Bridging the Gap Between Early and Late AI Adopters

Nov 20, 2024

Early adopters1 of innovation2 are in my experience temperamentally risk-tolerant, comfortable with uncertainty and change—they don’t get bugged by bugs and glitches. They have an openness to unfamiliar experiences and are often extroverted and social. They are naturally curious about novelty and keep their eyes open for the next big thing.

When it comes to new technology they are less price sensitive. Their desire for upgrades can take precedence over other needs when their budgets force a choice. In movies they prefer science fiction that play with the future.

Early adopters are important in driving innovation forward, but their eagerness to embrace the new can lead to significant personal and financial drawbacks. Their need to be first often results in paying premium prices for unproven, first-generation products that may soon be obsolete or dramatically reduced in price.

This “pioneer tax” extends beyond money—early adopters waste time troubleshooting, dealing with incomplete features, and navigating poorly documented interfaces, all while serving as unpaid beta testers for companies that profit from guinea pigs.

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Late adopters are more risk-averse, and they prefer stability and proven solutions. Highly conscientious, they tend to be more practical and pragmatic. The tried and true remain their go-to tools until the new becomes the old. Their need for novelty is low, their valuing of reliability is high, and they prefer established systems and products after prices have come down.

Late adopters still have landline phones, common among older adults who like the comfort of having a dedicated "home phone" rather than relying solely on mobile devices. They often pair this with an answering machine rather than voicemail.3

Despite digital alternatives like Google Calendar or Outlook, many late adopters still prefer physical day planners, wall calendars, and paper appointment books. They cite the tactile experience, battery independence, and ability to see their schedule without opening an app.4

While streaming has dominated entertainment, some late adopters maintain traditional cable packages. Some enjoy the familiar channel-surfing experience, live programming, and not having to manage multiple streaming subscriptions. They often still use DVRs instead of on-demand services.5

A subset of users deliberately choose simple phones that just handle calls and texts, avoiding smartphones' complexity and constant connectivity. These "dumb phones" often appeal to those trying to minimize digital distractions and seem to know no age boundaries.

Rather than embracing cloud storage and laptops/tablets, some users prefer desktop setups with local hard drives, physical media (CD/DVD), and wired peripherals. They often cite security concerns and ownership of their data as key reasons.6

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The LLM adoption divide presents a much different phenomenon from traditional technology adoption7 for several key reasons. Traditional tech (landlines, DVDs) represented incremental improvements in doing familiar things. LLMs represent an unfamiliar way of thinking about knowledge work and human-machine interaction.

The divide isn't just about comfort with new tools, but about beliefs regarding human cognition and expertise.8 Choosing a landline or a flip phone over a smartphone was a simple personal consumer choice. LLM adoption often ties directly to how people view their professional worth and their future employment—and how colleagues view them.

Traditional tech adoption mainly involved practical tradeoffs (cost, convenience, quality9). LLM adoption involves complex ethical questions about AI safety, truth, creativity, and consciousness. The divide often reflects embedded and protected philosophical positions about human uniqueness and machine intelligence.10

Traditional late adopters could often avoid the new technology entirely. Still today we find individuals who don't use email, who eschew smartphones for basic flip phones, or who handle their banking in person rather than online. Their newspapers cost an arm and a leg, but the familiar thump on the front porch is worth every cent. These personal choices might have limited their options but didn’t prevent them from living functional lives.

By contrast, LLM effects ripple through society regardless of personal adoption from AI-generated content flooding news and entertainment to automated customer service interactions to workplace tools and policies to their children's education.

Even those who actively resist using LLMs directly are increasingly interfacing with AI-mediated experiences, often without their knowledge or consent. The technology is becoming infrastructural in a way that makes pure "non-adoption" nearly impossible.

In previous technological transitions individual non-adoption remained a viable long-term strategy. The current divide creates novel forms of social and professional stratification based on AI literacy. LLM capabilities are advancing so rapidly that the knowledge/skill gap between early and late adopters may actually widen over time.11

The emergence of transformative technologies that became effectively mandatory for participation in society happens infrequently, and early adopters may actually be a sort of check and balance on scientific misjudgments characterized by, oh, innovations like nuclear weapons. In the case of AI, risks are real and must be contained. Late adopters remind us of this fact.

