Latest / EDGE AI POD / The Pipeline Is Stalling: America's Declining Innovation Edge

Transcript

The Pipeline Is Stalling: America's Declining Innovation Edge · EDGE AI POD

• 0:07
  Speaker 1: Welcome to the Deep Dive.
• 0:08
  Speaker 1: Today we're getting into something really critical for us the state of our innovation pipeline, Especially in community communities.
• 0:18
  Speaker 2: Our guide here is a person by the name of Greg.
• 0:21
  Speaker 2: The pipeline is strong.
• 0:23
  Speaker 2: Why America is the pipeline, thank you.
• 0:58
  Speaker 2: So let's jump in.
• 0:59
  Speaker 2: Professor Reddy talks about this historical model, this foundation for US innovation.
• 1:03
  Speaker 2: What were the main parts?
• 1:03
  Speaker 2: Okay, yeah, he lays this historical model, this foundation for US innovation.
• 1:05
  Speaker 2: What were?
• 1:05
  Speaker 1: the main parts.
• 1:05
  Speaker 1: Okay, yeah, he lays out three pillars essentially.
• 1:07
  Speaker 1: First was sustained federal money for basic research, the really fundamental stuff.
• 1:13
  Speaker 2: Right, not necessarily aiming for an immediate product, but building that knowledge base.
• 1:17
  Speaker 1: Second, strong links between universities, public institutions and private industry, Getting ideas from the lab out into the world.
• 1:25
  Speaker 2: Uh-huh, and the third.
• 1:26
  Speaker 1: An open door, basically Welcoming international talent, the best minds from everywhere to study and work here.
• 1:33
  Speaker 2: Okay, so those three pillars were key, but the article argues they're well, they're under pressure now.
• 1:40
  Speaker 1: Yeah, pretty much right away.
• 1:41
  Speaker 1: Let's take federal funding first.
• 1:42
  Speaker 1: The headline numbers might look okay, but there's more to it.
• 1:46
  Speaker 2: Oh, absolutely.
• 1:46
  Speaker 2: It's that classic nominal versus real value thing.
• 1:49
  Speaker 2: So the amount of funding nominally went up between 2014 and 2024 by about 21, 22%, but factor in inflation what those dollars actually buy.
• 1:59
  Speaker 2: That figure dropped by almost 10% in real terms.
• 2:02
  Speaker 2: The article's figure four shows it starkly.
• 2:04
  Speaker 2: Over that decade, researchers effectively lost about 26% of their buying power.
• 2:10
  Speaker 1: That's huge.
• 2:10
  Speaker 1: So more dollars, but they don't go nearly as far.
• 2:12
  Speaker 1: What does that mean on the ground, in the labs?
• 2:15
  Speaker 2: Well, it means tough choices.
• 2:16
  Speaker 2: The article mentions fewer slots for graduate students, putting off buying necessary new equipment.
• 2:22
  Speaker 1: Makes sense.
• 2:23
  Speaker 2: And maybe shifting focus to safer, shorter-term projects that are more likely to get funded quickly.
• 2:29
  Speaker 2: Plus, getting grants from places like the NSF or NIH is already really competitive low success rates.
• 2:35
  Speaker 2: This just tightens the screws.
• 2:38
  Speaker 1: Right, okay.
• 2:39
  Speaker 1: What about the second pillar, public-private?
• 2:43
  Speaker 2: collaboration.
• 2:44
  Speaker 2: How's that changing?
• 2:44
  Speaker 2: The shift seems to be towards more transactional relationships, more short-term goals.
• 2:50
  Speaker 1: Less like the old Bell Labs model or the early days of the SRC, the Semiconductor Research Corporation.
• 2:55
  Speaker 2: Exactly.
• 2:56
  Speaker 2: There used to be perhaps more industry willingness to invest in that fundamental pre-competitive research, the stuff that lifts the whole field.
• 3:04
  Speaker 2: Now, it seems, the focus is often on things closer to market.
• 3:07
  Speaker 1: Which could mean that really basic, foundational science gets less support from industry.
• 3:11
  Speaker 2: That's the concern, yeah, and that foundational work is well fundamental.
