Editor's Note: Against the backdrop of sustained investment in AI infrastructure, discussions within the semiconductor industry are shifting from "whether GPU supply is sufficient" to "whether the entire computing and manufacturing system can support the next stage of AI expansion." Over the past two years, the market has been more focused on models, compute clusters, and the NVIDIA ecosystem. However, as the long-term growth of AI demand gradually becomes a consensus, a more critical question emerges: If chips, packaging, power, materials, memory, and manufacturing capacity all become bottlenecks simultaneously, what kind of new semiconductor supply chain does the AI industry truly need?

This episode of "No Priers" invited Intel CEO Chen Lifu to discuss Intel's transformation, U.S. domestic manufacturing, foundry business, AI's renewed pull on CPU demand, and new manufacturing collaborations like TerraFab. Chen Lifu is both a long-term semiconductor investor and an industry operator from Cadence to Intel. Therefore, the value of this conversation lies not in presenting a single company narrative, but in showcasing how an industry expert reinterprets the semiconductor structure of the AI era.

In this conversation, Chen Lifu deconstructs "How Intel Can Revitalize" into a set of more fundamental structural questions: How to repair the balance sheet, how to refocus the product line, whether advanced manufacturing can return to the U.S., whether AI workloads will redefine CPU value, and how semiconductor investment should revolve around real bottlenecks.

First, the issue with Intel has shifted from "product lag" to "organizational and capital structure reconstruction." In the past, external discussions about Intel often centered on process delays, GPU absence, and lack of foundry competitiveness. However, Chen Lifu first emphasizes not a specific product generation, but the balance sheet, organizational culture, and customer trust. He proposes a path: first "crawl," then "walk," finally "run": first strengthen the financial foundation, simplify the product line, bring engineering teams closer to the CEO and customers, then gradually rebuild the roadmap. This means Intel's revival cannot be achieved with a single product launch; it is a systemic repair of organizational speed, capital patience, and technical roadmaps.

Second, the demand for compute structure by AI is becoming more complex. In the past, the AI narrative was almost dominated by GPUs, with training clusters forming the clearest consensus in capital markets. However, Chen Lifu points out that with the development of Agentic AI, reinforcement learning, multi-agent scheduling, and edge computing, CPUs are becoming important again. The CPU-to-GPU ratio may shift from 1:8 in the training era to 1:4, even approaching 1:1 in some scenarios. This means AI infrastructure won't have just one chip winner; future competition will increasingly revolve around system-level combinations for different workloads: CPU, GPU, NPU, advanced packaging, software stacks, and foundry capabilities will all become part of the same compute network.

Third, semiconductor manufacturing is shifting from a business efficiency problem back to a national infrastructure problem. Over the past three decades, global chip manufacturing, driven by efficiency, became highly specialized, with advanced manufacturing capabilities concentrated in a few regions and companies. However, supply chain shocks, AI capacity demand, and geopolitical risks make "relying on players in only one or two geographic regions" increasingly unsustainable. Chen Lifu compares the U.S. government's stake in Intel to the early relationship between TSMC and the Taiwanese government, pointing towards a new industrial policy consensus: for capital-intensive, long-cycle, strategically critical manufacturing systems, governments, sovereign funds, and long-term capital will once again become key participants.

Fourth, the logic of semiconductor investment is shifting from "betting on hot sectors" to "identifying real bottlenecks." The keyword Chen Lifu repeatedly mentions is not valuation, but bottleneck: interconnect, photonics, EDA, advanced packaging, power conversion, thermal management, new materials, memory, helium, electricity—all could become constraints in the AI expansion process. In the past, semiconductor investment was avoided by VCs due to high capital expenditure, long tape-out cycles, and high customer switching costs; now, as AI demand pushes these bottlenecks to the forefront, semiconductors are once again an area of joint focus for venture capital, strategic capital, and industrial capital. This means truly valuable investment lies not in simply chasing "AI concepts," but in judging which link is becoming the constraint for the next round of expansion.

Fifth, future computing won't exist only in hyper-scale data centers. The SaaS and cloud computing era established a highly centralized computing paradigm, but robotics, defense, home devices, Physical AI, and Agentic AI are making endpoint and edge computing important again. Chen Lifu does not deny the continued expansion of large AI data centers, but he is more concerned with what applications this infrastructure ultimately serves. In other words, compute capacity building only creates long-term value when combined with sustainable, large-scale applications. This also means the next stage of AI competition is not just about "who builds more data centers," but "who can connect compute, chips, and application scenarios into an expandable system."

