M.006 When Easy Becomes Hard with AI

Why the thing that drives human progress might be bad for business strategy

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In the EMBA program at NHH that I am chairing, we recently conducted an experiment intended to open the participants eyes to the opportunities and threats of generative AI. Or more specifically, we gave them a challenge: Build a tech startup in a day.

In about 8 hours, teams of 4-5 people built pitchable startups, including technical MVPs of their actual products, brand, detailed strategy and business models, and they even built (arguably messy) organizations consisting of multiple AI-workers. After hours of intense work, the crescendo was to pitch their ideas to one of the leading VCs in Norway. His conclusion was that all six pitches was investable.

This experiment shows two things. The first is that the dramatic reduction in frictions created by AI means that most of us can do things today that would be science fiction just a few years ago. The second is that if it takes a bunch of EMBAs a day to build something, there isn't really much to prevent someone else from doing the same if an idea turns out to be good.

This brings us to somewhat of a paradox: AI's greatest strength might also be strategy's greatest challenge.

The promise of AI

The AI-frenzy is everywhere these days. And with good reason. Studies show that just giving knowledge workers off-the-shelf-tools like Chatgpt increase their efficiency on a range of tasks with a staggering 25-30% (or more). AI can also reduce frictions on a more systemic level in business. Building technology, crafting a brand or operating 24/7 customer service, can with AI be set up at more systems level, in record speed.

It is therefore no wonder that AI occupies the mind of many executives these days.

But where the frenzy is even higher is among entrepreneurs. When you have less and value speed, a technology that promises more out of less at record speed is a god send. With the aid of AI-driven tools, a newly founded business can build or buy much of what they need to get off the ground more cheaply and much quicker than ever before. AI can also replace many tasks traditionally done by humans, at a fraction of the costs of real employees.

The result? Smaller teams, and many more startups.

All this is of course great both for the entrepreneurs and the firms taking advantage of these opportunities, and it can be good for the economy as a whole. Efficiency increases, productivity increases, innovation increases, and economic growth increases. All because AI is a friction reduction machine.

But AI’s role as a friction reducer is not really unique. It is actually the latest chapter in an old story of constant friction reductions by humans since the very early days.

A key outcome of human progress over the last 100,000 years or so is after all reduced friction. Life used to be hard. Over time it has become less and less so as we gradually innovated our way around friction after friction.

AI is the next iteration of this development, expected to propel further progress and growth through reducing frictions at a faster rate than ever.

But is there also a but? Could it be that AI removing frictions everywhere and making things easier, is also making other things harder?

To answer this, we need to look at where friction removal stops being universally good. And that place is business strategy, where friction removal might actually be undermining the foundations of competitive advantage.

The mother of competitive advantages

In strategy, the key question is to understand why some firms are better than other firms. And the answer to this question tends to be closely related to, you guessed it, frictions.

Friction is the mother of all sustained competitive advantages. A strategy seeks to create value by solving a problem for someone. That is, removing a friction. But the more counterintuitive point is that for firms to build and sustain an advantage from this over time, their strategy also need to embrace and build in frictions.

Frictions in the product market creates deviations from free competition, and potentially higher profits for the firms positioned there. If potential entrants face sufficient frictions related to establish themselves in a market, average profits will be higher. If customers and suppliers face frictions in the form of switching costs, they are less likely to jump between providers in the advent of price cuts (or increases), which means that vertical value capture and profits will be higher.

In other words: In product markets, no frictions, no profit potential.

A similar logic is found in factor markets, where firms go to acquire resources they need to compete. A friction-free factor market means that assets are correctly priced, making it very difficult for firms to acquire resources for less than their true value. When, on the other hand, factor markets have sufficient frictions, information efficiency will be lower, and firms might acquire resources for less than their true worth and build advantages on them. When frictions are so large that there isn't even a market for a resource, which is the case for many intangible assets like organizational capabilities, relationship, reputation etc, companies need to build themselves. If such resources turns out to be valuable, it becomes more difficult for others to imitate and compete with the advantage. Because imitators face frictions when trying to copy the process of building these resources. .

