In the latest episode of What the Tech from Boast, we sat down with Tony Kim, a corporate investor specializing in AI infrastructure, deep tech, and enterprise SaaS. Tony works at a Fortune 100 company’s corporate development and venture arm, where he’s involved in everything from M&A to venture capital and LP investments.
What makes this conversation so timely is Tony’s unique vantage point: he operates at the crossroads of traditional corporate strategy and the fast-moving world of AI innovation. Having started his career as an investment banker covering infrastructure and telecom in New York City, he’s witnessed the evolution of technology’s foundational layers. Today, he’s helping shape how a major corporation adapts to the AI revolution.
His insights on corporate venture capital (CVC), AI investment strategies, and how to distinguish real innovation from hype are exactly what founders need as they navigate the rapidly changing landscape of 2025.
The CVC Advantage: Strategy Over Pure Returns
One of the most valuable parts of our discussion was Tony’s breakdown of how corporate venture capital differs from traditional VC—distinctions many founders overlook.
Traditional VCs are primarily focused on financial returns, operating with fund structures backed by limited partners (LPs). Their goal is simple: maximize returns for investors.
CVCs, on the other hand, often operate differently. Some have their own fund structures, but many—like Tony’s—invest directly from the company’s balance sheet. This fundamentally shifts their mandate.
“Instead of just financial returns, CVCs are often pursuing strategic goals. That could mean building closer partnerships or developing new go-to-market channels. The CVC mandate is usually broader and more nuanced.”
What This Means for Founders
The approval process is more complex. While traditional VCs might greenlight a deal after partners review an investment memo, CVCs typically require buy-in from business units. Someone on the business side needs to champion the deal, which means more thorough diligence but also the potential for deeper strategic alignment.
Understanding this difference is crucial when you’re raising capital. If you’re pitching a CVC, you need to show not only how your company will deliver returns, but also how it aligns with the parent company’s strategic objectives. That could mean demonstrating partnership potential, go-to-market synergies, or technology that solves real problems for the corporate parent.
AI Has Changed Everything: Traction Is More Important Than Ever
If there’s one theme that dominated our conversation, it’s how dramatically AI has transformed the venture landscape in just the last 18–24 months.
The barriers to launching have dropped. We’re seeing companies hit annual recurring revenue (ARR) milestones in 12 months that used to take three to five years. AI-native companies are commanding higher valuations and attracting fierce competition from investors eager to get in on the action.
But here’s the flip side: AI is no longer an excuse for low traction.
“Even though AI companies are getting high valuations, they’re also showing impressive growth rates in ARR. It’s been fascinating to see pre-seed and seed-stage companies already demonstrating traction that would have been rare just a few years ago.”
In other words: If you’re building an AI company, investors expect you to move faster and show more traction earlier than they would have for a traditional SaaS company five years ago. The tools are there—the question is whether you’re using them effectively.
VCs Are Evolving Their Approach
In response to this competitive landscape, VCs are finding new ways to get closer to top talent. We’re seeing:
- Menlo Ventures partnering with Anthropic to launch a new fund called Anthology
- Andreessen Horowitz (a16z) raising dedicated AI funds
- VCs expanding geographically, looking beyond the crowded US market to Canada, Europe, and other regions
This marks a shift: Instead of founders chasing big-name VCs, VCs are now working hard to attract AI-native talent—even before companies officially launch.
Separating Real Innovation from AI Wrappers
With so much capital pouring into AI, one of the most important questions for investors (and for government R&D programs like SR&ED) is: What’s truly innovative, and what’s just riding the hype?
Tony’s answer gets technical—and that’s exactly the point.
“The fundamental design and architecture matter much more now. The goal is to dig into these companies and see if they’re more than just a wrapper. They’re not just an OpenAI wrapper, not just an Anthropic wrapper.”
What Investors Want to See
When evaluating AI companies, Tony looks at:
- AI memory architecture—How is data stored and accessed?
- Infrastructure choices—Which GPUs are you using, and why?
- Network and storage architecture—How have you customized your infrastructure?
- Training vs. inference focus—Are you optimizing for the right workload?
Companies that can’t answer these questions in detail are likely just API wrappers, using a single external API without meaningful technical differentiation.
Real innovation means customizing your infrastructure, making thoughtful architectural choices, and building technology that competitors can’t easily replicate with the same APIs.
The Boast Connection
This distinction is critical for R&D tax credits. Whether you’re applying for Canadian SR&ED credits or US R&D tax credits, the core requirement is the same: you need to be doing something new and technologically uncertain.
As Tony and I discussed, there’s been a surge of R&D tax credit providers that are basically AI wrappers themselves—automated tools promising to handle your claim with minimal human involvement. The problem? These tools often lack the deep technical expertise needed to properly document real innovation.
“With a lot of those off-the-shelf solutions, you get what you pay for. They’re not best-in-class. You absolutely need someone with real expertise—a human on your team.”
