Every generation believes its breakthrough technology will reshape the world overnight. Every generation is half right. The technology arrives quickly. The reshaping? That takes decades.
We are living through this tension right now with artificial intelligence. ChatGPT reached 100 million users in two months, the fastest adoption of any consumer technology in history. TikTok took nine months. Instagram took two and a half years. By that metric, AI has already won. But adoption is not transformation, and downloading an app is not the same as rewiring how you work, think, or build.
History has a pattern here, and it's worth paying attention to.
Lessons from the steam era: The factory that couldn’t see the future
The story of electricity replacing steam power is the clearest illustration of technology's long tail. Edison opened his Pearl Street power station in Manhattan in 1882, delivering electricity to 82 customers. By the 1890s, practical electric motors existed. The technology was ready.
But in 1899, electricity still provided less than 5% of the mechanical power in American manufacturing. Factories had spent decades optimizing around steam. A single massive engine sat in the basement, connected to every machine on every floor through an elaborate system of rotating shafts, belts, and pulleys. The entire building — its architecture, workflow, and workforce — was designed around that central engine.
When factory owners first adopted electric motors, they did something revealing: they used them to replicate the steam setup. They bolted a big electric motor where the steam engine had been and kept all the shafts and belts in place. Engineers called this "group drive." It was electrification without transformation: new power source, old thinking.
The real revolution came with "unit drive": individual motors attached to individual machines. No more shafts. No more belts. Suddenly factories could be single-storey, spread horizontally, reorganized around workflow rather than around a power source. Productivity soared and worker safety improved but this insight took thirty years to become standard. Electricity didn't reach 75% of manufacturing power until 1929, nearly half a century after Edison's first station.
The lesson isn't that people were slow. It's that the infrastructure, skills, organizational habits, and mental models built around the old technology created enormous inertia. You don't just swap the engine, you reimagine the factory.
The pattern repeats
This pattern of fast invention but slow integration echoes across every major technology shift.
Gutenberg built his printing press around 1440 and within sixty years, presses had spread to over 200 European cities, producing more than 20 million volumes. The technology proliferated rapidly but literacy rates moved glacially. In 1440, roughly 30% of European adults could read. Two centuries later, estimates suggest that figure had climbed to less than half the population in most of Western Europe. The press didn't just need readers; it needed schools, a merchant class hungry for knowledge, religious reformers distributing pamphlets, and a cultural shift that made reading something ordinary people did. The machine was fast but society was not.
The internal combustion engine followed the same arc. Nicolaus Otto designed the modern engine in 1876. Carl Benz produced a practical automobile a decade later. Yet in the 1890s, New York City still ran on 150,000 horses, each producing roughly 22 pounds of manure daily. The "Great Horse Manure Crisis" was a genuine urban planning concern. It wasn't until Ford's Model T made cars affordable and cities rebuilt roads, fuelling stations, and traffic laws that automobiles overtook horses, a transition that took roughly 20 to 30 years from the first practical car.
The internet's timeline is strikingly similar. ARPANET sent its first message in October 1969. Email existed by 1971, and by 1973 it already accounted for 75% of network traffic. The technology was clearly useful. But commercial internet access didn't arrive until the early 1990s, and it took until the 2000s for e-commerce, cloud computing, and digital workflows to genuinely reshape how businesses operated. Thirty years from the first packet to Amazon Prime.
The smartphone lesson: Why AI needs the right environment to succeed
Mobile phones compress this timeline but reinforce the same distinction between availability and transformation. Martin Cooper made the first mobile phone call in 1973. The first commercial handset arrived in 1983 — a two-pound brick with thirty minutes of battery life, yours for $3,995. Phones got cheaper and smaller throughout the 1990s, but for most people they remained just phones: voice calls and maybe a text message.
The iPhone launched in 2007, 34 years after Cooper's call. Apple sold 1.4 million units in its first year, then 11.6 million the next. While impressive, the real shift wasn't the device itself, it was what the device enabled once developers, entrepreneurs, and users reimagined their workflows around it.
Consider Uber. Founded in 2009, launched in San Francisco in 2010, it introduced UberX, rides in ordinary cars at a third of the taxi price, in 2012. Within two years, hailing a cab had been fundamentally redesigned. But think about what had to exist first: ubiquitous smartphones, reliable GPS, mobile payment infrastructure, app store ecosystems, and widespread cellular data. Uber wasn't just a clever app, it was the culmination of decades of underlying technology reaching maturity simultaneously. The "overnight disruption" had a 34-year runway.
The current state of AI
This all brings us to ChatGPT, which launched on 30th November 2022 and broke every adoption record. By early 2025, 43% of US knowledge workers reported using AI in some form, up from fewer than 10% just two years earlier.
The raw adoption is undeniable but look closer and the electricity parallel is impossible to ignore. Most organizations are in "group drive" mode. They've bolted AI onto existing processes such as drafting emails faster, summarizing documents, or generating first-pass code, without fundamentally rethinking the work itself. The motor is new, the shafts and belts remain.
The real transformation, the "unit drive" moment for AI, will come when organizations stop asking "how can AI do this task faster?" and start asking "would this task exist at all if we designed the workflow from scratch?" That's the difference between using AI to write a report and questioning why the report exists in the first place. Between using AI to answer customer tickets faster and redesigning the product so those tickets are never filed.
We're not there yet and history suggests we shouldn't expect to be. The printing press needed schools. The automobile needed roads. The internet needed broadband. The smartphone needed app ecosystems. AI needs its own infrastructure — not just technical, but organizational. New roles, new metrics, new ways of structuring teams and evaluating output. New trust frameworks. New regulations. These things don't arrive in a software update.
The honest timeline for AI transformation
If history is any guide, here's the uncomfortable truth: AI's full impact on how we work won't be felt for another 10 to 20 years. Not because the technology isn't ready (it largely is) but because we aren't. Our organizations, incentive structures, training pipelines, and habits are all built around the old architecture.
The companies that will define the next era aren't the ones adopting AI the fastest, they're the ones willing to redesign the factory - to question not just which tasks AI can accelerate, but which workflows, roles, and assumptions no longer make sense in a world where intelligence is abundant and cheap.
Electricity didn't just replace steam, it made the steam-era factory obsolete. AI won't just automate tasks, it will make the task-era organization obsolete.
The technology is here. The transformation is just beginning.
The factory needs a foreman
Here's the practical question nobody is asking loudly enough: if the future is a mix of humans doing manual work, software executing via traditional APIs, AI agents handling cognitive tasks, and swarms of agents collaborating autonomously, who coordinates all of that?
This is exactly the problem Cutover was built to solve. Cutover is an orchestration platform designed to coordinate any kind of task needed to get a job done, regardless of who - or what - is doing it. Whether it’s a human following a checklist, a system firing through an API, an AI agent making a decision, or a fleet of agents working in parallel, Cutover doesn't care about the power source, it cares about the workflow.
That distinction matters because the "unit drive" moment for AI won't arrive through better models alone. It will arrive when organizations can seamlessly blend human judgement, automated systems, and intelligent agents into a single coordinated flow that is visible, auditable, and adaptive. The factory of the future won't run on AI any more than the factory of the 1920s ran on electricity. It will run on orchestration - the technology that stitches the old and the new together, task by task, until the new way of working becomes the only way of working.