• 3:15
  Speaker 1: And the third pillar, international talent.
• 3:17
  Speaker 1: This feels incredibly important, especially for STEM fields.
• 3:21
  Speaker 2: It really is.
• 3:21
  Speaker 2: The numbers in the article are quite striking.
• 3:24
  Speaker 2: Like, 79 percent of computer science grad students are international 79 percent.
• 3:29
  Speaker 2: And 81 percent in electrical engineering.
• 3:31
  Speaker 2: These students and scholars are, you know, a huge part of the research engine in universities.
• 3:36
  Speaker 1: And their impact goes way beyond academia, doesn't it?
• 3:39
  Speaker 1: The article talks about founders, ceos, oh, definitely.
• 3:42
  Speaker 2: Something like 55 percent of US billion-dollar startups unicorns had immigrants as founders or co-founders.
• 3:49
  Speaker 1: You think of Google's Sergey Brin, Microsoft's Satya Nadella, Alphabet's Sundar Pichai, NVIDIA's Jensen Huang?
• 3:56
  Speaker 1: The list goes on.
• 3:58
  Speaker 2: Right and even in the latest AI boom OpenAI, databricks, anthic.
• 4:02
  Speaker 2: Figure three in the piece shows immigrant co-founded AI companies pulling in vastly more funding, like $118 billion versus $7 billion for others as of early 2025.
• 4:12
  Speaker 2: It's a massive contribution.
• 4:13
  Speaker 1: So putting up barriers to this talent.
• 4:15
  Speaker 1: The article calls it national self-sabotage.
• 4:17
  Speaker 1: Pretty strong words.
• 4:18
  Speaker 2: It is strong, but you can see the logic.
• 4:20
  Speaker 2: There's even research cited suggesting that for every three international students we educate, an American job is eventually created.
• 4:27
  Speaker 1: So making it hard for them to stay isn't just bad for innovation, it's potentially bad for the economy overall.
• 4:33
  Speaker 2: That's the argument.
• 4:34
  Speaker 2: We invest in educating them, then potentially push that talent away.
• 4:38
  Speaker 1: Professor Reddy also makes a point that this innovation system isn't just, you know, harvard and MIT, it's much wider.
• 4:44
  Speaker 2: Absolutely Crucial point.
• 4:46
  Speaker 2: It's a whole ecosystem Land-grant universities, HBCUs, community colleges, state flagships they're all part of this.
• 4:55
  Speaker 1: And they all rely on that federal support we were talking about.
• 4:58
  Speaker 2: Largely yes, for research, for training students who then go into industry.
• 5:02
  Speaker 2: And university endowments even the big ones, can't just step in and cover everything.
• 5:07
  Speaker 2: There are rules about how that money can be used.
• 5:10
  Speaker 1: So a dip in federal funding doesn't just hit the top tier, it ripples out everywhere.
• 5:14
  Speaker 2: Exactly.
• 5:14
  Speaker 2: The whole pipeline can get sluggish if the funding isn't there.
• 5:17
  Speaker 2: It's all interconnected.
• 5:18
  Speaker 1: The article also mentioned how the government's share of R&D funding has changed over time.
• 5:23
  Speaker 2: Yeah, quite dramatically.
• 5:24
  Speaker 2: By 2020, that was down to about 21 percent and universities themselves are picking up more of the tab, about 25 percent in 2021.
• 5:37
  Speaker 1: So less real federal money and a smaller share of the total pie.
• 5:41
  Speaker 1: Yeah, that puts a lot of pressure on universities.
• 5:44
  Speaker 2: A double whammy, in a way.
• 5:45
  Speaker 1: And then there's the whole geopolitical angle, restrictions on international collaboration yeah, that must be tricky to navigate.
• 5:51
  Speaker 2: Oh, it's a real balancing act.
• 5:52
  Speaker 2: You obviously need security, no-transcript university links and big corporate R&D spending.
• 6:27
  Speaker 1: What's the common element?
• 6:28
  Speaker 2: Diversified funding seems key.
• 6:29
  Speaker 2: Not relying on just one source, it builds resilience and also strong connections between the players government, academia, industry.
