AI Boom: Early Stage, Market-First Investing, and 3D Content Creation

The AI Boom: Beyond the Hype, Towards a Sustainable Supercycle

The current fervor surrounding AI, while reminiscent of the 90s dot-com boom, is fundamentally different, offering a genuine opportunity for value creation and long-term growth. This conversation with Martin Casado, a seasoned investor and technologist, reveals that we are still in the early stages of this technological wave, akin to 1996. The critical, often overlooked, implication is that unlike previous speculative bubbles, today's AI market is characterized by deployable capital, tangible revenue, and a demonstrable creation of true value. This insight is crucial for founders, investors, and technologists aiming to navigate and capitalize on what could be the greatest supercycle in two decades. By shifting from a company-out to a market-in investment lens, and by understanding the subtle yet profound differences between speculative exuberance and sustainable innovation, one can gain a significant advantage in this rapidly evolving landscape.

The Unfolding AI Supercycle: Why 1996 Is the Right Analogy

The energy surrounding Artificial Intelligence today evokes strong parallels with the early to mid-90s internet boom. Martin Casado, with his deep experience across technological cycles, places the current AI landscape firmly in 1996. This isn't a prediction of an imminent bubble, but rather an assessment of where we stand in a transformative technological adoption curve. The key differentiator, as Casado emphasizes, is the tangible value being created.

"The current technology wave is you can actually deploy capital and you can get revenue on the other side of it and i think that is what the market is trying to normalize but there's a true value being created in this ai and i think that if money's not falling it's going to miss the greatest super cycle in the last 20 years"

This isn't the era of IPOs for companies with no revenue, a hallmark of the late 90s bubble. Instead, today's AI companies, particularly those building foundational infrastructure like large language models, require significant capital but can demonstrably convert that investment into revenue and users. This capital intensity, while daunting, signals a market that is normalizing around real economic activity, not just speculative excitement. Companies like Google, Meta, Microsoft, and even startups like OpenAI and Cursor, are generating substantial revenue, grounding the current boom in a more solid foundation than the dot-com era. The implication for investors and founders is clear: focus on the underlying business fundamentals and the ability to capture value, rather than getting caught up in the sheer volume of new entrants or inflated valuations. The market is signaling that AI is not just a trend, but a fundamental shift that will create enduring value.

The Market-In Lens: From Founder-Centric to Market-Driven Success

Casado's investment philosophy has evolved significantly, moving from a company-out approach to a market-in perspective. This shift is critical for understanding how companies succeed in nascent, rapidly growing markets like AI. Instead of evaluating a company based on the perceived strength of its founder, product, or technology in isolation, Casado now prioritizes understanding the market itself.

"I used to think from company out. I've stopped that now. I think only from market in. The reality is the market creates the company in most cases, not the other way around."

This market-first approach recognizes that in transformative technological shifts, the market dynamics often dictate success more than any single company's attributes. The implication is that identifying a large, growing, and receptive market is the primary prerequisite for investment. Once a compelling market is identified, the focus then shifts to finding the right founder to lead within that market. This strategy, while not infallible, aims for a higher probability of success by betting on market tailwinds rather than solely on individual company execution. For founders, this means deeply understanding the market landscape, identifying unmet needs, and aligning their vision with the broader forces at play. It’s a reminder that even the most brilliant technology will struggle if it doesn't serve a significant and growing market need. The current AI wave, with its broad applicability across industries, presents a fertile ground for this market-in thinking.

The "God Model" vs. Composition: Navigating AI's Architectural Divide

Within the AI landscape, two distinct architectural philosophies are emerging: the "God Model" approach, aiming for a single, all-encompassing model, and the compositional approach, which leverages multiple specialized models. Casado highlights that the mistake is assuming one will supersede the other. The "Bitter Lesson" argument, which suggests that general-purpose learning methods (like scaling up models with more data and compute) are more effective than human-designed heuristics, supports the "God Model" path. Models like OpenAI's GPT-4 or Google's Gemini, aiming for broad capabilities, exemplify this.

