Agentic AI

In just about two and a half years, we’re assisting a further reshaping of the AI industry as we finally moved past the point where an AI needs continuous prompting to complete a task.

In what we can now call human-looped-in AI.

As in my reply to Amjad, founder of Replit, we moved from human-in-the-loop to human-looped-in.

That’s a further paradigm shift.

Or from a place where the human is instrumental in the AI to initiate a task and for the human to prompt its way through getting that complete to a place where the human only gets “looped in” by the AI (working in the background) whether to provide feedback on an intermediary work done or simply feedback on a final executed work.

From human-in-the-loop AI to human-looped-in AI

In human-in-the-loop AI, humans prompt the system to travel from point A to point B, guiding each step with explicit instructions.

In contrast, human-looped-in-AI empowers the agent to independently complete the entire cycle of tasks, with human involvement limited to providing strategic feedback or final approval.

This approach leverages the AI agent’s autonomy to manage complex operations while incorporating human oversight to guarantee quality and correctness.

Ultimately, it creates a more efficient, collaborative workflow between man and machine.

This model transforms conventional roles, ensuring AI completes tasks autonomously while humans carefully review outcomes.

Indeed, just yesterday, OpenAI released Deep Research.

This multi-step agent completes tasks, reports, and research that would have taken an analyst anywhere between a couple of hours to a few days to complete!

This is part of a much wider paradigm shift.

A platform shift is a fundamental transformation where incumbents initially leverage established distribution moats for short-term advantage in emerging markets but eventually struggle to adapt.

As new technology paradigms disrupt old business models, rapid shifts expose vulnerabilities, forcing companies to evolve or risk losing competitive dominance in dynamic cycles continuously.

Let’s review some of the key concepts that make this happen in the first place.

The Incumbent Paradox

The incumbent paradox (where the incumbent gets a leg up in the short-term, which loses effectiveness slowly, then quite suddenly) acts as a short-term distribution moat, allowing an incumbent to gain an early advantage in a newly developing market during a platform shift.

That’s a period of major confusion among the practitioners and onlookers, as it’ll prevail the narrative of “the new tech is just making incumbents richer and more dominant.”

Yet, that is only temporary. Indeed, as the platform shift happens, the tech incumbent will actually be the most challenged in making a core transition of a business model that is quite fit with the previous paradigm.

In this phase, the incumbent leverages its distribution strength to get a leg up, and it works!

Until distribution on the old paradigm, “slowly, then quite suddenly,” no longer works as a moat.

The Crossover Point

Thus, once the shift gains momentum, the gap between the old and new market widens rapidly, and the new distribution model takes control.

At that stage, the incumbent loses its competitive moat unless it has successfully adapted to dominate the new paradigm and platform.

The Platform Shift Dilemma says that moats that have worked for decades suddenly don’t.

Many incumbents are going through this in a very weird time window, where in the next decade, many of these might lose their core moats.

This dynamic, much slower in other industries, is instead the norm in a tech-driven business world.

Non-Linear Market Dynamics

The boundaries in the tech world are quite blurred as new markets develop continuously, and it’s tough to predict which one will be the next one that will eat them all while expanding on top.

Therefore, as a company operating in the tech industry, you must continuously transition your business model as you place bets on the future.

Indeed, the tech industry is super competitive; that’s what Andy Grove meant when, back in the late 1980s, he published “Only the Paranoid Survive.”

Look at Intel today, the market leader only 30 years ago, now an acquisition target.

Technology, which seems such a cool sector today, is also quite a wild ride, where the first-mover, in most cases, doesn’t make it to the other end of a market it opened in the first place.

It’s also a place where, in new markets, co-opetition is the rule, as technology markets operate with very complex dynamics and blurred boundaries where friend and enemy turn into “frenemies.”

When the future market gets dominated by startups turned into incumbents that benefit from winner-take-all, make the rule of the game until a new technology paradigm eats up the whole industry to go full cycle again.

That’s a key lesson to remember in the world of AI as it develops!

Recap: In This Issue

We’re looking just now at the emergence of a new AI shift (multi-step agents). Indeed, in human-in-the-loop AI, humans prompt the system to travel from point A to point B, guiding each step with explicit instructions.

In contrast, human-looped-in-AI empowers the agent to independently complete the entire cycle of tasks, with human involvement limited to providing strategic feedback or final approval.

That’s part of a wider platform shift.

Platform Shift: A platform shift is a fundamental transformation where incumbents use established distribution moats for a short-term advantage in emerging markets but eventually struggle to adapt as new technology paradigms disrupt old business models.The Incumbent Paradox:Incumbents gain an early edge by leveraging their existing distribution channels during a platform shift.This advantage is temporary—initial strength fades as the market evolves, leaving incumbents vulnerable when the old distribution model no longer serves as an effective moat.The Crossover Point:As the shift gains momentum, the gap between old and new market models widens rapidly.Incumbents lose their competitive edge unless they successfully transition to and dominate the new paradigm, marking the critical crossover point.Platform Shift Dilemma:Moats that have protected incumbents for decades can suddenly vanish in the face of disruptive changes.This leaves companies in a challenging situation where adaptation is essential or they risk losing their market dominance.Non-Linear Market Dynamics:In the tech world, market boundaries are constantly shifting as new markets emerge.Competition is highly unpredictable, and co-opetition—where competitors both collaborate and compete—is the norm.Rapid, non-linear shifts mean that today’s first movers often don’t survive in the long run, with startups turning into dominant players until another disruptive cycle begins.Dynamic Cycles of Dominance:The tech industry undergoes continuous cycles where new paradigms disrupt the status quo, forcing incumbents to evolve or be overtaken.This creates an environment where constant evolution is required to maintain competitive dominance.

With massive  Gennaro Cuofano, The Business Engineer

This is part of an Enterprise AI series to tackle many of the day-to-day challenges you might face as a professional, executive, founder, or investor in the current AI landscape.

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