The Future of Innovation & The End of Academic Monopoly

In today’s landscape, companies—not universities—are becoming the core engines of innovation.

Well, at least in the U.S.

The Internet Has Democratized Knowledge

Let’s face it: the rise of the internet has democratized access to knowledge.

Information is cheap and everywhere. Anyone with drive and curiosity can learn anything online. As a result, academia’s monopoly on expertise is no longer as effective.

A 2023 report from the National Student Clearinghouse found that computer science and engineering enrollments in U.S. universities have plateaued, while coding bootcamps and industry certifications (e.g., Google or AWS) have seen explosive growth (National Student Clearinghouse Research Center, 2023). This suggests that many aspiring technologists are bypassing universities altogether.

If you’re a self-directed learner, the internet can turn you into an expert in almost any field. That means when it comes to theoretical knowledge, academia doesn’t hold the same edge it once did.

This isn’t to discredit the work of academics or higher education institutions.

After all, it is an undeniable fact that universities still produce groundbreaking research, create an environment for deep, intellectual collaboration, and provide structured learning and mentorship for those who need the time to cultivate their research skills in a dedicated workplace. These are all critical factors that boost our society’s research advancements, which are the core of innovation. I believe universities remained and will continue to be vital for foundational science and critical thinking, but when it comes to moving ideas from the lab to the market, the private sector is pulling ahead…

The Shift to Applied Innovation

Hot take #2: We might be approaching the limits of traditional theoretical science.

A 2023 analysis in Nature found that scientific papers and patents are becoming increasingly incremental rather than revolutionary (Park et al., 2023).

The low-hanging fruit of 20th-century science (semiconductors, antibiotics, nuclear energy) has been picked. As a result, harder, niche problems that require more industry collaboration are left behind.

There just aren’t many paradigm-shifting breakthroughs left that can radically transform manufacturing or other industries. The real innovation today is happening on the ground—solving hard, practical engineering problems. And that’s where startup teams and industry engineers, who are in the “trenches”, have a major advantage over academics. Companies like Apple, NVIDIA, and Moderna now invest more in R&D than many governments. In 2023, Amazon spent $73 billion on R&D — that’s more than France’s entire public research budget (Statista, 2023; OECD, 2023)! Many transformative technologies, like OpenAI’s GPT models and Tesla’s autonomous driving systems, were developed in industry. A 2022 Stanford study found that 80% of AI breakthroughs originate in corporate labs (Zhang et al., 2022). Meanwhile, university research struggles with funding gaps and slow tech transfer processes (Britt, 2023).

This might be more of a dire issue, especially in the U.S., given the current funding situation in 2025.

VC Demands Execution. Not Just Ideas.

Hot take (maybe not really a hot take) #3: An idea or a patent from an academic project will no longer be sufficient as the driving force behind a successful startup.

The shift to applied innovation, coupled with tightening U.S. funding landscapes, explains why academic projects struggle to attract investment. I define early-stage academic projects this way: These projects are research endeavors in their initial phases of exploration, where the focus is on hypothesis testing, foundational discovery, or proof-of-concept development. These projects are characterized by high uncertainty, minimal preliminary data, and no seemingly immediate path to commercialization to the public and/or investors.

A good, maybe even exceptional example of an academic project that succeeded in securing funding from VCs would be Moderna.

Moderna’s core technology — messenger RNA (mRNA) — originated in academic research at institutions like MIT, Harvard, and the University of Pennsylvania.

Initial academic work for Moderna’s core innovation relied on grants from agencies like the NIH. However, funding stagnation in the 2000s–2010s limited progress. For example, Karikó’s grant applications were repeatedly rejected, forcing her to pursue smaller, niche funding sources. Despite these circumstances, Moderna’s pivot to applied innovation, developing mRNA as a therapeutic platform, and efforts in raising funding from the 2000s to the 2010s, allowed it to secure 4.9 billion in venture capital support by 2015 and later dominate the COVID-19 vaccine market, generating 18.5 billion in 2021.

However, this is a success that’s harder to replicate in 2025.

Federal agencies now favor “use-inspired” research aligned with immediate societal needs (e.g., AI, pandemic preparedness), leaving curiosity-driven, academic projects underfunded. Only 33% of university research funding is projected to come from federal sources by 2025, down from 60% in 2000.

Additionally, the shift to commercialization-driven endeavors in R&D investment is real. According to a 2025 academic research and development global market report, private R&D investment now constitutes 73% of total U.S. R&D spending, but businesses focus overwhelmingly on applied research (55%) and experimental development (87%). These are all areas closer to commercialization. For instance, cleantech investors favor energy storage or EVs over speculative academic research in renewable energy. VCs and private equity increasingly target ventures with clear market pathways (e.g., AI software, healthcare spinouts), this, again, creates a disadvantage for early-stage academic projects underfunded. A lot of seasoned VCs aren’t even touching angel or Series A rounds anymore.

The bar is simply higher.

Founders are expected to get scrappy, find early traction, and get through the A round before serious capital even considers coming in. Investors want to see a clearer picture before they write a check. Data from PitchBook (2024) shows that seed-stage funding has grown, but Series A rounds have become far more selective (PitchBook-NVCA Venture Monitor, 2024). Investors now expect startups to demonstrate revenue, customer adoption, or a clear path to monetization before committing. Someone with a more academically trained background, like a full-time PhD student, might be constrained by their responsibilities in education and struggle with contributing time to their startup endeavors.

