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At the Fortune AIQ Summit, futurist Amy Webb compared companies’ dependence on AI tools to New York City taxi drivers relying on navigation apps. Her point was not that a study has proved workers are losing skills: it was a warning that adopting tools without building the judgment and organizational capability to use them can leave a business dependent rather than more capable.
What Webb means by “learned helplessness”
Webb, founder and CEO of Future Today Strategy Group, described passengers handing a cab driver a phone and asking the driver to enter an address. She estimated that this had become a regular New York City interaction for about two years and said drivers had “a certain amount of learned helplessness” because of reliance on navigation. The example is Webb’s analogy, not a measured finding about taxi drivers or proof that navigation apps cause a loss of skill. Fortune reported her remarks at its October 1, 2026 summit.
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Webb used the comparison to question whether companies are developing the ability to assess and direct AI—or simply becoming reliant on whatever tools they have adopted. Buying access or generating more output, by itself, does not establish that a business has a strategy or is doing better work.
Why Webb says corporate AI efforts can stall
Webb said large companies are late to AI and are committing substantial capital to pilots without deciding in advance how those experiments will advance the business. Her account described several ways those efforts can go wrong:
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- Projects lose a path forward. A pilot may become orphaned after security disputes, rather than being tested against a defined business goal.
- Expertise can leave the company. Webb said some firms outsource development of technology that later proves proprietary, leaving them stuck. The implication is not that every project should be built internally, but that leaders should recognize which capabilities are strategically important.
- Employees route around the process. When formal approval or infrastructure is unavailable, frustrated staff may build tools themselves. That can create security and oversight problems as well as potential value.
Webb said she had not yet seen large companies establish the mechanisms needed to adapt, while many CEOs were still trying to understand AI. She also said, “Artificial intelligence didn’t just show up a couple of years ago.” These are her observations from a summit conversation, not independently quantified findings about all Fortune 500 companies. Fortune’s report does not cite a survey or dataset establishing how many firms lack an AI strategy.
What a more deliberate approach looks like
Webb’s prescription was flexibility supported by mechanisms: organizations should be able to adapt, but experimentation needs a route through security, leadership, and planning. In practice, that means treating a pilot as a business decision rather than a technology demonstration.
- Start with the business problem. State what should improve and how the company will recognize improvement before funding a pilot.
- Decide what must remain under company control. Distinguish work that creates a proprietary advantage from supporting services that are sensible to buy.
- Provide a secure way to experiment. Give employees an approved route to test ideas, with security and risk staff involved early enough to shape that route.
- Evaluate the work, not the activity. Assess whether the result is accurate, useful, and tied to the intended outcome—not merely whether staff used AI or produced more material.
These steps are a practical reading of the tensions Webb and the other panelists described; Fortune’s account does not present a standardized framework or scoring method.
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Runway’s example—and what it does not prove
Runway COO Michelle Kwon described AI use across the company and said its entire staff writes code. She said employees, including people outside engineering, had built about 215 apps for an internal app store since earlier in 2026. She also said a worker built a new autonomous advertising agent in “a handful of weeks.”
Kwon said the agent raised advertising output by more than 1,000% from a very small base and was being released publicly. Fortune’s account supplies no baseline, measurement method, or independent audit for that figure, so it should be understood as Kwon’s reported claim about one project—not as a general estimate of AI’s effect on productivity.
Runway’s approach also was not “build everything yourself.” Kwon said the company buys services for work that is not central to its business, such as payment systems and HR compliance software. The distinction is between developing internal capability where it matters and purchasing supporting services where that makes sense; the panel report offers no controlled comparison between Runway and Fortune 500 companies.
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More AI output is not automatically more productive work
Kwon cautioned that using AI is not proof of productivity or quality. “Just because you are using AI, that is not a proxy for your productivity or you doing a good job at work,” she said. She objected to receiving unprocessed chatbot output or a large slide deck without a clear indication of what action the recipient should take. As she put it, “It’s not helpful if I receive a 100-slide deck.”
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Peloton CTO Francis Shanahan said workers can be inundated because they can build so much, and that he was seeing more burnout. Those remarks describe what the panelists said they were observing; the report does not quantify workforce-wide effects. For employees and managers, the practical test is whether an AI-assisted result has been checked, synthesized, and made useful to its audience—not how quickly or abundantly it was produced.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Security, leadership, and the cost of improvisation
Webb recounted a friend at an unnamed large company who, after a CTO refused to approve a secure sandbox, created an instance, obtained supercomputer access, and assembled a team. Webb said the team planned to spend “a couple hundred million dollars” on AI tokens. The company is unidentified, and the figure is a planned spend in Webb’s account, not a verified expenditure or a representative corporate budget. She connected the story to weak leadership and planning, as well as “fear and FOMO.”
Her advice to risk and security officers was to ask “Tell me more” before saying no, and for leaders to give them some leeway. That is not a case for skipping controls: the episode illustrates how a blanket refusal can push work outside an approved process, while ungoverned experimentation creates its own risks.
Webb also warned that more connected listening devices could arrive over the next 18 months, but Fortune’s report did not supply a product list or an independent basis for that forecast. In a discussion of AI agents and liability, she described a coding error that sent spam to 1,000 people after a loop was placed in a live environment. She used the anecdote to argue that ordinary human error should not be described as AI “waking up”; the incident is her example, not a general statistic about AI systems.
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How to read the claims
Fortune’s October 2, 2026 story reports a panel conversation held the previous day. The claims about corporate pilots, Runway’s apps and advertising output, the planned token spend, and the taxi analogy are attributed to the speakers. They illuminate the risks and choices those speakers see, but they do not establish how prevalent those problems are across the Fortune 500 or prove that one company’s approach produces better results than another’s.
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