Two Clocks
Two clocks shape how organizations adapt to AI.
Clock 1 measures how fast capabilities improve. New features and model releases arrive every few months. They write better, plan better, and handle fuzzier instructions than the last version. This pace is set by labs, vendors, and the wider research community. It keeps moving whether you're ready or not.
Clock 2 measures how fast your organization changes how it works because of those capabilities. It includes policy, process, training, confidence, and the work of turning experiments into everyday practice. This clock moves much more slowly in most places.
When Clock 1 runs much faster than Clock 2, a gap opens. That gap is where your organization's fate lies. The smaller the gap, the better.
The catch is that Clock 1 isn't under your control. It's almost entirely external. Conversely, Clock 2 is almost entirely internal. You can't slow the first one down. But you can set the speed of the second.
Clock 1: AI Capability
What Speeds It Up
Most technologies follow an S-curve: a slow start, a period of rapid progress as forces compound (techniques combine, infrastructure matures, costs drop, talent deepens), then a plateau as returns diminish.
You might look at AI today, with new models, techniques, and applications arriving almost every week, and think we’re already in the steep part of the curve. In my view we’re still early, but three forces are pushing us toward that inflection point:
Reasoning and planning Models can now break problems into steps, test alternatives, and adjust when they fail. That makes them useful on genuinely novel tasks, not just variations on familiar ones.
Orchestration across systems Software can route parts of a problem to different specialized models or tools and then combine their outputs. One system may handle legal text, another financial calculations, another plain-language summaries. An orchestration layer breaks down a task, sends each piece to the right system, and integrates the results. A single improvement in one area can lift hundreds of workflows that use it.
AI used to build better AI Teams use AI to write parts of training code, generate synthetic data, test outputs, and speed up experiments. Each contribution is narrow and supervised, but the cumulative effect is faster development. When the tools that build the next generation improve each cycle, the next generation arrives sooner. It is not AI autonomously improving itself; it is engineers using AI as a powerful tool, and even small productivity gains shorten improvement loops.
These forces reinforce one another. Better reasoning makes AI more useful for complex development tasks. Coordination across systems tackles bigger parts of the process. As AI contributes more to its own development, cycle times shrink. Each step makes the next faster. That is what makes a J-curve possible instead of the traditional S-curve plateau.
What Slows It Down
There are brakes, but they mostly delay rather than stop the curve.
Compute and energy constraints limit how large and fast models can grow. Training the most advanced models requires enormous amounts of specialized hardware and electricity.
Regulation may slow releases or constrain what data can be used. Different jurisdictions are setting different rules, adding complexity and compliance costs.
Economics, especially hardware and power costs, can cap how much companies invest. If training runs get expensive enough, fewer organizations can afford the frontier.
Even with these brakes, Clock 1 is unlikely to slow to the pace at which most organizations adapt. The gap between what is possible and what you are doing will keep growing unless you actively work to close it.
Clock 1 Is Not Your Constraint
Clock 1 is fun to watch. The frontier keeps advancing, new capabilities land weekly, and it is easy to imagine what might come next. There is a natural pull to track model releases, debate timelines, and speculate.
Clock 1 is not under your control, and it is not your constraint.
Consider a thought experiment: even if we had AI that was indistinguishable from perfect (infinite capability, zero cost, completely reliable), most organizations would still struggle. Not because the technology failed them, but because they never built the capacity to absorb it. The constraint is not what AI can do. The constraint is what your organization can do with AI.
Treat Clock 1 as the environment, not a project. You cannot manage it. You can only operate within it. Which means Clock 2 is where all your leverage lives.
Clock 2: Organizational Absorption
Clock 2 measures how quickly you turn potential into practice, the time between “this is possible” and “this is how we work.” You can speed it up by understanding what accelerates it and what holds it back.
What Speeds It Up
Speeding up Clock 2 starts with recognizing that different groups move at different speeds and need different things from you.
A small number adopt immediately, maybe two to five percent. These are your innovators: curious, comfortable with ambiguity, and willing to handle rough edges. They need permission and visibility, not much else.
A larger group, maybe fifteen to twenty percent, adopts once they see the innovators succeeding. These early adopters are pragmatic. They want examples, guidance, and reasonably polished tools.
Then comes the majority, maybe sixty to seventy percent. They adopt when it becomes standard practice, when not adopting feels like the exception. They need it to be easy, reliable, and expected.
