Learning Velocity: The Next Strategic Advantage

Entrepreneurs realize that speed compresses learning. They know some of their initial assumptions will be wrong or only partially right, so going fast allows them to test ideas and move from opinions to evidence to products before they run out of cash or time.

A startup that spends years to get to market may end up solving the wrong problem. The startup that launches in weeks or months figures out what their audience cares about, what is irrelevant and they evolve their approach in real-time. Test, learn, adapt and repeat the cycle.

The most successful entrepreneurs know that the original idea is often less important than the rate at which they improve it.

This thinking has existed for decades. Now, AI enters stage left.

The Importance of Learning Velocity

We are entering an era where learning velocity is reducing the time between a question and an answer, an idea and a test and a mistake and its correction.

Learning Velocity is the speed at which an individual, organization, or nation converts information into capability, decisions, and action. Increasingly, competitive advantage is determined not by who possesses the most information, but by who learns from it fastest.

AI changes the speed and quality of the learning cycle.

Can we understand if a disinformation attack is starting before it succeeds?

Can we routinely build synthetic adversary simulations worldwide to improve our preparation?

Can we see 10,000 new accounts emerge in a few weeks to amplify identical narratives across five platforms in five languages and know exactly who is driving it?

Can we identify the next TikTok or Discord well before it gains altitude?

Adversaries realize the pace at which they learn is often as important as what they learn if they are to win the race to market. From Silicon Valley to Bekaa Valley, the learnings are the same. It’s all a matter of how it is applied.

Are We Ready?

Throughout history, strategic advantage has often belonged to those who learned faster than their adversaries. U.S. military doctrine embraced the OODA Loop—Observe, Orient, Decide, Act—as a model for accelerating decision cycles faster than opponents.

Times have changed. Bad actors are compressing learning cycles from months to days and increasingly from days to hours. The public and private sector too often operates on models of learning that are designed for a slower century.

Their advantage is not necessarily better technology, more information, or greater resources. Their advantage is that they learn faster.

Let’s imagine how they learn for a minute.

Imagine the Fastest-Growing University in the World

It has no campus, no admissions office, no tuition, and no accreditation.

Its students come from every country. Its curriculum updates daily. Its teaching assistants are powered by artificial intelligence.

Its graduates include cybercriminals, fraudsters, influence operators, hostile intelligence services, extremist groups, and increasingly, highly capable lone actors.

Welcome to the New University for Bad Actors

Freshman Year: Open-Source Intelligence

Freshmen begin with open-source intelligence 101.

Their textbooks include LinkedIn, Google Earth, company websites, SEC filings, public procurement records, satellite imagery, and social media.

Their first assignment is simple: learn everything possible about a target without ever touching its network.

They learn how to map executive organizational charts, vendor relationships, facility layouts, and employee behavior patterns almost entirely from publicly available information. Whether or not they put on the “freshman 15”, they will learn how to use commercial satellite imagery, drone footage, all forms of digital media, ship, aircraft and supply chain tracking data, patent filings, geolocation tools and more.

Sophomore Year: AI-Assisted Learning

This is not followed by a sophomore slump. Rather, they rapidly move into AI-assisted learning.

Their professors are ChatGPT, Claude, Gemini, Perplexity, Grok, DeepSeek, and other large language models.

What once required months of research can now be accomplished in hours.

Generative AI enables bad actors to create deepfake videos, cloned voices, fabricated identities, realistic images, and highly personalized communications at scale. AI agents can conduct individualized outreach to thousands of targets simultaneously, making social engineering attacks more believable and more effective.

They learn how people form trust. Why a narrative resonates or fails with an audience. How to choose the right topics, build a plan and create polarization. And they start to see where trust is weak and where attacks may be more successful.

They are becoming better students of human behavior.

Junior and Senior Year: Laboratories and Code

Juniors enter the laboratories ready for one of the most important shifts in intelligence and strategic competition.

Hugging Face, GitHub, ArXiv, Discord developer communities, Papers with Code, Stack Overflow, Reddit, Kaggle, and open-source communities become classrooms where they learn how innovation occurs and where the world is going.

