, speed, cost, data sensitivity, and legal exposure? Begin here before you touch any tool. If you cannot articulate what success looks like, no technology can deliver it.
• Step 2: Map Candidate Tools and Vendor Posture. What are the options? Human judgment alone, classical software, a specific AI model, a hybrid approach, or — increasingly — a quantum-enhanced solution? For each candidate, assess the vendor’s training-data policies, data-use terms, reliability metrics, update cadence, and legal standing. The cheapest tool is not always the right tool. The most powerful tool is not always the right tool.
• Step 3: Pilot with Verification Gates. Run a small experiment first. Build in truth tests — check the output against known correct answers. Include independent verification from a second source or method. Never scale an unverified output. Never.
• Step 4: Govern and Document. Classify the data involved. Establish disclosure requirements. Maintain an audit log. If you cannot explain to a colleague, a regulator, or a court how you arrived at a result, the result is not trustworthy — regardless of how sophisticated the tool that produced it.
• Step 5: Scale and Educate. Train your team on the tool, the governance requirements, and the verification process. Monitor for drift — tools change, vendors update policies, models are retrained. Establish a sunset plan for tools that no longer serve. Technology Judgment is not a one-time decision. It is a discipline practiced continuously.

If you cannot explain how you arrived at a result, the result is not trustworthy — regardless of how sophisticated the tool that produced it.

VIII. The Definition That Predicted Everything

Return, for a moment, to April 1970.

Captain Lovell had a dead guidance computer, a gun sight, and the earth’s terminator line. The technology available to him was, by today’s standards, primitive beyond description. But the skill he exercised — selecting the right tool, applying it with judgment, verifying the result against the constraints of physics and survival — was timeless.

That same skill is what the modern professional needs now, when the available tools include artificial intelligence systems of staggering capability and quantum computing on the near horizon. The selection is more complex. The stakes, for most of us, are less immediately mortal but no less consequential for our careers, our organizations, and the society that depends on citizens who can think clearly about the tools they use.

The definition I wrote in 2015 anticipated all of this — not by naming AI or quantum computing, but by identifying the enduring human capacity that lies beneath every technology ever invented: the judgment to choose wisely.

AI will not replace you. But a person who knows how to use AI will.

This is the Technology Skill in a single sentence.

The Critical Skills have never been more in demand. Not just for your career, though they will define your career. Not just for your children, though they will determine what your children can achieve. For the future of a democratic society that depends — now more than ever — on citizens who can think, evaluate, verify, and choose.

Practice the Technology Skill today. Choose your tools deliberately. Teach verification to everyone you lead. Insist that your organizations treat AI readiness and quantum readiness not as quarterly initiatives but as multi-year commitments to human judgment.

The book’s own closing line from the Technology chapter remains the final word:

The complexity of the Technology Skill will only increase, given the astronomical pace at which technology itself is advancing.

It increased. And the skill still holds.