A small group resists until they have no choice. They need clear expectations and consequences. The hard jump is moving from early adopters to the majority, and most efforts fail because those groups need different things.
Your absorption strategy should match this reality: one approach for innovators and early adopters, and a different one for the majority.
For the innovators and early adopters These are the people who start the campfires. Your job is to make those fires visible and help them spread.
Give them permission to explore. Celebrate what they are learning, not just what they have proven. Make experiments visible through short demos, quick posts, and casual show-and-tell. The goal is not perfect documentation. The goal is judgment—what fits, what does not, how to verify outputs, how to weave AI into real workflows. People copy what they can see working near them; visibility beats memos.
Remove friction. Give access quickly. Let them try without approvals for every attempt. Shield them from bureaucracy. Their value is speed of learning, and friction erodes it.
But do not stop at exploration. Move them from experiments to expeditions. Experiments are about learning; expeditions are about reaching a destination. Hold them accountable for adoption, not just discovery. Success is measured by whether others follow, not just by finding something interesting.
For the majority The majority will not adopt on curiosity or excitement. They adopt when it is the normal way to work, the tools are reliable, and expectations are clear.
For this group, adoption is an expectation. New hires learn it in onboarding. Managers are accountable for how their teams use it. Performance conversations include whether the work is being done with the best available tools.
What Slows It Down
Several predictable habits keep Clock 2 slow, and none of them are structural. They are all choices.
Endless pilots mean learning forever, changing nothing. Perfectionism means waiting for certainty, which guarantees you stay behind. Certainty only comes from production use at scale. Tool fixation means measuring access instead of changed work. Optional culture means making adoption voluntary, which ensures uneven pockets. Each feels safe in the moment. Over time they make slowness permanent unless you actively change them.
Clock 1 explains the pressure; Clock 2 explains the response. When they finally align, the pace of change becomes manageable, and meaningful progress starts to feel normal.
When the Clocks Sync
When your absorption speed roughly matches capability speed, everything gets easier and the work itself changes.
Anxiety drops because you are not always behind. New releases become routine rather than overwhelming. You can evaluate each development on its merits instead of treating everything as urgent.
Judgment improves because you have used the tools enough to know what matters. You are deciding based on what you have actually learned, not guessing from demos.
Real growth appears, not just efficiency gains. Most conversation focuses on efficiency: automation, speed, headcount. Those are real, but they miss the bigger opportunity.
The bigger opportunity is work that was not possible before. Spreadsheets did not just speed up math; they made financial modeling and scenario planning routine. AI offers a similar shift, only larger in scope. When drafting, analysis, or planning take seconds, you iterate more, personalize more, and document more. You ask questions you never asked and explore ideas you would have dismissed.
That expansion of what is possible is where growth comes from. Not replacing people, but enabling higher-value work. Not doing the same with fewer resources, but doing more with the same. Not just cutting costs, but increasing revenue and impact.
Absorption compounds. Each workflow you modernize builds skills, templates, and trust that make the next one easier. Processes become reusable. A culture of continuous adaptation sustains itself.
The gap between fast and slow \"absorbers\" does not stay constant; it widens. An organization six months behind today may be a year behind next year and two the year after if rates stay the same. The fast absorber keeps the edge and extends it.
The inverse is also true. If you are behind and raise your absorption rate, you can close the gap quickly. You can skip to current capabilities and build around those. Being behind only becomes permanent if you stay slow.
Wrapping up
You cannot set the pace of capability improvement, but you can set the pace of absorption.
Clock 1 will keep ticking. The capabilities will keep improving. The pace might slow a little because of external constraints, but it will not slow to match most organizations’ current absorption rate. If you want these clocks to sync, you have to speed up Clock 2.
Start by acknowledging that different groups in your organization move at different speeds. Support the innovators differently than the majority. Make the campfires visible. Build reliable tools for people who are not enthusiasts. Make adoption an expectation, not an option. Think in tasks, not tools. Make AI a reflex, not a special consideration.
Over the next few years, absorption speed will shape your trajectory more than any single model release. The organizations that thrive will not be the ones with perfect information or zero risk, but the ones that decide quickly, change how work gets done, and learn in motion.
The gap between these two clocks will define who grows and who stagnates, not because of any specific AI application, but because of compounding advantages.
Clock 1 will keep ticking whether you act or not. Clock 2 is all on you. Sync the clocks while the gap is still small enough to close.