Phil pods with Cognizant Co-Founder Frank D'Souza: Why AI is breaking the IT services industry

Sure, Phil. First of all, thanks for having me on the podcast. I was the founder, co-founder of Cognizant Technology Solutions in the early 1990s. I spent 26 years at Cognizant, including my last 12 years there as CEO. We grew that company from a startup to 280,000 people, $16 billion of revenue, and about $40 billion of market cap when I left about six years ago. I created Recognize, a private equity business that focuses entirely on investing in the next generation of digital services businesses. I've been doing that for the last six years. It's great to be with you.

You know, Phil, I think there were two or three things. The first is that we founded Cognizant at a moment when the industry was going through a massive operating model change, the offshoring moment in the early 1990s. There was a whole confluence of things that happened at the same time, low-cost bandwidth, the internet, that enabled global delivery to happen at scale. We were in the right place at the right time. The second part was that we executed really well. We had an incredible team and we spent a lot of time on this idea of talent density, putting the best team against the highest opportunities. The third thing is that we created for ourselves and with our investors the opportunity to invest in the business. We had a rule of 40 business before we talked about the rule of 40, which was that we told our investors we were going to maintain our margins at a level between 19 and 20%, below our competitors, to give us room to reinvest in growth. So I think it was those three things: tremendous industry tailwinds, the best team, and the space to make the right investments. Those things together created some magic.

Yeah, I would absolutely, Phil. When industries go through moments of big change, you have the well-documented phenomenon of the innovator's dilemma. Incumbents tend to focus on the current operating model, the rules of competition they are comfortable with, and it's very hard for incumbents to reinvent themselves. At those moments, the challengers have an advantage. They don't have the baggage of the past. They start with a clean sheet of paper and are able to disrupt the status quo. You see the pecking order shift at these moments of great disruption.

Yeah. In the white paper, I talk about this idea that code in some senses has never been more valuable. If you think about some of the most valuable companies in the world – Anthropic, OpenAI, SpaceX – these massive IPOs lining up, companies that have achieved revenue growth in periods of time never seen before. What underpins the value of many of these companies is their code, their product embedded or embodied in code. At the same time, the act of producing code, the cost of that is falling to zero. Large language models today are incredibly capable of writing code, and the marginal cost is tending towards zero. So you have this paradox where code has never been more valuable, coding is becoming lower and lower cost. In the context of any other industry, if your product has never been more valuable and your factor of production is becoming cheaper, that would be a formula for tremendous value creation. The issue in our industry is that we have historically anchored pricing on the input, not on the output. You don't go to buy a new car and say, 'I'm buying four tires, a transmission, chassis, cylinders, spark plugs.' You just buy the car. In our industry, for some reason, buyers and the industry have got used to selling the tires and the chassis. So I think the industry has this great opportunity to move to output and outcome pricing and create tremendous value.

I think so. The industry has been talking about this for a long time, but it's been accelerated by AI and generative AI in particular. The industry understands how to do this. By moving to output and outcome pricing, you align interests between the service provider and the client much more strongly. So I think it's possible, but it's not an easy transition for the incumbents because everything about the industry right now – the culture, the way people have been trained, the incentives, the metrics we use to track the health of a business – are anchored on what I call the scarcity era, where producing code was the scarce resource. We anchored on utilization, numbers of people. Making the shift will require firms to focus on a new set of metrics and change culture and compensation accordingly.

Absolutely. I've always believed that one of the most underappreciated aspects of the services industry is that because any individual company has clients across a range of industries, technologies, and functional areas, that diversity of work attracts the best and the brightest. People want breadth of experiences. Structurally, the industry is advantaged because it can create more interesting career experiences for consultants. We're going through a moment where everything is changing, AI expertise is scarce, but this is a moment when the industry can shine by attracting the best and brightest to do their best work for great clients.

When I talk about the J curve, there's a gap. If you're really deploying AI to its fullest potential, you'll likely see a compression in the sense that for a given amount of work, you can do it with fewer humans. If your pricing model is based on input prices like billable hours, you'll see some compression in revenue. So you might have a J curve of revenue, compression before you work your way out. Most firms that have been in the industry face some degree of J curve. If your revenue per employee or profit per employee is flat or declining, it's a good sign you're on the wrong side of the J curve. If it's flattening or increasing, you're likely through it.

Yeah. I believe if you step back, there is Jevons paradox: as the cost of something comes down, demand goes up. I think that's true in software. The world is becoming more software and technology intensive, not less. The demand for software will be substantially greater. The challenge is that many public companies are still using scarcity-era metrics when talking to investors. Investors don't understand how to dimension the J curve. If we were clear with investors that we're in a transition, but there's a path through it, and we had the courage to explain that there's a productivity gain from AI causing short-term compression, but done right should improve margins, I think investors would understand and welcome that message.

Yeah. It has always been the case that great technology innovation leads to a great tailwind for the services industry. I think the frontier labs that continue to innovate create opportunities for services firms to focus on the first mile and the last mile. To take technology and make it real, safe, ethical, and compliant requires services around the edges. The courage services firms need is to say that AI is better at some things we used to do, like writing code and test cases. Productivity will be dramatically improved. That's progress. Firms need to stand up and say, these are the parts of my business where productivity will have a dramatic effect, but these are the new opportunities where we can grow. On balance, that's a net positive.

I view it as a tremendous opportunity. In the early days of generative AI, there was this fear that LLMs would do everything humans do and there would be no need for services firms. The fact that these frontier labs are creating their own services businesses is an acknowledgement that the technology still needs humans to make it work productively for clients. The future belongs to humans and agents together deploying solutions. The model is now clear, and it creates an equal opportunity for services firms to partner with the frontier labs.

For sure. That's not just in our industry. You're not going to lose your job to AI; you're going to lose your job to somebody else using AI. Jobs are changing. The question is whether you are skilled at using this technology optimally, freeing yourself to do things that are uniquely human. I talk about art and AI. We don't yet have artificial wisdom. Wisdom is the sum of human experiences and humanity. Computers haven't captured that. The collaboration between agents and humans is a collaboration between artificial intelligence and human wisdom, and that will allow us to discover new things.

Yeah. Let's start with the idea of talent geometry. The industry used to focus on pyramids of humans. I believe the future will belong to teams that look more like a diamond shape. Today's AI automates many tasks that less experienced folks used to do. Coding and testing by junior developers will become automated. The real productive work will be done by a diamond-shaped team where experience matters. But there are two important points. First, the middle of the diamond is not the same as the middle of the pyramid. In the pyramid, the middle was people managers. In the diamond, the middle is about directing and managing AI. It's a different skill set. Second, services firms may not have fewer people overall. We still need to bring people in to train them. The gap at the bottom will be filled by deep apprenticeship programs. We need to bring entry-level folks and train them like other engineers. This may be an area for the whole industry to come together and create an industry-wide apprenticeship program.

Yeah. You've brought up a very important point. Even if you put aside how we bring people in, you can't build a sustainable culture by hiring middle-level folks from outside. You have to build culture from the bottom as people grow with the organization. Firms are going to need internship programs from the bottom up to build a sustainable culture.