• 6:39
  Speaker 1: OK.
• 6:39
  Speaker 1: So, based on the problems of these international examples, the article lays out a strategy for the US, a multi-pronged approach.
• 6:45
  Speaker 2: Right, it's not just one fix.
• 6:47
  Speaker 2: One idea is expanding industry consortia, kind of like the old SRC for semiconductors, but for new areas.
• 6:54
  Speaker 1: Like an SRC for AI.
• 6:56
  Speaker 2: Something like that Getting companies to pool resources to fund university research in AI, quantum biotech, maybe with federal matching funds.
• 7:04
  Speaker 2: He mentions things like ML Commons and, significantly, the Edge AI Foundation as existing efforts to build on.
• 7:12
  Speaker 1: Ah, ok, the Edge AI Foundation.
• 7:13
  Speaker 1: This connects directly to our Edge AI focus.
• 7:16
  Speaker 1: How does the article see its role?
• 7:18
  Speaker 2: It's highlighted specifically as a model for that needed industry academia collaboration, particularly in Edge AI, and it's worth noting, professor Reddy himself is on their board.
• 7:27
  Speaker 1: Oh interesting.
• 7:28
  Speaker 1: So he's directly involved.
• 7:29
  Speaker 2: Yes, which underscores the importance he places on it.
• 7:32
  Speaker 2: The foundation is creating ways for universities and companies to work together on specific edge AI challenges.
• 7:39
  Speaker 2: Things like neuromorphic computing, tiny ML, physical AI, even generative AI on the edge.
• 7:45
  Speaker 1: So it's a concrete example of strengthening those university industry ties in a critical emerging field.
• 7:52
  Speaker 2: Exactly, it directly addresses that need.
• 7:54
  Speaker 1: Beyond these consortium foundations, what other ways are suggested to boost that connection?
• 7:59
  Speaker 2: Things like expanding industrial liaison programs at universities, creating innovation co-labs where company researchers can physically work on campus.
• 8:07
  Speaker 1: Like embedded researchers.
• 8:08
  Speaker 2: Sort of yeah, and developing more university advanced research parks to cluster corporate R&D near academic hubs, just generally making it easier for ideas and people to flow back and forth.
• 8:19
  Speaker 1: What about money from other sources, like philanthropy or endowments?
• 8:23
  Speaker 2: The article suggests exploring that, maybe matching programs to encourage private donations for STEM research and encouraging universities with large endowments to allocate more internally for riskier exploratory research.
• 8:36
  Speaker 1: But with a caveat.
• 8:37
  Speaker 2: Yes, a caution against over-reliance on corporate linked funding.
• 8:40
  Speaker 2: You still need that stable base of public investment for independence and diversity in research directions.
• 8:47
  Speaker 1: Makes sense.
• 8:47
  Speaker 1: And then there's the talent retention piece keeping the brilliant people we educate here.
• 8:52
  Speaker 2: Crucial.
• 8:52
  Speaker 2: A big proposal is a dedicated STEM green card.
• 8:56
  Speaker 2: Basically, If you earn an advanced STEM degree here, you get permanent residency.
• 9:01
  Speaker 1: That seems straightforward.
• 9:03
  Speaker 2: Conceptually yes.
• 9:04
  Speaker 2: Also expanding H-1B visas for PhD-level researchers, streamlining visa processes for professors and students on exchange, just making it less difficult for talent to stay and contribute.
• 9:16
  Speaker 1: And back to federal funding, the need for stability and supporting basic science comes up again strongly.
• 9:21
  Speaker 2: Absolutely Predictable.
• 9:22
  Speaker 2: Budget increases for agencies like NSF and DARPA are key.
• 9:26
  Speaker 2: Maybe even a dedicated trust fund for science Trust fund Interesting.
• 9:30
  Speaker 1: And really emphasizing support for that fundamental curiosity-driven research.
• 9:35
  Speaker 1: That's where the really big breakthroughs often come from, even if you can't predict them.
• 9:39
  Speaker 1: Plus, looking again at policies that might be overly restrictive on how research funds can be used.
• 9:44
  Speaker 1: The article also mentions new kinds of institutions, hybrid models.