If this conversation is compressed into one judgment, it is this: AI is pushing semiconductors from single-chip competition towards a comprehensive restructuring involving supply chains, capital structures, manufacturing capabilities, and system architecture. In this sense, the subject of discussion in this article is not just whether Intel can revitalize, but whether the computing infrastructure of the AI era needs to be redesigned from scratch.

Below is the original content (reorganized for easier reading and comprehension):

TL;DR

· The bottleneck of AI is no longer just GPUs, but industrial system constraints constituted by power, memory, packaging, materials, and manufacturing capacity.

· The key to Intel's revival lies not in point product counterattacks, but in the systemic repair of the balance sheet, engineering culture, customer trust, and product roadmap.

· CPUs are important again not because the GPU narrative is cooling, but because Agentic AI, reinforcement learning, and multi-agent scheduling are creating new compute load structures.

· Semiconductor foundry is not just a manufacturing business; it's a trust business. Before delivering wafers to customers, they must first trust that yield, cycle time, and reliability won't destroy their revenue.

· The signal of TerraFab is that AI demand growth is so rapid that leading customers are beginning to proactively intervene in upstream manufacturing infrastructure, rather than passively waiting for chip supply.

· The U.S. rebuilding advanced chip manufacturing relies not just on factory subsidies, but on the recombination of government capital, long-term funds, industrial customers, and manufacturing capabilities.

· The core of semiconductor investment is not chasing hot concepts, but identifying the real bottlenecks limiting industry expansion, such as interconnect, power, thermal management, packaging, and new materials.

· Future AI competition won't just happen in hyper-scale data centers; edge devices, robotics, defense, and Physical AI will push compute back to the application site.

Original Compilation

Host:
Hello everyone, welcome back to "No Priors." Today, Elad and I are joined by Chen Lifu. He previously worked at Walden, later served as CEO of Cadence, and is now the CEO of Intel. We talked about his plan to transform Intel, the U.S. government becoming a significant shareholder in Intel, how to be an excellent semiconductor investor, and whether we can actually manufacture chips in the U.S. Welcome, Chen Lifu.

Why Did Chen Lifu Take on Intel?

Host:
Chen Lifu, great to meet you. Let's start with the most direct question: Intel is an extremely important U.S. semiconductor company, but the CEO role is very challenging. Why did you still take this job?

Chen Lifu:
That's a good question. I'm 66 years old this year. Many people would say you should retire, not take on the hardest job in the industry. I did it for a few reasons. First, Intel is an iconic company. It's very important to the semiconductor ecosystem and to the U.S. So I decided, after Cadence, to do one last thing.

Host:
A lot has happened over the past year. What surprised you the most?

Chen Lifu:
What surprised me most was something my previous work experience and training never taught me: One early morning, President Trump asked me to resign, saying there was a conflict of interest, with no exceptions.

So I had to first convince myself: First, I don't need this job. I took it purely to save Intel. Therefore, I set aside personal issues and thought about what I could do to help Intel.

The good news is, I scheduled a meeting for Thursday morning and met him on Monday. He was willing to listen. I told him I was born in Malaysia, grew up in Singapore, later went to MIT, and have lived in the U.S. long-term. I have never lived in a country outside the U.S.

I told him all this, he listened very seriously and gave me the opportunity. So I'm very happy.

Host:
Now you have the chance to really get to work. You just said the goal of this job is "to save Intel." In your view, what does it concretely mean for Intel to win again, to prosper again?

Chen Lifu:
I've been in this role for 14 months now. A lot has happened in these 14 months.

First, changing the culture. Clearly, we need a stronger sense of accountability. Second, decisions must be faster. I'm very accustomed to startup culture: moving at the speed of light, no bureaucracy, no layers upon layers of meetings.

So the changes I pushed include: strengthening accountability, listening to customers, satisfying customers. Some customers say Chen Lifu is humble, willing to listen, willing to solve their problems, and strives to satisfy customers.

Also, from day one, I decided to have all engineers report directly to me. I'm an engineer by background. I want to know exactly what's wrong, what needs fixing. I want to listen to customers, satisfy them, and ensure we have the right products, simplify the product line, while having a clear roadmap and vision for the next five to ten years.