They learn which AI capabilities have the most promise in the next year. They can see which technologies are gaining acceptance and which ones are losing steam. Perhaps most important, they witness which workforce skills will be most valuable. They know the performance benchmarks, which datasets matter and where the problems lie.

These students realize they can see which technologies have promise years before they become mainstream.

They are also keenly aware of the speed of innovation. They can read the signals of repository growth, model releases, publication speed and more.

The graduation ceremony is brief. No champagne, just more learning ahead.

Graduate Studies: Shared Learning Networks

Graduate students enter encrypted networks, private forums, and invite-only communities where ideas, techniques, and lessons learned are exchanged among peers. Successful attacks are dissected and analyzed. Indicators of compromise, response timelines, victim profiles, and operational mistakes are openly discussed so that future attacks become more effective.

Every successful operation becomes a lesson for the next one.

Some students have more to learn.

Doctoral Research: Data as a Weapon

Doctoral candidates study commercial data ecosystems, public records, and digital surveillance.

They discover that some of the most valuable information does not need to be stolen. It can be purchased.

Post-doctoral researchers venture into dark web marketplaces and specialized forums where knowledge, tools, services, and expertise are exchanged at extraordinary speed. Personal information, new malware, breached corporate data, criminal service marketplaces, critical infrastructure targeting and more.

The dark web and shared learning networks provide that competitive advantage – information others don’t have.

Why Are They Learning Faster Than We Are?

Bad actors are not necessarily learning more than we are. The real difference is how fast they learn.

When resources are constrained and survival depends on adaptation, speed of learning becomes a competitive weapon. The most successful entrepreneurs build companies with an idea, often inadequate finance resources and a ticking clock showing they could run out of time.

Speed is not only an asset, it is about survival of the idea.

A New Era Deserves a New Model

The challenge for many established institutions is not a lack of intelligence, talent, or resources. It is resistance to change.

We don’t like failed pilots, so people stop experimenting. Our budgets are annual. Our feedback loops are slow. We may learn quickly, but we act slow.

Behavioral economists refer to this as loss aversion. Humans experience the pain of losing existing processes, authority, expertise, and familiar ways of working more intensely than the potential gains associated with adopting new approaches.

The reality is learning faster than adversaries and adapting faster will be our advantage to create and sustain.

Learning Velocity will become our long-term strategic advantage, which means we need a practical way to evaluate our progress.

The model is defined as the speed at which an individual, organization, or nation converts knowledge into capability.

The Learning Velocity Model

L — Locate

How quickly can we identify emerging threats, opportunities, technologies, and changing conditions?

Measure in days, hours, minutes and seconds.

E — Evaluate

How quickly can we determine what matters and separate signal from noise?

What is in the way of any decision?

Are we learning in the best places?

A — Align

How quickly can insights spread across teams, agencies, departments, and decision-makers?

If any insights are blocked, is this done to improve our security?

R — Respond

How quickly can knowledge be converted into decisions, actions, and measurable outcomes?

Time from moment we know to moment we impact. The full timeline is important.

N — Navigate

How quickly can we adapt when assumptions prove wrong or circumstances change?

How do we learn from failure? Are we improving our ability to anticipate?

Preparing for a World Beyond AI

AI will continue to serve as a learning accelerator. It will be joined by quantum computing, which will make it possible to simulate a problem in minutes or hours. 6G will expand how we connect billions of sensors, vehicles, drones and devices to feed AI systems. Digital twins will create virtual versions of supply chains, aircraft, power grids and more so we can test operational concepts before deployment. The physical world will become measurable in new ways, ranging from advanced sensors to satellites.

And of course, as these technologies converge, new transformational waves will be experienced.

The future competitive advantage will belong to the organization or country with the fastest and most adaptable learning system, not the most information.

Moore’s law was first proposed in 1965. Gordon Moore, co-founder of Intel, predicted that the number of transistors on a chip would double every two years, leading to major improvements in computing power. It illustrated how we compressed the cost of computation.

The skeptics said you can’t keep shrinking transistors forever or the manufacturing costs will be prohibitive or the growth rate is unsustainable.

The skeptics were wrong.

The cognitive security version of this model, which has blanks to fill in, will be something like “learning velocity doubles every X years while the cost of learning falls by half.”

It’s up to us to define this model and turn a theory into an advantage that improves security of our world.

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