• 9:48
  Speaker 2: Yeah, the idea of US innovation institutes, maybe located near universities but with more flexibility in hiring an IP.
• 9:55
  Speaker 2: They'd focus on bridging that gap between basic university research and corporate R&D.
• 10:01
  Speaker 1: Tackling those medium-term challenges.
• 10:03
  Speaker 2: Potentially yes, along with designating regional innovation hubs to spread the economic benefits more widely.
• 10:10
  Speaker 1: OK, quite a comprehensive list.
• 10:12
  Speaker 1: Let's bring it back specifically to edge AI.
• 10:14
  Speaker 1: How do these big challenges funding talent hit that field?
• 10:20
  Speaker 2: Well, edge AI relies heavily on advances in things like efficient algorithms, new types of low power hardware.
• 10:26
  Speaker 2: That requires basic research.
• 10:28
  Speaker 2: So cuts in real federal funding directly slow that down.
• 10:31
  Speaker 1: And the talent pipeline.
• 10:32
  Speaker 2: Hugely important for edge AI.
• 10:34
  Speaker 2: Like we said, many top researchers in AI and related hardware fields are international students.
• 10:39
  Speaker 2: If we make it hard for them to come here or stay here, we're directly hindering our own progress in edge AI development and deployment.
• 10:45
  Speaker 1: And this is where something like the edgy GA foundation comes in again, exactly, development and deployment, and this is where something like the EDGE, ega Foundation comes in again.
• 10:48
  Speaker 2: Exactly.
• 10:49
  Speaker 2: It's working to directly counteract some of these challenges within the EDGE AI space by fostering those specific industry academia links.
• 10:58
  Speaker 1: Providing those collaboration points.
• 10:59
  Speaker 1: We talked about neuromorphic tiny ML.
• 11:01
  Speaker 2: Right Connecting the university research to the companies that need those breakthroughs for actual products.
• 11:07
  Speaker 2: Professor Reddy's involvement really highlights how crucial these kinds of focused efforts are seen to be.
• 11:12
  Speaker 2: They help translate the research into reality.
• 11:16
  Speaker 1: This has been incredibly useful really mapping out the situation.
• 11:19
  Speaker 1: So, boiling it down, what are the absolute key takeaways?
• 11:23
  Speaker 2: I'd say first, the US innovation system is facing real strains on those three pillars.
• 11:28
  Speaker 2: Federal funding isn't keeping pace in real terms.
• 11:31
  Speaker 2: Industry collaboration is shifting and attracting and retaining global talent is becoming more challenging.
• 11:37
  Speaker 1: And these aren't separate problems.
• 11:38
  Speaker 1: They're all linked.
• 11:39
  Speaker 2: Totally linked.
• 11:40
  Speaker 2: Second fixing.
• 11:41
  Speaker 2: This requires a coordinated effort.
• 11:43
  Speaker 2: Government, industry, academia all need to be involved.
• 11:47
  Speaker 2: It's not just one group's problem.
• 11:49
  Speaker 1: And specific actions are needed.
• 11:51
  Speaker 2: Sustained investment in basic research, smarter policies for international talent and really building those strong public-private partnerships, like the work being done by the Edge AI Foundation in its specific area.
• 12:04
  Speaker 1: For you listening.
• 12:05
  Speaker 1: We hope this deep dive has given you a solid framework for thinking about this complex ecosystem.
• 12:10
  Speaker 1: It's about understanding the forces at play without getting lost in every single detail.
• 12:15
  Speaker 2: Yeah, and maybe a final thought to chew on how could a really thriving edge AI ecosystem supported by these kinds of changes impact your world, your work?
• 12:24
  Speaker 1: Right.
• 12:25
  Speaker 1: What role could different people or organizations play individuals, companies, policymakers in making sure that innovation pipeline for edge, ai and beyond stay strong?
• 12:35
  Speaker 2: It's something worth considering.
• 12:36
  Speaker 1: If you want to explore more, the article mentions the People's Pledge for American Higher Education.
• 12:40
  Speaker 1: You can check that out at bitly higher end pledge.
• 12:43
  Speaker 1: Thanks for joining us for this deep dive.