Intel's Vision for the Next Decade: First Save the Balance Sheet, Then Rebuild Products

Host:
What is your vision for Intel over the next decade?

Chen Lifu:
I think there are several things. First, whether at Cadence or Intel, I've always believed: first learn to crawl, then stay humble, listen to customers; second step, start walking; finally, start running, sprinting. That's my culture: step by step.

For me, the first step is strengthening the balance sheet. Intel's balance sheet was in some ways very poor. So I was glad to see the U.S. government become a significant shareholder.

As I explained to President Trump, when TSMC started, it also had the Taiwanese government as a shareholder. Look at Japan, Singapore; semiconductors are essentially infrastructure, and the U.S. government needs to provide support.

Second, I was also glad my old friend Jensen Huang invested $5 billion to support me. I'm glad I did at least some things right. His $5 billion investment has now turned into $25 billion, or even more.

Also Masayoshi Son from SoftBank. I used to be on SoftBank's board, and he also reached out to help me. So first we strengthened the balance sheet, then focused on products. I significantly simplified the product line, listened to customers, and pushed for next-generation products with leadership.

In a way, we're also lucky. Now with the rise of Agentic AI, CPUs are in high demand. In the past, in training scenarios, the CPU-to-GPU ratio might have been 1:8. Now I see it could become 1:4, even 1:1. I'm glad CPUs are important again.

I've talked to some AI model developers. They say in reinforcement learning, and the speed of scheduling many agents, CPUs actually have an advantage. So in a way, I'm glad the market demand for my CPUs is very high.

Overall, we need to push on the product front, especially in data center servers. Another part is our wafer foundry business.

Initially, this is a capital-intensive business, not easy. You need several conditions. You need the right IP to support customers. For example, if a customer is doing mobile-related products, you must have low-power IP. Without these capabilities, you can't serve them.

Foundry is a service business and a trust business. If a customer gives you orders, gives you wafers, but yield is poor, their revenue suffers, they might even miss opportunities.

So, for us, it's very important to focus on yield, defect density, cycle time, and ensure we can meet customer needs with high quality and reliability, serving customers. These are things I really focus on.

Ultimately, you also have to go full-stack. Not just silicon, but also software. Some customers directly ask me: Can you give me whole-rack systems? This means you must build systems. So we are quietly advancing these things step by step, recruiting the best talent wherever possible.

By the way, I personally handle all hiring, no headhunters. So sometimes, having a strong network, knowing who to call, is very helpful.

Host:
You've been in this industry a long time. You were CEO of Cadence, I remember for about 12 years?

Chen Lifu:
13 years.

Host:
13 years. Then two more years as Executive Chairman, so 15 years total.

Chen Lifu:
I initially agreed to do it for only three months.

Host:
Three months?

Chen Lifu:
Yes. So now I'm very careful. Once you say "I'll only do three months," it might end up being 15 years.

What is TerraFab? Why Does Musk Want to Build His Own Fab?

Host:
It seems you also have a long road ahead at Intel. Another widely discussed major project is TerraFab and your collaboration with Elon Musk. Can you talk more about how this project formed? What is your involvement? How do you collaborate?

Chen Lifu:
Of course. Elon Musk, I think we all agree, is one of the best entrepreneurs of this century, maybe even the best. We share a common judgment: semiconductor infrastructure hasn't actually kept up with AI growth. You need capacity, productivity, and efficiency. These are problems he and I both see: there is indeed a missing link here.

Second, I'm happy to partner with him. He's unconventional. I call it "non-traditional." He questions every step: Why do it the traditional way? In a way, it's very refreshing. I like that. I like working with people with different perspectives, then figuring out the best path together. Both sides learn a lot in the process. Clearly, he also has his own vision: his robots, his cars, need a lot of silicon.

Host:
Can you explain what TerraFab is? Many people might not be familiar.

Chen Lifu:
TerraFab is his decision to build his own wafer fab. Meanwhile, we are happy to work with him, ensuring we can collaborate to help him get into production faster, achieve volume faster, using some of our technology and processes. This is something we are working on together. His team is excellent, I communicate with them weekly. It's very exciting working with them.

Host:
He also mentioned some ideas, like wanting to smoke in the cleanroom, which is usually considered...

Chen Lifu:
Yes, yes, and burgers. I don't think I would go that far. Maybe certain areas of the cleanroom can do it. But the key is to keep an open mind. We'll also listen, see what's feasible.

How is AI Reshaping the Global Semiconductor Supply Chain?