In other words: In factor markets, no frictions, no profit potential.

The issue with AI, from a strategy perspective, is therefore that AI's quest to reduce frictions everywhere will make it more difficult for firms to sustain competitive advantages. When friction disappears in one area, competitive advantages built on- or shielded by that friction will evaporate. The general outcome? More competitive advantages will be shorter lived than before (more on this in this paper).

When easy becomes the problem

Unfortunately, many startups seem too seduced by the gravitational pull of frictionless paths to see this point. This is most visible in the 'vibe startup' trend, where talented people powered by AI build impressive things with small teams, in record speed. Much like we did with our EMBA students. And such efforts often gravitate towards ideas that are easy to test and realise. Those with less friction.

Choose an idea that would work with a tech stack built with AI or nocode. Check.

Choose a market where distribution can be done online. Check.

Focus on customers that can be approached in social media, perfect for automated AI driven marketing rigs. Check.

Create a SaaS, where users self-serve and buy with a click. Check.

And why wouldn’t you? Democratization of technology has after all reached a point where the friction in each of these areas are so low that if you see an opportunity, you can build an advantage quickly. But the same trend also implies a steeper strategic challenge in sustaining any advantage you hope to build. The challenge of building your moat.

This means that more brainpower, creativity and attention should be directed towards the strategic challenge of friction removal by AI. And a good place to start is to remember that frictions serve a dual purpose in strategy: it's simultaneously the problem that strategy and innovation (with or without AI) seek to solve, and it is the solution to sustain a competitive advantage built on solving that problem.

But how can we strategically build frictions in attempt to sustain an advantage?

Maybe we should embrace more friction?

Above, we showed that the assets that you cannot easily buy in a market have the highest likelihood of supporting a sustained competitive advantage. Things like relationships, culture, complementarities between people and their competences, network effects, reputation, and more. The intangibles.

Actively seeking to build a strategy that complements the frictionless building, operation and distribution fueled by AI, with key (intangible) assets fueled by friction, will therefore make a firm a much more difficult target for competitors.

Many established firms are lucky in this sense, because they already have many key resources brimmed with friction. Their challenge is more on transforming their company and implement the new technology in ways that complement these established strengths. For startups, its the other way around. They face less of a challenge in technology implementation as they don’t have any legacy, but need to build or acquire the complementary assets that create frictions for others. Which is more challenging than it sounds.

Another way companies can be strategically smart about embracing friction is in who they target. For startups, it's easy to be seduced by the seemingly frictionless access to consumers in B2C markets, over messy B2B markets with slow sales processes, unclear decision processes, and difficult access. But given everything we have just discussed, this could also be seen as an opportunity more than a red flag. B2B is brimmed with frictions, and transparency is lower. If a company finds good ways to navigate frictions in a B2B market, any success will be less visible to potential competitors. And when a competitor eventually spot that a new idea was good, they will face friction after friction in trying to follow if its less than obvious how you navigated the frictions in the first place.

The strategic paradox of AI

All this suggest that the most successful companies of the AI era likely won't be those that simply ride the wave of reduced friction. It is more likely those that master the paradox: take advantage of AI's capabilities in reducing frictions, while simultaneously building competitive moats that are inherently friction-heavy and hard to replicate or circumvent.

If we seek to build more than a very temporary advantage, we should therefore ask ourselves questions like: Where do we want friction to remain? What should only we be able to do? And, what complementary assets can we build or enhance that takes time for competitors to catch up to?

When everyone can build fast and cheap, it can therefore make sense to build slow and hard - in the areas that matter most for competitive advantage. The clue is to spot the difference between good friction and bad friction, and have the discipline to embrace the former while eliminating the latter.

Because when everything becomes easy, easy suddenly becomes hard.