When Steve Jobs was a child, he read an article about a study on the efficiency of motion for the various species on the planet. The study ranked different species by how efficiently they moved across long distances. The study showed that the condor was the most efficient species and used the least energy to move across a kilometer. Humans were not very efficient and were about a third of the way down the list. However, a scientist decided to build on this study by testing the efficiency of a human on a bicycle. He found that a human on a bicycle was way more efficient than the condor or any other species. Jobs used to tell people this story as an analogy for the personal computer. With a computer, humans can move much faster and get much more done than we could without one.

As Jobs put it, “Computers are a bicycle for the mind.”

SaaS products have become an extension of this concept inside the modern enterprise. They’re like bicycles for the mind in that they make the average worker more productive by improving their workflows, providing them with new data and insights, providing infrastructure, or integrating different software applications to streamline work.

Getting these products into market at scale was extremely difficult. I have the scars to prove it. CIOs were resistant to software hosted outside of their direct control. Integrations took a lot of time and effort and dramatically slowed sales cycles. There was reluctance to partner with lots of modular software vendors. Users were reluctant to change their workflows. Buyers wanted heavy customization. It took years for it to work.

But amazing go-to-market teams overcame all of this, and now, most large companies use hundreds if not thousands of SaaS applications.

These SaaS companies and their investors have thrived thanks to the adoption of SaaS, but more importantly, thanks to the SaaS business model, where you aren’t just selling software for a one-time fee, implementing it and going out and finding the next customer. You are selling an annuity contract that will grow as you add features and your customers add employees. SaaS companies generally use ‘per-seat’ pricing, where a customer pays based on the number of employees using the product. This annuity places a large focus on CAC/LTV ratios (the cost of acquiring a customer relative to the lifetime value of having that customer). If you calculate that the average customer pays you $100k in the first year and that that price will increase 10% per year, and the average customer would stay with you for 7 years, the SaaS company can justify very large investments in new customer acquisition. This has resulted in innovative sales organizations and structures to drive speed and efficiency. SaaS companies would hire armies of Sales Development Reps or “SDRs” right out of college to do prospecting with clear promotion paths up to salespeople, sales managers, etc. Similar customer success and user success teams were built to manage ongoing renewals and upsells and drive engagement with the product to make sure the customer kept paying the annuity.

With the emergence of AI, there’s now much speculation that these SaaS companies are in trouble: 1/ because AI will allow a SaaS company’s customers to have fewer employees, reducing the number of seats they can sell, and 2/ because competitive SaaS products will do the job for the employee, making the SaaS product superfluous (the AI doesn’t need a bicycle).

When rolling out AI across enterprises, you could consider a simple three-step framework from the lightest touch to the heaviest touch: 1/ Making existing employees more productive, 2/ Replacing the work employees are already doing, and 3/ Figuring out what work needs to be done. Let’s consider each:

Phase 1: Making existing employees more productive, e.g., the extension of the ‘bicycle for the mind.’ This is what the LLMs are doing now and how most people are experiencing AI at the moment. They can perform tasks done by humans, but they require oversight. In my mind, they’re really just slick SaaS-like products. There are also other types of AI, like robots, that can mimic human expertise and analyze information, but these are by no means widespread and are really just making teams more productive. It’s all great and seems to be getting better, but I don’t think it’s going to change the industry or the business model in a massive way. It probably actually helps because the value creation gets bigger. In theory, a SaaS company makes an employee, say, 5% to 20% more productive and charges their customers some percent of that productivity gain. If SaaS+AI makes employees 80% or 100% more productive, the SaaS contracts could get really large.

Phase 2: Replacing the work the employees are already doing. The most talked about version of this that I’ve seen is automating coding or data entry or customer service or lower-skilled salespeople with AI agents where the AI is doing the human’s job rather than augmenting the work of the human. This leapfrogs the ‘bicycle of the mind’ concept. If successful, it would very much change the SaaS business model because you’re not selling a license for an employee; you're effectively selling an employee. In theory, if an employee makes $50,000 per year and the AI replaces their work, they could price the product at $49,999 per year, just under the employee’s salary. Because the AI doesn’t sleep or take vacations or can work much more quickly than the human, they could charge 2x, 3x, or 10x the employee's salary. Lots of people are very concerned about this phase because it’s the thing that will eliminate jobs at a very large scale.

I’m skeptical of that. For that to happen, a company would have to conclude that the value at Phase 1 is tapped out, meaning that it’s better to stop making the human more productive, and they should pass the baton to the robot. It seems to me there will be a very high bar for that decision. I wrote about the Jevons Paradox in December, which says that when a resource gets more efficient, we use more of that resource. Maybe the best example of this is bank tellers. It was widely believed that when the ATM was invented, it would eliminate bank teller jobs. The exact opposite happened. There are more bank tellers today than ever. When workers get more efficient, we want more of them to do more stuff. Companies are meant to grow. If bank tellers get more efficient because they don’t have to distribute cash or take deposits all day and can do higher value activity at the same price, companies will hire more of them. I suspect this will happen with AI. The day-to-day work may change, but as AI becomes more prominent, your workers get more efficient, and you want more of them, not less. Going the other way seems anti-growth to me, and anti-growth is not a stable place for a company to be.

Further, many SaaS companies don’t use a per-seat model. Healthcare SaaS companies might charge based on patient or member lives. Others charge based on the amount of differentiated data they provide or some set of assets the customer needs to support. AI will still need many of these things to do the work of humans, and I’m not sure those pricing models will change just because a robot is using the product.

Finally, today’s companies are built entirely around people. This was the key insight in the founding of the workforce management company Rippling – that a company’s central nervous system is its people – the tools, systems, support, infrastructure, management, and allocation of resources all center around people. We like to think companies are built around their customers, but they’re not. More granularly, the day-to-day tasks that get done are people-first tasks. We know they can be greatly augmented by software, but replacing them at scale is going to be a long, complex road. Ben Thompson had a great piece on this recently, pointing out that the companies that really find they can operate with AI as a replacement for employees will have to be companies that haven’t started yet. Companies that don’t have the hard-coded employee-based structure that companies have today. Companies that are native AI companies. And I don’t mean they start a company with people and have an AI product. I mean, they start a company without people, or at least without any large functions beyond a small group of individuals that manage the robots. That’s an important insight and distinction.

Phase 3: Figuring out what work needs to be done. This is where things get interesting. This, to me, is the pinnacle where the AI climbs the stack of human intelligence and replaces the highest-value activities. It’s where it goes from a front-line worker to a strategist who can zoom out and tell senior leaders how to run their company. There are signs of this with things like supply chain management and dynamic resource allocation, but many of those things still feel like Phase 2. Real Phase 3 is so far out at the moment that it’s kind of hard to comprehend and talk about.

All of this is just one framework to look at the diffusion of AI into the enterprise. As with any framework, you might have the elements of it correct, but the framework itself might be wrong. Regardless, as it stands today, the evolution of SaaS into AI-driven enterprises is less about replacement and more about enhancement. The replacement concept gets talked about a lot but still feels vague and, historically speaking, feels somewhat irrational. Companies built around people will find ways to amplify their workforce’s potential, not eliminate it.

I’m pretty sure the bicycle will be around for a while.