The companies that succeed in claiming R&D incentives are the ones that can clearly explain:
- What technological uncertainty they’re tackling
- Why existing solutions don’t solve the problem
- What systematic approach they’re taking to address it
- How their architecture or methodology is genuinely different
If you’re just wrapping an existing API, you don’t meet these criteria—and you shouldn’t expect government funding programs to reward that work.
The Full-Stack AI Investment Thesis
Tony’s investment focus covers the entire AI stack, from bottom to top:
Hardware Layer:
- Semiconductors and AI accelerators
- Optical interconnects
- Next-generation memory architectures
Infrastructure Layer:
- Data centers (both greenfield and brownfield projects)
- Storage and networking solutions optimized for AI workloads
- Energy infrastructure to support massive compute needs
Software Layer:
- Large language models (LLMs)
- AI agents and agentic frameworks
- Innovations at the application layer
This full-stack perspective matters because supply constraints are still the biggest bottleneck. Demand for AI compute, memory bandwidth, and energy far outpaces current supply. That creates huge opportunities for companies that can address these constraints at any layer of the stack.
The AI Agent Revolution: Still Early Days
Despite all the buzz around AI agents, Tony believes we’re still in the early innings—especially on the agentic AI front.
The vision is compelling: AI systems that can book your travel, coordinate complex workflows, and operate semi-autonomously across multiple tasks with minimal human input. Companies like Lovable (formerly Lovable Labs) are showing what’s possible, building impressive ARR in record time.
But big questions remain:
- How efficient can AI agents really become?
- Will they truly replace human labor in meaningful ways, or just augment it?
- Can they reliably handle complex, multi-step processes?
“The wildest part is, I don’t think we’re far off. I think we’re getting there. These systems could potentially solve all your fundamental problems.”
The next six months of benchmark data will be crucial in answering these questions. We’ll see whether agentic AI delivers measurably better outcomes in industries like healthcare, legal, and beyond.
What’s Next: Six-Month Predictions
When I asked Tony for his predictions for the next six months, a few themes stood out:
Hyperscaler Infrastructure Expansion
Major cloud providers (hyperscalers) are committing over $500 billion to AI infrastructure in the coming years. This includes:
- New data center construction (both new builds and upgrades)
- Custom chip development (reducing reliance on third-party vendors)
- End-to-end stack integration (owning as much of the value chain as possible)
The Global AI Race Heats Up
We’re seeing an arms race dynamic:
- US focus: Massive private investment in closed-source models (OpenAI, Anthropic, etc.)
- China response: Significant catch-up investment, with interesting innovation in open-source models
This isn’t just a business competition—it’s becoming a matter of national interest, with geopolitical implications that will shape policy and investment for years to come.
Continued Full-Stack Investment
Funding will keep flowing across the entire AI stack, but innovations in AI agents will attract the most attention. The question of how these systems can fundamentally change workflows and replace (or augment) human labor will be front and center.
The Infrastructure Imperative
Beyond algorithms and agents, the infrastructure needed to support AI at scale must expand dramatically. Tony’s background in infrastructure and data center financing gives him a unique perspective—he’s seen this story before and knows that foundational infrastructure investments often create the most lasting value.
Key Takeaways for Founders
Tony’s parting advice for founders navigating this landscape:
Embrace Change
“Thanks to AI, if you’re an AI-native company, fundraising might be a bit easier. But for traditional SaaS companies, it can be daunting if you need to pivot into AI. Embrace change however you can. Don’t be afraid.”
Traction Still Counts
Even if you’re not AI-native, a strong thesis and strong traction will still attract investment. Don’t feel pressured to rebrand as an AI company if it’s not authentic to your core value, but do think seriously about how AI could enhance what you’re building.
Know Your Audience
If you’re pitching CVCs, remember their evaluation criteria differ from traditional VCs. Strategic fit, business unit buy-in, and long-term partnership potential matter just as much as financial returns.
Build Real Differentiation
Don’t just be an API wrapper. Invest in understanding your infrastructure, make thoughtful architectural choices, and build something that’s genuinely hard to copy. This matters for fundraising—and for securing R&D incentives that can extend your runway.
The Bottom Line
We’re living through a fundamental shift in how tech companies are built, funded, and scaled. AI has lowered the barriers to entry while raising the bar for what counts as impressive traction. Corporate venture capital is expanding its mandates and competing more aggressively for deals. And the full-stack infrastructure needed to support AI at scale represents a multi-hundred-billion-dollar investment opportunity.
For founders, the message is simple: adapt to change, focus on building true technical advantages, and recognize that the old strategies no longer work as they once did. Whether you're seeking venture capital, strategic corporate investment, or non-dilutive funding through government R&D programs, the basics remain the same—you must address real problems with authentic innovation.
However, things are moving faster, competition is fiercer, and the opportunities are greater than ever before.
Listen to the Full Episode
Interested in learning more about corporate venture capital, AI investment strategies, and what truly sets real innovation apart from the hype? Tony offers insights on everything from semiconductor alternatives to the global AI race.
The opinions shared by Tony S. Kim in this interview are his own and do not represent those of his employer.