This doesn’t mean PhDs or academically trained founders are less likely to get funded, but it’s no longer a strong differentiator in most sectors these days, compared to the early 2010s.

An analysis of 347 U.S. unicorns over the past decade shows that just 18% had at least one PhD founder. Their performance is only slightly better (but not significantly so) than non-PhDs in terms of valuation efficiency and time to $1B. However, in technical sectors like healthcare (44.1%) and AI/ML or big data (31.4%), PhDs are notably overrepresented and tend to build more efficient, high-performing companies. Still, a PhD and a patent alone no longer guarantee a competitive edge in the eyes of modern VCs.

You might be wondering—what’s “enough”? Well, I’m not an experienced investor, so I can’t give you a definitive answer. But based on my analysis of successful founding teams from 2024–2025, here’s what consistently stands out:

  • Clear signs of traction.

  • Know your TAM and SAM, and show that CAC, LTV, margins, and churn trends (once you have enough customer data) support long-term profitability

  • Strong founder–market fit (ideally backed by years of relevant industry experience)

  • Impressive academic credentials to round it out

Put these elements together, and you’ve got the foundation of a standout candidate - at the very least, enough to get investors interested in hearing your pitch.

(People define success in a startup very different. Here, I define it as crossing meaningful revenue benchmarks, e.g., is the ARR $10M+? )

(And, obviously the ones that eventually prepare for an exit. )

(Nowadays, there are way too many startups that have no problem attracting initial investment interest, but fail to prove long-term profitability. They might scale quickly and make good initial revenue, but I personally believe the true success lies in a team that’s able to provide service, deliver value, and stay competitive consistently. That’s a real business.)

A Question Only You Can Answer (for yourself!)

So, given all of my hot takes… For ambitious young innovators, perhaps the more relevant and crucial question to ask is:

What do we do?

Do we get an PhD (or go to grad school in general), or do we jump into working at a company?

If you ask me, the answer depends on the returns you believe a graduate degree would get you.

When to Choose Academia (MS, PhD):

  1. If you want to pioneer fundamental science
    Fields like quantum computing, particle physics, or theoretical AI still require deep academic training. A PhD gives you access to cutting-edge labs (e.g., CERN, MIT Media Lab) and mentors who shape entire disciplines. A good example: CRISPR gene-editing breakthroughs emerged from decades of university research (Doudna & Charpentier, 2014).

  2. If Credentials matter
    If you’re going into fields like biotech or academia, where there is a high demand for PhDs in leadership roles. Get your PhD. Moderna’s mRNA team, for instance, was stacked with PhDs who turned academic insights into a vaccine (Moderna, 2020).

  3. If you enjoy unstructured exploration (or have a track record of thriving in an academic environment)
    PhDs allow you to dive into niche problems without the pressure of quarterly profits. A good example would be the DARPA-funded university project (Leiner et al., 2009). You get more freedom to define your research and carry it out at your own pace. (Obviously, not too slow, at a rate that your advisor’s okay with ;) )

When to Choose Industry:

  1. If you want to solve problems now
    Big things are happening all the time in industry. SpaceX engineers iterated reusable rockets in years, not decades (Berger, 2020). If you want to contribute now, go into industry.

  2. If Execution > theory
    If you value execution more than theory in your daily work, choose industry. A good proof/piece of evidence for this is how OpenAI’s GPT-4 required trial-and-error engineers (OpenAI, 2023). Companies need to get things running ASAP.

  3. If you’re interested in Entrepreneurship
    Founders like Vitalik Buterin (Ethereum) prove credentials aren’t everything. Experience matters as an entrepreneur. Obviously, I put this as a third condition for a reason. Tons of PhDs became successful entrepreneurs, but of course, at the time cost of 5-6 years, whereas you can use the time to jump into industry. Your call.

Finally, things are constantly changing. I don’t think decisions like pursuing a graduate degree is something to be taken lightly considering the time cost and financial implications, however, I also believe that if doing academic research is something you want to do, just for the sake of doing it, you should absolutely pursue it. We only live once after all.

It is your life, so live your life to the fullest, and don’t forget to stay upwind & stay true to yourself when making a career choice.

Best of Luck.

~ Nicole Hao, 2025-05-09, Ithaca, NY.

References

  • Berger, E. (2020). Liftoff: Elon Musk and the Desperate Early Days That Launched SpaceX.
  • Britt, R. (2023). “U.S. Science Funding Faces Political Headwinds.” Science Business.
  • Doudna, J. A., & Charpentier, E. (2014). “The new frontier of genome engineering with CRISPR-Cas9.” Science.
  • Leiner, B. M., et al. (2009). “A Brief History of the Internet.” ACM SIGCOMM.
  • National Student Clearinghouse Research Center. (2023). “STEM Enrollment Trends.”
  • OECD. (2023). Main Science and Technology Indicators.
  • Park, M., et al. (2023). “Papers and patents are becoming less disruptive over time” Nature.
  • PitchBook-NVCA Venture Monitor. (2024). Q1 2024 Report.
  • Statista. (2023). “Amazon R&D Spending 2023.”
  • Zhang, D., et al. (2022). Stanford AI Index Report.

Copyright © 2025 Nicole Hao. All rights reserved.

Written on May 9, 2025