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Posted on 04/27/2026 1:24:38 PM PDT by nickcarraway
Peter Thompson is co-founder and CEO of LucidLink, the cloud-native file streaming platform that enables instant, secure access to large files by streaming data on demand, allowing teams to access and edit complex filesets without downloading or syncing.
I was 48 years old when I left my job and enrolled in the Entrepreneurial Studies program at Stanford.
Most people at that stage of their careers are trying to reduce risk, not introduce it. They have steady income. They have dependents. In tech, the unspoken assumption is that if you were going to take a big swing, you should have done it already. I decided to swing anyway.
For most of my career, I watched Silicon Valley celebrate a particular kind of ambition: the kind that belongs to the young. We applaud founders who drop out of school, and prodigies building in dorm rooms. Those stories are real and extraordinary. But beneath them is a quiet counter-narrative: the idea that reinvention later in life is unusual, and that seasoned operators who know an industry’s flaws intimately and set out to fix them are somehow the exception.
When I speak with seasoned executives considering school or startups, the hesitation is rarely about ability or potential. It’s about perception. Risk after 40 is more often dismissed as a “midlife crisis” than embraced as a calculated choice. That’s not just unfair—it’s economically shortsighted.
What Two Decades in the Industry Taught Me
Before Stanford, I spent decades in enterprise storage. Early in my career, I joined a small company and was sent to help expand the business across Asia Pacific. I had to sit across from customers in markets like Japan and speak as the company’s storage expert—except I wasn’t, not at the beginning. I had to learn fast. I had to admit what I did not know. There were plenty of moments where I was right at the edge of my capability.
Over time, those uncomfortable moments compound. One day you wake up and realize you actually do understand the system. You know why certain architectures fail—you have seen enough cycles to recognize patterns. By my late forties, I had that pattern recognition on autopilot. What I no longer had was the spark that discomfort once fueled.
A friend who had gone through the Sloan Fellowship at Stanford suggested I apply. His advice was simple: put yourself back in an environment where you are not the expert, and be deliberate about what comes next.
I applied. I was accepted. I was the oldest person in the program.
Shortly after the program began, I received a call from an engineer I had worked with years earlier. He had developed a new approach to cloud file access that challenged deeply held assumptions about how storage systems needed to work. He showed me a prototype that defied what conventional wisdom said was possible.
At 28, I probably would have rushed in. At 48, experience pushed me to slow down and test it from every angle before moving forward. We spent months pressure-testing the idea before fully committing. After graduation, we started pitching investors and were rejected 33 times. That’s not easy, but I had watched enough cycles to know that investor consensus and customer reality are not always aligned. We kept on.
The conviction to persist did not come from blind optimism. It came from having watched this problem surface repeatedly over two decades. I had seen the clunky workarounds. I had sat through the budget conversations. I knew this pain was structural, not temporary. Eventually, we found an investor who saw it the same way.
Today, LucidLink serves thousands of companies—including Paramount, Adobe, Shopify, and Spotify—and has grown into a global business last valued in 2023 at $390 million. We won an Emmy last year for transforming the way entertainment gets made.
I do not tell this story to suggest that starting a company at 48 guarantees success. It does not. I tell it because that company would not exist if I had accepted the commonly held idea that my window had closed.
Why This Is a Business Problem, Not a Cultural One
As AI reshapes white-collar work, more professionals will reach inflection points. Some will be displaced. Others will realize that the roles they mastered are evolving faster than expected. Economic pressures are simultaneously pushing many to extend their working lives. Later-stage reinvention will become more common, not less. The question is whether the tech ecosystem treats that reinvention as an asset or a liability.
Age bias is usually framed as a cultural problem. It is also a business problem. We lose out when experience is dismissed. When later-stage operators are subtly discouraged from building, we narrow the range of problems being addressed. In industries like infrastructure, healthcare, media, and enterprise software, depth matters. Pattern recognition matters. Having lived through downturns matters.
This is not an argument against young founders. Many transformative companies were built by people in their twenties. It is an argument against assuming that innovation belongs to a single demographic. Ambition doesn’t expire. Experience, combined with a willingness to be uncomfortable again can be a competitive advantage.
If we want the next generation of companies to solve harder, more systemic problems, we should normalize career reinvention at every stage. Not because it feels inclusive, but because it makes economic sense.
Some of the most important companies of the next decade will be built by people who have already had one or two careers. The real risk is not that they try and fail. It’s that they decide, before they even begin, that they’ve already missed their moment.
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AI Overview
Yes, COBOL is still heavily used, particularly in banking, insurance, and government sectors. It acts as the backbone for over 95% of ATM transactions and 80% of in-person banking. Despite being over 60 years old, it runs massive batch processes due to its efficiency with large-scale data and its reliability.
Why is COBOL Still Used?
Massive Infrastructure: Over 200 billion lines of code are still in active use.
Banking Backbone: About 43% of banking systems, including top banks, still depend on it.
Cost of Change: Rewriting these systems is expensive and risky, making maintenance of old systems more practical.
Reliability: It is extremely stable and efficient at processing high-volume transactions, such as for the IRS.
Continual Evolution: It is not dead code; COBOL standards were updated as recently as 2023.
I thought Python was relatively slow.
I have found it perfectly fast for many of my data science applications. My co-workers took on a challenge over the Christmas holidays. We had all the satellite orbit data for 20 years and all of the AIS voyage logs for the same time frame. The task was to identify every time a ship successfully sailed outside the surveillance footprint of the satellites. We did it. It was plenty fast against an enormous volume of data.
How long ago did you learn it? It’s not usually taught now. There are consultants that make a lot of money working on these mainframes, but the use isn’t widespread.
How long ago did you learn it? It’s not usually taught now. There are consultants that make a lot of money working on these mainframes, but the use isn’t widespread.
Ditto but I retired from IT after 46 years.
Absolutely ... 100%. I've had a similar career, and the American professionals I've worked with were head-and-shoulders superior to the lackluster non-American droids they're depending on nowadays. That's because tech is built into the DNA of America, and nowhere else is that the case (well, I have met some bright Russians along the way).
optobservatory.org
9 out of 10 new jobs in U.S. since 2020 have gone to foreigners:
https://freerepublic.com/focus/news/4376285/posts
See Congressman Eli Crane’s “End H-1B Visa Abuse Act of 2026”
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