AI Revolution: Unprecedented Growth, Geopolitics, and Future Value
AI is driving unprecedented revenue growth, hyperdeflation of costs, and a global tech race. Strategic insights for investors and leaders.
Key Insights
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Insight
The current wave of AI companies is achieving "absolutely unprecedented takeoff rates" in customer revenue growth, significantly faster than any previous technological revolution.
Impact
This signals a profound shift in market dynamics and capital allocation, demanding rapid adaptation from established businesses and presenting massive opportunities for new ventures. Investors should prioritize firms demonstrating rapid customer acquisition and monetization in this environment.
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Insight
The unit cost of AI is collapsing "much faster than Moore's Law" due to optimization of inputs and massive build-out of infrastructure, driving exponential demand growth.
Impact
This cost collapse democratizes access to advanced AI, accelerating its integration across industries and driving new business models. It also creates intense competitive pressure on AI service providers to continuously innovate and reduce costs.
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Insight
The AI industry will likely structure itself with a small handful of powerful "God models" (equivalent to supercomputers) alongside a vast volume of smaller, highly capable models running on embedded systems.
Impact
This suggests a future where diverse AI solutions cater to different needs, from complex "God models" for high-end tasks to widespread, cost-effective embedded AI. Businesses must strategically choose and integrate appropriate AI models for optimal efficiency and scale.
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Insight
Open-source AI models are rapidly proliferating knowledge and capabilities, enabling new developers and startups to quickly catch up to state-of-the-art closed-source models.
Impact
Open-source models lower barriers to entry for AI development, fostering a vibrant ecosystem of innovation and talent. This can lead to faster technological advancement and increased competition across the AI landscape.
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Insight
The global AI landscape is a "two-horse race" between the US and China, with China actively developing its own models and chip ecosystem, creating pressure that may prevent over-regulation in the US.
Impact
This competition fuels massive R&D spending and national strategic focus on AI, potentially accelerating global technological progress. It also influences policy decisions, making it crucial for businesses to navigate evolving international trade and technology regulations.
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Insight
Overly draconian state-level (e.g., California's SB 1047, based on EU AI Act) or international AI regulations can stifle innovation, kill open-source development (e.g., downstream liability), and disadvantage regions in the global AI race.
Impact
Overly restrictive regulation can severely impede innovation, deter investment, and cede technological leadership to less regulated regions. This highlights the importance of balanced, innovation-friendly policy frameworks to ensure economic competitiveness.
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Insight
While base AI infrastructure is often usage-based ("tokens by the drink"), successful AI applications are moving towards value-based pricing, capturing a percentage of the business value or productivity uplift provided by AI.
Impact
Shifting to value-based pricing can unlock significantly higher revenue streams for AI application providers by aligning cost with measurable business outcomes. This encourages developers to focus on delivering tangible productivity and economic benefits to customers.
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Insight
There is a significant divergence between public panic about AI's impact (survey data) and actual widespread, enthusiastic adoption and usage (revealed preferences).
Impact
This indicates that despite public anxieties, the practical benefits of AI are driving rapid integration into daily life and work. Businesses should focus on delivering tangible utility to overcome perception biases and accelerate market penetration.
Key Quotes
"This new wave of AI companies is growing revenue, like just like actual customer revenue, actual demand, translated through to dollars showing up in bank accounts. I'd like to an absolutely unprecedented takeoff rate."
"If you pull, if you run a survey or a poll of what, for example, American voters think about AI, it's just like they're all in a total panic. If you watch the reveal preferences, they're all using AI."
"The price of AI is falling much faster than Moore's Law. And when I could go through that in great detail, but basically, like all of the inputs in the AI are are on a per unit basis, the costs are collapsing."
Summary
The AI Revolution: Unprecedented Growth, Geopolitics, and the Future of Value
The technological landscape is undergoing its most profound transformation in decades with the advent of AI, a revolution described as even larger than the internet. This shift is not merely hype but is translating into explosive revenue growth, rapidly evolving technological capabilities, and significant geopolitical implications that demand strategic attention from finance, investment, and leadership circles.
AI's Economic Impact & Technological Trajectory
AI companies are experiencing an "absolutely unprecedented takeoff rate," generating substantial customer revenue at a pace unseen in previous technological waves. This rapid growth is fueled by a hyperdeflation of AI's per-unit costs, outpacing even Moore's Law. As the costs of all AI inputs collapse and infrastructure build-out accelerates, advanced AI capabilities become increasingly accessible, driving exponential demand across sectors.