Host:
It's exciting to see you reshaping this company in the U.S.: building the foundry business step by step, while also collaborating with projects like TerraFab. Looking at the global AI and semiconductor supply chain—meaning, if you observe how AI is reshaping supply chains by country in a macro way—you'll find different countries are affected differently.

For example, the talk about AI causing layoffs, I think most are exaggerated now. Many layoffs are actually due to over-hiring during the 2020 pandemic. But I see the first to be cut are often outsourcing companies, because firms prefer to cut external personnel first, not internal staff. So they cut external customer service, external IT. This impacts countries with large BPO industries more in the short term, like the Philippines, India, etc. They might face AI impact.

Asking further, how companies in each country can participate in the future of AI in a positive way almost requires country-by-country analysis. Places with cheap energy can do data centers; places capable of training models can train models, but maybe only the U.S. and one or two other places have that capability.

How do you see changes in the global semiconductor supply chain? Should certain countries invest more? For example, Israel has Mellanox, NVIDIA, Intel presence, should it do more in semiconductors? Should the Philippines return to a manufacturing base? How do you think about these questions from a global perspective?

Chen Lifu:
Good question. Clearly, AI is changing the entire landscape. I think its impact will be larger than the internet, more profound. AI initially helps you do things more efficiently. Many agents can help with tedious but necessary tasks, faster. So it can significantly improve efficiency. Even in semiconductor design, AI improves efficiency, like timing, how fast you can complete a design; second is cost. So these help companies improve efficiency.

There are also several bottlenecks in AI demand and growth. First, of course, is the power constraint everyone knows. Some countries simply don't have enough power, so they'll be affected. Second, many don't realize, helium's impact on the semiconductor industry can also be very significant. Third, as we all know, there's a severe memory shortage now, everyone is scrambling for memory. Even if you want to build fabs to increase capacity, it takes years. CPUs, GPUs are the same, demand will be very high. Prices will also rise because we must pass costs to customers. So all this will affect industry growth.

Overall, I think the companies most impacted are those not embracing AI. Because AI can help companies improve efficiency across functions. We should embrace AI and find better ways to use it, whether for prediction, design, or various workloads. The potential is huge.

Host:
A simple objection many have about TerraFab or Intel's foundry business being competitive actually centers on one question: some factors are inside the fab, like the IP you mentioned, business speed; there are also external factors. Elad also talked a lot about this.

One is labor cost and actual manufacturing capability. You're investing in foundry, obviously believing there's a possibility: we can manufacture domestically. Elon also believes this. Can you talk about this issue? How real is this constraint?

Chen Lifu:
You mean labor constraints?

Host:
Yes.

Chen Lifu:
When I decided whether to double down on foundry or exit foundry, there were many voices in the market. You saw, many said it's too expensive, won't succeed. But I ultimately decided this is very important for the U.S., and for the entire industry.

We've all experienced supply chain challenges. For any large semiconductor company, you must seriously think about supply chain. You need a robust and resilient supply chain, can't rely on just one or two players in different geographic regions.

So I think more and more people will realize manufacturing in the U.S. is critical. And the most advanced processes, like our 14A, about 1.4nm, we're already planning 1nm and 0.7nm. Dimensions keep shrinking, even finer than a hair. So complexity is very high, not easy. If any step goes wrong, you start over. So manufacturing must be very precise.

From this perspective, this will increasingly become a bottleneck. We respect TSMC very much, they're a great partner. More importantly, we both need more capacity to serve customers. So we decided to grit our teeth for the long-term investment. I think long-term, this is very critical, and where I can create more value for the industry.

Host:
People have long discussed that eventually we'll hit some resolution limit, can't shrink further. Linewidths get too small to continue. When do you think we'll truly hit that limit?

Chen Lifu:
Good question. I think now we have 18A, next 14A will go into production, and I can see a path to 10A and 7A. So I think this road can continue. But it will get more expensive and more difficult. That's why we need partners. We can't do it alone. We need to work with material suppliers, equipment vendors to ensure we truly improve yield and performance.

Another part becoming a bottleneck is advanced packaging. Everyone knows TSMC's CoWoS. Now we also have a very good next-generation solution called EMIB. I must ensure it can achieve production yield meeting customer requirements.

Now Moore's Law is also losing steam as you said. So I'm also looking at new materials, going back to the material level, back to the periodic table. I invested in three types of materials: gallium nitride, silicon carbide, and indium phosphide, also observing how these new materials can drive the next step.