Consider innovations that were first resisted but didn’t end with annihilation. Writing was resisted by oral tradition devotees like Socrates, who opposed writing, claiming it would destroy memory. Eventually it became impossible to fully participate in commerce, law, or governance without it. Those who remained illiterate became increasingly marginalized and dependent.

Banking and currency replaced complex and unwieldy barter and gift economies12. Some cultures resisted the monetization of life, but eventually it became impossible to engage in long-distance trade or to store wealth without it. Digital payments, the modern equivalent of dollar deposits, make cash-only living increasingly difficult

Industrial manufacturing devastated artisanal craftspeople and home production13. Strong resistance movements like the Luddites ultimately failed. Hand-made goods became luxury items rather than daily necessities. Those who didn't adapt to the industrial economy faced economic marginalization.

Electricity was feared as dangerous and unnatural14. Some religious communities still limit its use. It became impossible to participate in modern commerce or communication without it, however, and non-electrified communities became increasingly isolated.

The Internet itself was initially dismissed as unnecessary by many businesses and individuals.15 Some still resist but face increasing difficulty with basic services. Government, banking, commerce, and social life now demand predominantly digital interaction. The "digital divide" creates serious economic and social disadvantages.

In each case, individual resistance was possible but carried increasingly heavy costs in terms of social and economic participation. The technology became non-optional much like AI appears to be becoming.

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At least five critical risks face AI non-adopters, and in my opinion AI leaders bear the responsibility to address them:

Professional Obsolescence Risk

Non-adopters face diminishing career prospects as AI tools become workplace standards. AI leaders must create clear on-ramps with structured training programs that respect experienced professionals' existing expertise while helping them integrate AI tools. The focus must be on augmentation rather than replacement narratives.16

Economic Disadvantage Risk

Non-adopters will become less competitive in pricing, efficiency, and output quality. AI leaders must develop fair pricing models that don't exclude small businesses and independents.17

Digital Literacy Gap Risk

The knowledge gap between AI-fluent and AI-resistant workers widens daily. AI leaders must build intuitive interfaces that leverage familiar workflow and provide progressive learning paths that start with simple use cases and gradually increase complexity.18

Social/Professional Isolation Risk

Non-adopters may find themselves excluded from evolving professional networks and opportunities. AI leaders must foster inclusive communities that welcome newcomers. Creating mentorship programs pairing experienced AI users with late adopters will perhaps lead to badges or credentials to insure AI expertise is passed on.19

Decision-Making Disadvantage Risk

Those who don't understand AI will struggle to make informed choices about its use in their field. AI leaders must improve transparency about AI capabilities and limitations tuned to idiosyncratic demands of specialty fields.20

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The time for debating whether to adopt AI has passed. Our children are growing up in a world where AI is everywhere. Our responsibility now lies not in resistance, but in shaping how this technology is integrated into our lives and our children's futures. It’s not about abandoning valid concerns or rushing into every new AI development. Instead, it's about channeling our natural caution into productive engagement.

Late adopters bring vital perspectives to AI development. Their emphasis on reliability, security, and human values must help shape AI's evolution. But this influence can only happen through active participation. Rather than standing apart from AI advancement, late-adopters must demand substantive professional development, ongoing training, and meaningful input into how AI tools are developed and deployed in their fields.

Our children don't have the luxury of non-adoption. They will inherit an AI-integrated world whether we participate in shaping it or not. Just as previous generations faced integration of television and the internet into children's lives, we face the crucial task of establishing healthy patterns for AI interaction.

The path forward isn't about becoming AI enthusiasts, but about becoming AI literate. It's about maintaining our critical perspective while developing the knowledge to engage meaningfully with these tools. Changing resistance into constructive skepticism and channeling concerns into actions that help shape AI's development in alignment with human values and needs will require all hands on deck.

The choice before us isn't whether to adopt AI, but how to engage with it wisely. In a profound reversal of traditional learning, our children may become our guides, not because they know more, but because they see with fresh eyes what we view through the lens of the past. Our role is not to resist this future, but to bring to it the wisdom that only experience can provide.

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Bridging the Gap Between Early and Late AI Adopters

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