Former Intel CEO Andy Grove wrote a book more than two decades ago called “Only the Paranoid Survive.” Intel took that mantra to heart for many years, reinventing itself multiple times and pushing to stay ahead of technology trends. I had a front-row seat to Intel’s aggressiveness, working as an intern at the company 30 years ago and, later, as an investor with the company’s Intel Capital venture-capital unit.

As anyone following today’s tech headlines knows, a lot has changed for the chip giant, unfortunately. Intel is widely acknowledged to have missed the mobile-computing revolution and, more recently, ceded the silicon limelight to Nvidia, the GPU pioneer whose chips are now fueling the current AI boom (and whose market value is nearly 30 times Intel’s.) Intel’s star has fallen so much that there’s been serious discussion that the company could get acquired by another large tech company or even be taken private by investors. Recently, the Wall Street Journal reported the company could be split, with its operations taken over by rivals TSMC and Broadcom.

How did this happen? Here, I’ll explore some of the lessons Intel’s shifting fortunes could hold for AI leaders trying to navigate the current technology landscape, where silicon continues to drive so much innovation and technology cycles are moving faster than ever.

The main problem: complacency

Intel’s woes have a host of causes, but to me the chief one is complacency. The company’s original, founder-driven mentality – which had propelled it to dominate the CPU market with over 90% share in PCs and servers – began to shift a couple of decades ago. Under former CEO Paul Otellini’s sales-driven leadership, Intel expanded its business significantly but missed critical emerging markets, including gaming (Nvidia’s former sweet spot), mobile computing and then cloud services. While Intel focused on protecting its existing markets, Nvidia spotted the graphics-processing unit (GPU) opportunity—first in gaming, then crypto, and now AI.

Similarly, Amazon Web Services turned “small” cloud services into a $27B+ quarterly revenue stream. Even Microsoft, Intel’s longtime “Wintel” partner, innovated out of its old PC-centric business model to become a major player in cloud and quantum computing and now AI. Intel, instead, focused on protecting its existing business.

So how can today’s startup founders stay nimble and avoid getting caught up in the complacency trap?

1. Stay in “founder mode.”

Y Combinator Founder Paul Graham made news last year with his essay extolling the virtues of what he called “founder mode.” According to Graham, successful tech-company founders should stay engaged in all aspects of their business—what might be called micro-managing—instead of handing over the reins to professional managers (who operate in “manager mode”, hiring good people and delegating work to them.)

Intel, like many large companies, morphed from a founder-led business—it was started by Gordon Moore and Robert Noyce in 1968, with Grove as its third employee—to one run by executives more focused on management and sales. Paul Otellini ran the company from 2005 to 2013 as the first non-engineer to serve as CEO. While he managed a lucrative partnership with Apple, he also misread the size of the mobile-computing market and the fact that less-powerful, “good enough” chips could power the next generation of computing.

Similarly, today’s startups can’t afford to dismiss adjacent markets or emerging use cases, particularly with the length of technology cycles shortening. Our portfolio company Databricks* is a great example. The company’s evolution from a business that developed the Apache Spark open-source processing system, focused on big data workloads, into one that made 1) software-defined data warehouses, and now 2) comprehensive AI/ML platforms, demonstrates the importance of constant reinvention while maintaining core strengths.

Interestingly, Databricks had seven original founders, all academics at the University of California at Berkeley. One of them, Ali Ghodsi, still runs the company today.

Other notable companies have engineered similar transformations. As mentioned, Microsoft successfully transitioned from the PC to the cloud era and then to AI; the company’s biggest source of revenue is currently its “intelligent cloud” business segment. Apple went from being known for candy-colored computers to changing the world with the iPhone, while Netflix morphed from a DVD movie-rental service into a major producer of award-winning movies and TV shows.

2. Stay close to the customer

I think the Intel story also demonstrates that product innovation requires customer proximity. Intel grew distant from end users over time, focusing on selling its products to system-integrator go-betweens instead of more directly to customers. Its foundries essentially sold chips to Intel’s internal PC division, cutting the company off from real market feedback about its products.

Nvidia took the opposite approach: The company doesn’t just build chips, but creates complete solutions based on customer needs. I see Nvidia and its charismatic CEO, Huang, constantly engaging with customers at events. He’s constantly collecting data about how machine-learning workflows evolve and building both hardware and software to meet those needs—even though he’s already running a business that had $60.9 billion in revenue in its fiscal year ending in January 2024.

Nvidia owns around 90% of the GPU market, but Huan clearly wants to understand trends in AI applications and use cases while thinking ahead about what architectural decisions will create the next opportunities for his company.

3. Stay product-driven

Maintaining a product-driven culture inside a company is always crucial for innovation, and no company exemplifies that better than Intel. For years, the company was an undisputed industry leader and employed some of the smartest engineers in the industry. Under Grove, it operated under a mantra of “productive paranoia”: constantly anticipating potential market threats and using that fear to help drive innovation quickly and seize new opportunities before they’re apparent to everyone else.

Grove dramatically shifted Intel’s direction from dynamic random-access memory (DRAM) chips to microprocessors in the mid-1980s, for example, which represented a huge and risky shift for the company. But Grove’s intuition, like Huang’s with his graphics chips, was correct. Years later, in my view, under subsequent CEOs, Intel’s leadership became increasingly focused on sales and market protection rather than innovation.

Today’s AI upstarts can learn from these decades-old product shifts. I firmly believe that most of the value from AI will come at the application and middleware layer, and that raising hundreds of millions (or billions) of dollars to compete at the AI foundation layer with much larger tech companies (Microsoft, Facebook) is a losing game. Getting as close as possible to your end customer, and seeing how they’re deriving value from your specific application, is the way to go.

The next few years will determine which AI companies can learn from Intel’s experience and which will repeat its mistakes. For founders building in this space, the message is clear: Your current success is just the beginning. Stay hungry, stay close to your customers, and never stop innovating. The market won’t wait for those who don’t.