Tech Adoption and the Race for Second Place

Every office has one: the colleague who breathlessly evangelises the latest technology, certain that this time will be different. They demonstrate features with the enthusiasm of a convert, predict transformational change, and seem genuinely puzzled when others respond with polite nods rather than immediate adoption. They are not malicious, merely misguided about human nature. And they are destined to be quietly ignored.

I have watched this pattern play out repeatedly in professional environments. The evangelist invests time, energy, and often significant budget in what they believe to be revolutionary tools. They write emails about efficiency gains, organise demonstrations, and speak with the fervour of someone who has seen the future. Yet months later, most colleagues continue using familiar methods, the revolutionary tool gathers digital dust, and the evangelist moves on to the next technological salvation.

This cycle is accelerating around artificial intelligence. The predictions are breathtaking: entire professions transformed, junior roles eliminated, productivity multiplied overnight. Yet the more grandiose the claims become, the more they reveal a fundamental misunderstanding of how technology actually enters professional life.

The Human Speed of Change

History offers a sobering perspective. When Johannes Gutenberg unveiled the printing press in the mid-15th century, illuminated manuscripts continued to be produced by hand for more than a century afterwards. Electric lighting existed for decades before most offices abandoned gas lamps. The fax machine was invented in 1843 but did not become standard business equipment until the 1980s. Even email took nearly twenty years to move from university networks to general business use.

As the historian Ian Mortimer observed, the printing press was invented in the 15th century, but its revolutionary impact belonged to the 16th. The century of invention saw modest change; the century of adoption reshaped the world. Technology's capacity to transform society is rarely matched by the speed of its adoption—a pattern that offers a useful lens for understanding how artificial intelligence will reshape professional life over the coming decade.

The delay is rarely about technical limitations. It reflects deeper psychological and institutional forces. Loss aversion plays a significant role: the fear of making a mistake with new technology often outweighs the excitement of potential gains. Status quo bias reinforces this, making the familiar feel safer even when it is demonstrably less efficient. Meanwhile, organisations become locked into processes and infrastructure designed for older realities.

Legal practice provides a particularly vivid example. Despite living in an age of cloud storage and instant communication, wet-ink signatures remain legally required in many jurisdictions. County courts still accept fax submissions that would be rejected if sent by email. These are not oversights or bureaucratic failures. They reflect the conservative pace at which professional standards, regulations, and institutional habits evolve.

The Productivity Paradox

But there is a deeper issue at play, one that professionals intuitively understand even if they rarely articulate it: the misalignment between organisational and individual interests when it comes to efficiency gains.

When AI can draft a contract in three hours instead of six, the result is rarely an early finish. Instead, it becomes feasible to review twice as many contracts in the same timeframe. When document analysis software halves the time needed for due diligence, the response is not fewer working hours but higher expectations for turnaround times and case volumes.

Professionals recognise this pattern. They understand that productivity gains typically flow upwards, increasing organisational capacity while often intensifying individual workloads. The rational response is subtle resistance: tools that are never quite mastered, training sessions that somehow do not stick, and processes that remain perpetually "too complex" to fully optimise.

This is not sabotage or Luddism. It is an understandable form of self-preservation. A trainee solicitor who becomes exceptionally proficient with AI contract drafting may find themselves assigned twice the usual caseload, not rewarded with additional development opportunities or recognition.

Learning from the Last Wave

Microsoft's tablet PC initiative offers a compelling case study in premature evangelism. In 2000, Bill Gates announced tablet computers with characteristic confidence, predicting in 2002 that "the tablet is a PC that is virtually without limits—and within five years I predict it will be the most popular form of PC sold in America." Microsoft invested heavily in tablet PC development, created specialised versions of Windows, and pushed manufacturers to build tablet hardware.

The technology was impressive but the timing was wrong. The devices were expensive, heavy, had poor battery life, and the software was not optimised for touch interaction. Most critically, consumers were not ready for the concept. Microsoft's tablet PCs became expensive niche products used mainly in specialised industries. The company had successfully educated the market about tablet computing—but someone else would reap the rewards when Apple launched the iPad eight years later with the right technology at the right time.

This suggests something counterintuitive about competitive advantage. While business schools teach the virtues of first-mover advantage, there is often greater value in allowing your competitors to exhaust their budgets, burn through their best ideas, and make the inevitable early mistakes. Facebook displaced MySpace, Google outpaced AltaVista, Amazon learned from earlier e-commerce failures. The second mover enters leaner and meaner, with clearer conviction about what actually works rather than what sounds clever in theory. They benefit from expensive market education funded by someone else's shareholders.

The pattern extends beyond technology platforms to specific tools and approaches. Think of Betamax video cassettes—technically superior to VHS but ultimately abandoned by the market. Early adopters who invested heavily in Betamax systems found themselves isolated and eventually forced to start again. The same dynamic plays out repeatedly: laser discs, HD-DVD, Google Glass, countless software platforms that seemed revolutionary until they were superseded.

The Strategic Value of Patience

This does not mean ignoring AI entirely. It means adopting a different mindset about adoption timing. The goal is not to be first but to be ready when the moment is right.

The most effective approach involves small-scale experimentation rather than wholesale transformation. Use AI tools for internal research and analysis while maintaining proven methods for client-facing work. Test new platforms on low-stakes projects before committing to business-critical processes. Build familiarity with the technology without betting the practice on its current iteration.

This approach respects two crucial realities. First, that current AI tools are evolving rapidly and today's "cutting-edge" solution may be tomorrow's obsolete platform. Second, that meaningful adoption requires cultural change, not just technical implementation. Colleagues need time to build trust in new approaches, understand their limitations, and integrate them into existing workflows.

The key is building capability without premature commitment. Understanding the tools before you need them. Developing confidence in their reliability through repeated use in non-critical situations. Most importantly, recognising that the winners in technological transitions are rarely the first movers but those who move decisively when technology, market acceptance, and institutional readiness align.

Permission to be Deliberate

Sam Altman, the founder of OpenAI, recently complained that AI technology had not unleashed the social change he expected: "I somehow thought society would feel more different." His surprise reveals the gap between technological capability and human adoption patterns.

Change will come, but it will be uneven, tactical, and influenced as much by psychology as by technical advancement. Organisations that understand this will avoid the expensive mistakes of premature commitment while positioning themselves to benefit when adoption accelerates.

The challenge is to resist both extremes: the breathless evangelism that overlooks human nature and the stubborn resistance that ignores genuine opportunity. The middle ground involves thoughtful engagement without panic, experimentation without over-investment, and preparation without pressure.

For professionals watching the AI revolution unfold, this offers permission to be deliberate rather than hasty. Build understanding gradually. Test carefully. Learn from others' mistakes. And remember that in the uneven race of technological change, the greatest advantage often goes not to those who sprint first, but to those who start running at exactly the right moment.

The race for second place is not about being slow. It is about being smart enough to let others pay for the expensive lessons while you prepare to move with confidence when the time comes.