In packaging, I started investing in glass. Glass is a good thermal insulator, so I invested in a startup called 3DGS. Later I realized Intel has about a thousand patterns on modules, so how substrates and modules combine is very important.

We just announced a large project with the Indian government for manufacturing in India and New Mexico, U.S. So advanced packaging is very important. I also started looking at synthetic diamonds. It's also a very good insulator. So I also invested in Diamond Foundry. These are next-generation directions to watch. That is, new materials, new substrate materials, and new design methodologies will push the industry forward.

As an engineer, you always hit a wall. But after hitting the wall, you either find a way to jump over or go around, ultimately getting a better result. As someone long-term investing in semiconductors and participating in building the industry, from EDA tools to design to manufacturing, having this experience is actually very helpful. Now I can use my way to make a small contribution to the industry.

The Key to Semiconductor Investment

Host:
What you said is interesting: there are always ways to go around, but there are also physical limits. When you reach scales like 7 angstroms, you hit limits, must find new materials or other detours.

The interesting question is, we've discussed this for a long time. I remember 20 years ago, people said we'd eventually reach a point with no space left on chips. Will you encounter some asymptote where performance differences between different fabs level out?

Chen Lifu:
Good question. Regarding Moore's Law, in the past we pursued performance doubling while also considering power and cost. You can double performance, but cost and area can't maintain the same advantage. So you must make trade-offs in these areas, unless you find new materials, new design methods, and get them to truly land.

I started hiring more talent in material science. This is the innovation focus in our field: how do we continue to advance?

I remember 18 years ago, I was still investing in semiconductors. Back then most VC firms, including some excellent top-tier ones, were good friends. At first in partner meetings, all partners were in the room listening to me talk about semiconductors. Halfway through, half found excuses to leave. The remaining half would ask: Chen Lifu, do you have any software-as-a-service projects? In the end, only two stayed out of sympathy.

So history has changed. Now semiconductors are hot again. Look at Jensen Huang's NVIDIA, a $5.3 trillion market cap company. Broadcom and TSMC are both around $2 trillion. Lisa, my good friend at AMD, company cap near $800 billion. And Intel is close to $600 billion.

So in a way, semiconductors are hot again and crucial. 15, 18, 20 years ago, when I invested in semiconductors, no VCs wanted to invest with me, except large companies like Samsung, Arm, SoftBank. Now I see many VCs willing to invest in semiconductors, so I'm glad.

Host:
Given the huge investor interest in this area now, which was once considered too hard. You are both a long-term operator and a long-term VC at Walden. Generally, people have many concerns about semiconductor investment, I'll list a few: it's capital intensive; tape-out success is unpredictable; you must deeply understand workloads; and customer switching risk is high.

We've both been involved in some companies that may have gotten design wins, but scaling orders remains a question. Also cyclicality: you build heavy asset manufacturing capacity, but demand might change in a given year, or might not.

What's your view on why this industry is hard? At the same time, there's long-term demand growth from different areas now, like awareness of supply chain diversification importance, and explosive demand growth from the AI side. You're still an investor, and now making the biggest bet of your life as CEO. How do you think about these different risks? How would you advise others to invest in this supply chain?

I know it's a big question, but given your experience, I think many might now have a "YOLO investment" mentality: e.g., if memory is short, buy memory stocks; but unwilling to take on things requiring ten-year timelines, like material science.

Chen Lifu: Okay, your question is broad. Let me try to explain.

First, venture capital and entrepreneurship are in my blood, I really enjoy the process. Not to boast, but I do have some good exits. To date, 159 IPOs, 126 M&As, including in semiconductors. For semiconductors alone, I've invested in over 200 companies over the years, 38% in the U.S. So I usually look at micro trends.

Host:
Just to note, that's very impressive.

Chen Lifu:
Thank you, thank you. I just enjoy building these companies. But more importantly, on the investment side, I first look: where is the bottleneck? What problem are you really trying to solve?

For example, I invested in a company called Credo Semiconductor, which has an Australian lab. At the time, I saw interconnect becoming a bottleneck, so decided to support it. I also supported Celestial AI doing optical interconnect. Because within clusters, interconnect speed is becoming more important, so I think optics will be very important. Look at Jensen Huang, he's invested in almost all companies related to photonics.