The industry is evolving rapidly, with current AI products still considered "super early." The future likely holds a dual ecosystem: a few powerful, centralized "God models" (akin to supercomputers) coexisting with a vast volume of smaller, highly capable models embedded in local hardware and everyday objects. This layered structure promises ubiquitous intelligence adapted to diverse tasks.
The Open vs. Closed & Startup vs. Incumbent Dynamics
Contrary to early predictions of a few dominant players, the AI race remains dynamic. Open-source models, particularly from emerging global players like China's Kimi and DeepSeek, are dramatically accelerating knowledge dissemination and capability "shrinkage." This allows new companies and individual developers to rapidly catch up to state-of-the-art closed-source models, fostering a highly competitive and innovative environment.
Startups are proving more than just "GPT wrappers." Leading application companies are backward-integrating, building their own specialized AI models, and leveraging open-source alternatives to optimize economics and domain-specific performance. This agility enables rapid disruption and the emergence of new "instant incumbents," challenging established tech giants.
Geopolitical Competition & the Regulatory Minefield
The global AI landscape is characterized by an intense geopolitical competition, primarily a "two-horse race" between the US and China. China's significant advancements in both open-source models and native chip ecosystems exert crucial pressure, positively influencing US policy by promoting innovation over stifling regulation.
However, the threat of ruinous regulation remains. Overly draconian state-level bills in the US (e.g., California's SB 1047, modeled after the EU AI Act) or international policies that assign downstream liability to open-source developers risk killing innovation, discouraging academic research, and ceding technological leadership. Federal preemption and balanced, innovation-friendly policy are critical to avoid self-inflicted harm.
Strategic Pricing & Societal Adoption
While foundational AI services are often priced on a usage basis ("tokens by the drink"), AI application providers are creatively exploring value-based pricing. This involves charging a percentage of the business value or productivity uplift delivered by AI, whether augmenting human roles (e.g., doctors, coders) or automating tasks. Higher prices, when linked to value, enable greater R&D investment, ultimately leading to better products for customers.
Despite widespread public "panic" regarding AI's impact on jobs and society (as revealed in surveys), actual societal adoption tells a different story. "Revealed preferences" show rapid and enthusiastic integration of AI into daily life and work, indicating a consumer embrace of its capabilities that often contradicts expressed anxieties. Ultimately, observed behavior will dictate AI's pervasive impact.
Conclusion
The AI revolution is a force of unprecedented scale, reshaping economic models, accelerating technological progress, and recalibrating global power dynamics. For investors and leaders, navigating this era requires a diversified investment strategy, proactive engagement with policymakers to foster innovation, a relentless focus on value-driven product development, and a keen awareness of both technological advancements and geopolitical shifts. The future of AI is not a singular path but a complex, interconnected ecosystem with immense potential.
Action Items
Venture capital firms and strategic corporate investors should adopt a portfolio approach, investing across big/small, open/closed, and foundation/application AI models, even if contradictory.
Impact: This diversified investment approach minimizes risk in an uncertain technological landscape and maximizes potential returns by capturing successes across various AI paradigms. It positions investors to capitalize on a multifaceted AI future.
Engage with policymakers, particularly at the federal level, to advocate for sensible, unified AI regulation that prevents fragmented and overly restrictive state-level laws.
Impact: Proactive engagement ensures policy frameworks support, rather than hinder, technological progress and maintains national competitiveness in the global AI race. It directly impacts the operational freedom and growth potential of AI companies.
AI companies should prioritize product development that delivers quantifiable business value, enabling value-based pricing models rather than solely relying on cost-plus or usage-based pricing.
Impact: Focusing on tangible business value enables premium pricing and stronger market fit, leading to greater profitability and sustained growth for AI solutions. This strategy differentiates products in a rapidly commoditizing market.
Businesses and individual developers should actively utilize and contribute to open-source AI models to accelerate learning, reduce development costs, and quickly deploy advanced capabilities.
Impact: Utilizing open-source tools reduces R&D costs, accelerates time-to-market for new AI applications, and cultivates a skilled workforce. This approach fosters a more dynamic and competitive innovation ecosystem.
Closely monitor global AI ecosystem developments, especially advancements from competitors like China, to understand competitive pressures and identify new capabilities.
Impact: Staying abreast of international AI trends, particularly from leading nations, informs strategic planning, competitive analysis, and potential partnerships. This enables companies to adapt quickly to geopolitical shifts and emerging technological breakthroughs.