People resisted automobiles, home electricity, household appliances, calculators, computers, cell phones, digital music, the internet, online stores, online learning, and electronic signatures.
Today, many are resisting artificial intelligence.
“It is clear to those who recognize it today: if a child does not develop expertise in using AI, they will fall not just a little behind their peers, but a monumental distance behind. The gap between those who embrace AI and those who resist it will be greater than most people can imagine. It will be like comparing those who insisted on traveling by horse with those who embraced jet travel. In fact, the gap will be even larger—much larger.”
— Howard J. Leonhardt
The technologies differ. The recurring reaction is familiar: distrust the new tool, defend the established method, and underestimate how quickly convenience and productivity can change expectations.
Then the new approach improves. Costs decline. People learn to use it. What once seemed unnecessary becomes routine.
Productivity has a way of bulldozing resistance.
When automobiles first appeared, some horse owners and people whose livelihoods depended on horse transportation opposed them. Early cars were expensive, unreliable, and poorly suited to many roads. Those shortcomings gave skeptics plenty of ammunition.
Yet automobiles kept improving. An economy built around horses gradually adapted to motor vehicles. Expertise in the old system did not remove the need to understand the new one.
Home electricity brought another profound transition. Some households hesitated over cost, safety, and unfamiliar equipment. Electrical hazards were real, and better standards and infrastructure mattered. But electricity also opened the door to extraordinary improvements in everyday life.
Washing machines reduced the physical burden of laundry. Dishwashers automated repetitive kitchen work. Microwave ovens made heating food dramatically faster. Each encountered skepticism about usefulness, expense, performance, or safety.
Their value became clear through experience. Families gained time and options.
A task did not become more valuable simply because it took longer to complete by hand.
Calculators brought a similar argument into the classroom. Educators debated whether students would lose arithmetic skills if machines performed calculations. By 1980, the National Council of Teachers of Mathematics was recommending that mathematics programs take advantage of calculators and computers.
Students still needed mathematical understanding. They also needed to learn how to use mathematical tools.
That principle applies directly to AI.
Personal computers faced their own credibility problem. The Computer History Museum describes how IBM’s introduction of its PC in 1981 helped shift perceptions of personal computers from toys to business tools.
Imagine an office insisting on typewriters and manual calculations while competitors adopted word processing and spreadsheets. Its employees could work tirelessly and still fall behind.
Effort matters. The tools supporting that effort matter too.
Cell phones also had obvious early limitations: bulky equipment, high prices, uneven coverage, and short battery life. Some people questioned why anyone needed to be reachable away from a home or office telephone.
As the technology improved, mobile communication changed expectations about availability, coordination, and access to information. Businesses had to adjust to customers and employees who could communicate wherever they were.
Digital music challenged another established system. Some listeners preferred physical recordings. Parts of the music industry resisted distribution models that threatened existing revenue and control. Concerns about piracy, artist compensation, and sound quality were substantial.
But listeners valued portability, access, and choice. The industry had to develop business models around those preferences.
The lesson carries across industries: customers do not owe permanent loyalty to an inconvenient distribution system.
The internet and online stores followed a related path. People questioned whether customers would trust unfamiliar sellers or enter payment information online. Pew Research documented early users’ reluctance to conduct online transactions—and found that many became engaged once they tried them.
Successful businesses worked to address trust, payment security, delivery, and returns. Treating those challenges as solvable problems created opportunity.
Treating them as proof that online commerce had no future created vulnerability.
Electric cars are another example of a technology facing resistance alongside practical obstacles. Buyers weigh purchase prices, charging access, range, and their own driving needs. Those considerations deserve serious answers.
The competitive task for manufacturers is to improve the product and infrastructure. For buyers, it is to evaluate what works for their circumstances. Reflexively dismissing the entire category makes it harder to recognize improvements when they arrive.
Adoption can be uneven. A technology does not have to suit every person immediately to reshape an industry.
Harvard offers a particularly memorable example of changing expectations.
In 2010, shortly after becoming dean of Harvard Business School, Nitin Nohria was asked when the school would enter online education.
His answer:
“Not in my lifetime.”
He later publicly acknowledged that he had been wrong. He initially viewed online education as a threat to residential learning and found early formats insufficiently interactive. His perspective changed when colleagues developed a more engaging approach.
By fiscal 2025, Harvard Business School reported $63 million in annual online revenue—from the business school alone.
An approach its dean once dismissed became a substantial educational business.
Changing his mind was sound leadership.
Electronic signatures challenged the familiar expectation that an agreement needed ink on paper. The United States established a broad legal foundation through the ESIGN Act in 2000, including the principle that covered signatures and contracts cannot be denied legal effect solely because they are electronic.
The productivity advantage is easy to understand: fewer documents printed, mailed, scanned, and returned. People remain responsible for understanding what they sign, while the process becomes faster.
Now AI is provoking many of the same arguments.
Will it weaken skills? Can its output be trusted? Will it disrupt jobs? Should students use it? Should companies allow it?
These questions deserve practical answers. They also create an obligation to learn enough about the technology to make informed decisions.
Evidence already shows productivity gains in specific settings. A study involving more than 5,000 customer support agents found that an AI assistant increased issues resolved per hour by approximately 14% on average, with larger gains among less experienced workers. Benefits varied, and experienced workers gained much less.
That is a reason to test, measure, and improve how AI is used.
For students, AI can support explanations, practice, feedback, and exploration. Education must still establish that students understand their work, can assess evidence, and can think independently. Schools should teach those abilities alongside responsible AI use.
For industry, the opportunity is to examine everyday tasks—research, drafting, coding, customer support, planning, and analysis—and determine where AI improves verified results.
Organizations that develop those capabilities can compound their advantage. Employees gain experience. Processes improve. Customers begin expecting the faster service and greater capability that successful adoption makes possible. The speed of innovation can be accelerated dramatically.
Those who refuse to develop AI competence risk being brushed to the wayside by people who combine expertise with effective AI use.
That is a prediction about competitive pressure, not a promise that every AI product will succeed. History includes failed technologies, costly mistakes, and skeptics who identified real problems.
But permanent refusal is a poor response to a tool that keeps improving.
We should preserve judgment, teach fundamentals, verify results, and learn to use better tools. The goal is better work: stronger decisions, faster execution, broader access, and more time for the tasks that require human attention.
The horse economy gave way to motor transportation. Household machines reduced exhausting chores. Computers transformed office work. Digital distribution reshaped music and commerce. Online education expanded access to learning.
AI is creating another test of our willingness to adapt.
“Not in my lifetime” makes a memorable quotation. Learning, adapting, and producing better results makes a stronger strategy.