Also, I look at what solutions the market needs. For example, we just talked about design complexity and cost, can we use AI and machine learning to drive better design and better solutions? Now several new startups are entering the EDA-related field trying to improve performance. I think it's a gold mine.

And new materials. We talked about indium phosphide, so I invested in Inphi, later acquired by Marvell. You can also invest in new materials like gallium nitride and silicon carbide. Some of these companies have started getting acquired, including a power management company called Empower, doing well in IVR.

Power management is now a bottleneck. For example, dropping from 40 volts to 1 volt, you lose a lot of power in conversion, how to improve power efficiency is key. So power, thermal management, these become bottlenecks.

Therefore, I always start from "What problem are we really trying to solve?" Is this problem real? Is the customer truly in pain over it? If yes, I start investing.

The next step is, from day one, lock in the first customer. I usually like the first customer to be a hyperscaler because they have scale. If they like your stuff, willing to pay millions over the next few years, even give purchase commitments. This is important because with a large customer, you can scale.

So I always look at some formulas: How do you do this? Where do you find talent? Sometimes, finding talent is very important. That's why I'm interested in the U.S., Silicon Valley, Austin. Also, Israel has a lot of talent. So I've invested quite a bit in Israel.

Because Israel has many disruptive, innovative entrepreneurs, they work very hard. Even during war, they still take conference calls. Sometimes they say: Okay, there's an alert now, I have to go to the basement, network might be bad, maybe we can only use voice. In a way, it's even a bit funny. I really admire this resilient entrepreneurial spirit.

Overall, I think there are many opportunities, especially in AI. Now besides Agentic AI, physical AI is becoming the next huge frontier. You must look at it from a full-stack perspective.

That's also why I'm still deeply involved in many frontier models and some investments I support, because I'm very bullish on open-source frontier technologies for physical AI. I think that's a gold mine.

Host:
You mentioned the opportunity to use AI to make certain aspects of chip design and testing faster, cheaper, more creative. Combined with your Cadence experience, where do you see the most fertile directions? Is anything already working?

Chen Lifu:
I was at Cadence for about 15 years, I'm glad. One thing I'm proud of is being able to find my successor along the way and mentor him. Later he became an excellent CEO. He embraces AI a lot, using Agentic AI to improve efficiency.

That's the good side. I think Synopsys is also working hard on this. They got $2 billion investment from NVIDIA, I think that helps them a lot. He also acquired Ansys to enter the whole system design field.

Overall, these companies are doing their best. But startups also have opportunities to do something more disruptive, eventually either IPO or get acquired by these two, or Siemens.

So I think opportunities are for everyone, depending on the entrepreneur's vision. My philosophy has always been: if the entrepreneur wants to sell the company, because it's a faster exit path, no lock-up period, no quarterly earnings worries, that's fine. Some entrepreneurs want to IPO from day one.

As VCs, I think the three of us are VCs, we support entrepreneurs' dreams and help them achieve them.

Host:
Looking at these different directions you mentioned, including future product development, or AI's impact on the semiconductor industry, now there are companies like Periodic doing materials, Purepoint doing EDA and design, other links in the manufacturing chain.

Do you think Intel ten years from now, or future semiconductor companies, will be fundamentally different from today because of AI? If so, where are the differences?

Chen Lifu:
I think so. First, back to the issues you mentioned at the start: capital intensity, unpredictability, cyclicality. These must be factored into your investment decisions.

I usually like to enter very early, form a team. It's fun. I think you do this too. Second, find the right investors to co-invest with. Not always looking at brand-name firms, I usually look more at the individual. Who truly understands this area? Most importantly, find partners who can stick through tough times and good times.

Many are happy to work with you when things are good, but when trouble hits, they leave. I like those who truly stay with the company through difficulties. Some successful companies almost went bankrupt multiple times before taking off. So finding partners willing to do that is very important.

Also, look at strategic investors, can they help the company create value in manufacturing, memory, connectivity, etc. I also have friends in growth stage and hedge funds, I like them because they have different perspectives. They understand public markets, can guide entrepreneurs on what paths not to take. This is very helpful.

Overall, it's very interesting. You realize entrepreneurship, like engineering, is about solving problems. At each step, find people who can help you solve the problem. If solved, move to the next frontier.

Frankly, looking back, out of ten companies I've invested in, nine change business plans halfway because the market changed. So I like entrepreneurs being a team, not just one person. Second, they must be open-minded, willing to listen, accept our coaching.