However, the compositional approach, where specialized models are combined (e.g., one for image generation, another for 3D scene creation, a third for music), offers significant advantages in control and specificity. Casado points to the example of creating a video using Midjourney for images, World Labs for 3D scenes, and Suno for music. This approach allows for finer-grained control over the output, catering to specific creative visions.

"I honestly believe we're going to see both of these paths and I think the biggest mistake is people assume it's going to be one or the other."

The downstream effect of this duality is a rich ecosystem of AI tools and platforms. For developers and creators, understanding these different approaches is key to selecting the right tools for their specific needs. For investors, it means recognizing that defensibility may arise not just from the foundational model itself, but from the ability to orchestrate and integrate these various AI components into cohesive, valuable workflows. The future likely involves a hybrid approach, where powerful general models coexist with specialized tools and sophisticated composition techniques, creating a more robust and adaptable AI landscape.

The Unseen Value of Mundane Tasks: AI in Coding and Content Creation

Casado expresses surprise at how profoundly AI has impacted coding, an area he has been involved in his entire life. He notes that AI coding assistants have made programming more accessible and enjoyable, even for those who might have otherwise stepped away due to the steep learning curve of new frameworks.

"If you ask me what is the one area that ai has surprised you it's in coding i've been developing my whole life and i would never have guessed it'd be this good."

This observation has significant implications for the future of software development and, by extension, the creation of digital content. By lowering the barrier to entry and increasing productivity, AI is democratizing complex tasks. This is mirrored in content creation, where AI is rapidly bringing the marginal cost of producing images, video, and music closer to zero. While some, like prompt engineering, might be transient roles, the underlying ability to generate and manipulate digital content at scale is a fundamental shift. For businesses, this means new opportunities for personalized content, rapid prototyping, and more efficient creative workflows. The challenge, and opportunity, lies in productizing these AI capabilities and integrating them into existing business processes. The long-term advantage will go to those who can effectively leverage AI not just for novel creations, but for the mundane, repetitive tasks that consume significant human effort.

Actionable Takeaways: Navigating the AI Frontier

• Embrace the Market-In Lens: Prioritize understanding market dynamics and identifying growing sectors before evaluating specific companies or founders. This approach offers a more robust framework for investment and strategic planning.
• Focus on Tangible Value Creation: Differentiate between speculative hype and real economic value. Invest in and build companies that can demonstrate revenue and user adoption, grounded in sustainable business models.
• Understand Architectural Divergence: Recognize the coexistence of "God Models" and compositional AI approaches. Leverage specialized tools and integration strategies to build sophisticated AI-powered solutions.
• Leverage AI for Productivity: Integrate AI tools, particularly in areas like coding and content generation, to enhance productivity and reduce the cost of creation. This is an immediate advantage for individuals and organizations.
• Develop Long-Term Vision: The AI supercycle is in its early stages. Focus on building durable businesses with strong market positions rather than chasing short-term gains. This may require patience and a willingness to invest in foundational capabilities.
• Consider the "Unsexy" Applications: The true value of AI may lie not just in groundbreaking research, but in its application to everyday tasks and industries, such as coding, design, and enterprise workflows. This is where immediate and sustained impact can be realized.
• Prepare for Capital Intensity: Understand that building and deploying advanced AI models requires significant capital investment. Plan accordingly for fundraising and resource allocation.

Key Quotes

"The current technology wave is you can actually deploy capital and you can get revenue on the other side of it and i think that is what the market is trying to normalize but there's a true value being created in this ai and i think that if money's not falling it's going to miss the greatest super cycle in the last 20 years"

-- Martin Casado

"I used to think from company out. I've stopped that now. I think only from market in. The reality is the market creates the company in most cases, not the other way around."

-- Martin Casado

"I honestly believe we're going to see both of these paths and I think the biggest mistake is people assume it's going to be one or the other."

-- Martin Casado

"If you ask me what is the one area that ai has surprised you it's in coding i've been developing my whole life and i would never have guessed it'd be this good."

-- Martin Casado