Finally, they form their own plan, not just do what I say. Better is, you give enough feedback, they draw their own conclusions. As long as you agree with their judgment, even if different from yours, you can accept it. That's the fun of entrepreneurship. They can move faster.

Back to your question, looking ten years ahead, what kind of companies will win? This is just my personal view: companies that can articulate strategy clearly, laser-focused on a niche, find the right partners, and have scaling ability will win.

In a way, this goes back to my point about full-stack. You need full-stack solutions. It could be a large company transforming into a large platform. Like Jensen Huang, I admire him. He focused on CUDA, focused on software libraries. He said, I want to be a platform company, and he did it.

It could be startups, like Anthropic, OpenAI, which found paths more elegantly, changed the game. Startups move extremely fast, at the speed of light, and can become dominant.

Hopefully Intel can also play such a role, because we have XPU, NPU, advanced packaging, and foundry. Putting these together, you can build specialized chips for different workloads. I'm moving in that direction.

Host:
That makes sense. Part of my question was to know where you're going, part was asking whether this will fundamentally change how you work. Because in the software world, I see very big changes happening now: who you hire, who you want on the team, many people starting to manage multiple agents.

So now many people I know prefer hiring people in their 30s, 40s, 50s, because they're used to managing teams. They think this directly translates to managing agents, including understanding how to set up complex tasks, do QA, etc.

I'm wondering, in the physical world, or fab environment, how do you see team structure, skill requirements, or changes with AI overlay? Is it a natural slow evolution, or radical change in some areas? E.g., in materials, is it now just using these three models plus some chemistry knowledge? So I'm curious how you see that future world.

Chen Lifu:
Good question. Back to my "crawl, walk, run." In the "crawl" phase, first you recruit the best talent from the semiconductor industry. Now I'm starting to think, to build full-stack, what software talent do I need to bring in?

Currently, my team's average age is around 40s, 50s, I need to bring in some new talent. They understand workloads, understand frontier models, understand open source, that's important.

Now my son has become my teacher. Every time he invites me to his house, we play with grandchildren while I ask him about AI and machine learning. He knows deeper than me, so I learn a lot, also trying to understand investments and bring some talent in.

We are changing Intel. It used to be a very old-school, traditional, spreadsheet-dependent company. Now I'm transforming it into an AI-enabled company, using AI in design, making the entire organization embrace AI. So it won't rely so much on spreadsheets and manpower.

You must combine excellent talent with the best AI tools, not just for organizational management, not just for sales, now I'm also considering marketing, design, all embracing AI.

Host:
I think many investors, at least for me, in recent years after starting my own company, thinking about different funding sources for capital-intensive companies has been a very educational process.

I used to do a lot of software investing. If you say I need $150 million before hitting some critical scale, then you need some very smart friends with completely different balance sheets.

You've experienced this for a long time. You also have unique experience working with government as a large stakeholder. How do you view this industrial policy? It once brought huge successes like TSMC, one of the world's most important companies. But in U.S. business culture, industrial policy has long been unpopular. How do you think this perception should change now? Where does it apply?

Chen Lifu:
Good question. Clearly, for capital-intensive businesses and infrastructure-type projects, you need access to capital. In a way, for early-stage VC, many investments now also become capital-intensive. In the past, a VC willing to put $1 billion into a single company was unheard of in VC, but now it's happening.

So in a way, you must adapt. I like to look at things with a bell curve. Either you enter very early, because now Series A can be over $1 billion valuation, so you must be at pre-seed stage, before company valuation hits $2, $3 billion. This is very rare today, so you must pick right.

The other part is finding capital that helps the company scale. That's why some mutual funds are willing to enter the private market, join me in investing early stage. I welcome them because they are less sensitive to requirements like "must hold 20% of the company." There aren't that many 20% slices left now. So you must find the right investors.

In capital-intensive areas, like AI factories and foundry business, you do need to utilize government funds, sovereign funds, and some very large capital. Now there are large funds specifically supporting infrastructure, and we hope to leverage some of that capital to ensure we can scale operations.

Overall, government and sovereign funds have become very important. Meanwhile as a public company, I also intentionally look for more long-term, growth-oriented investors, because they can help me grow the business, not just focus on short-term capital allocation, asking if you're going to buy back stock. Those questions are fine too, but at the same time, I must build the business. So balance is very important.

What Investors Most Misunderstand About Intel

Host:
What do you think is the biggest misunderstanding investors have about Intel at this point?

Chen Lifu:
Several. First, back to "crawl, walk, run." The past four months, I've been crawling. But people are starting to realize the potential. Another very important point: We must truly deliver the best products. For example, in PC client, we still have market share. But we do need to build better performance. So I'm quietly building CPU architecture, GPU architecture, and software architecture teams, so we can move faster like a culture of multiple startups and leapfrog with better technology.

Beyond products, new energy is coming in, like Agentic AI, Physical AI. These are huge markets we can invest in.

On foundry, we still have a long way to go compared to TSMC, whether from performance or other aspects. So we must stay humble, build those foundational blocks, like the IP, yield, defect density, cycle time I mentioned earlier, make it more efficient, reliable. Foundry is a trust business. Customers must trust you first before giving you wafers, relying on you. So these things take longer.

But I think by 2030, 2031, people will start to see our potential size. On product side, PC client is our foundation. Then we'll enter edge computing, enter Physical AI and Agentic AI.

In the past, we mainly provided servers and PCs for humans. Now you'll see another dimension: millions of agents, they also need compute, need access to software stacks. So I think we have a chance to participate there.

The game is not over. We can still play in Agentic AI and Physical AI. That's the direction I'm going.

AI is just beginning. The training part is led by Jensen; edge computing, agents in Agentic AI, and Physical AI, I think are huge opportunities. Everyone has a chance. So that's what I want to aim for.

I hope investors will understand, although in the past 14 months, we've created 6x return for shareholders, this is just the beginning. We have much more room.

Host:
From here, there's still VC-style return.

Chen Lifu:
Yes. I'm always looking for 10x opportunities. As someone with a VC heart, you always want 10x.

At Cadence, when I was CEO, from the temporary CEO starting point of $2.42, to when I stepped down as Executive Chairman, created about 85x return for shareholders. Close to 76x, even 85x.

Doing that at Intel is hard because the base is larger. So I said, okay, then aim for 10x. If we can do 10x in five, ten years, I think that's a good return. With a VC heart, that's my goal.

Will Compute Always Stay in Data Centers?

Host:
Wishing you success in this already huge mission on a large base. There's an implicit judgment behind your description: where workloads will run. Some say we'll just build larger and larger data centers, 1 gigawatt is just the beginning. Centralized operation, even centralized inference, will dominate in efficiency.

But others consider edge, client-side. Do you believe there will be some equilibrium state for future compute? Or is it solely determined by the workloads themselves? What's your view?

Chen Lifu:
This is a very good question. Now AI infrastructure is being built at large scale, I think that's right. I don't think it will slow down because workloads are increasing massively.

Host:
We are supply-constrained now.

Chen Lifu:
Yes, supply-constrained. So if anything slows development, it's supply constraints.

But on the other hand, I always look at what solutions, what applications all this infrastructure building ultimately serves. I focus more on applications. If you can identify a huge application, or several applications that together are meaningful enough, and focus around it, then not everyone building will win. Some will win big, some will slowly fail, or plateau.

Like the internet era. You saw some companies became huge, like Amazon, Netflix; some became marginalized, disappeared, or got acquired. So for me, the thinking is the same. The real focus is: what application do they want to serve? How big is that application? Is it sustainable? Is it too crowded?

If too crowded, maybe only one or two remain, others get consolidated. So the industry will experience big growth, then start consolidating, eventually maybe one or two become the real winners. We've seen this movie before, so I'm not surprised.

Focus on applications. Netflix is an application, Amazon is a real application. In my view, they are winners.

Host:
But you assume some of these applications, served via client or edge compute, would be better than relying entirely on data centers?

Chen Lifu:
Exactly.

Host:
I myself also invest in some robotics and defense companies, so I know on-device compute is a very important choice. For example, if there are robots at home in the future, your assumptions about what compute is in the home, what connectivity, determine what you can do. I think this was somewhat forgotten in the SaaS era.

Chen Lifu:
Yes. My investment logic is: Find the real problem that needs solving. Second, find players you can partner with. Third, look at the application. How big is this application? Is it sustainable? If it's truly big and you believe in it, then double down, triple down.

Host:
But you also include betting on applications not yet widely deployed.

Chen Lifu:
Yes.

Host:
Great. Thank you so much for coming on the show today, great talking with you.

Chen Lifu:
Thank you very much.

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