New Horses in AI
The evolution of artificial intelligence relies on comparison with historic technological advancements, and in many aspects, it is emerging as the next big thing, similar to the internet revolution. The emergence of new AI models, particularly ones that offer cost effective and scalable intelligence, signals a significant shift in technological capabilities and global competition. Similar to the early days of the internet, where universities and unexpected players played pivotal roles, the AI race is not just about who has the most resources anymore but about who can leverage innovation most effectively.
Key lesson from history is that breakthroughs often come from unexpected places. AT&T and WorldCom did not foresee the internet emerging from academic institutions, yet it did. Similarly the current AI revolution is not just being driven by Silicon Valley giants but also by smaller research teams across the globe. This decentralization of innovation highlights the importance of adaptability and rapid iteration over financial competence.
Recent release of AI models by lesser known entities demonstrates that new entrants can compete with tech giants. Response to such releases, particularly their impact on financial markets, mirrors past technological shifts where industry players had to reevaluate their positions rapidly. This underscores the reality that AI is not an isolated field but is deeply interwoven with economics, policy, and global competition.
Common mistake in assessing AI’s trajectory is the tendency to view it through a zero sum lens. Many assume that if one AI model performs exceptionally well, it necessarily diminishes the value of others. However, history suggests otherwise. Expansion of the internet did not render existing technologies obsolete overnight; rather, it enabled a broader ecosystem of innovation. Similarly AI’s progress should be seen as expanding the field, creating new opportunities rather than simply shifting power from one entity to another.
For instance, deep learning models may become more efficient, leading to AI applications that were previously unfeasible due to cost or computational constraints. This could enable widespread AI adoption on personal devices, leading to what some call “AGI in your pocket.” Proliferation of AI across different domains from healthcare to creative industries indicates that its impact will be vast and diverse.
One other significant debate in AI development is whether to scale up (building ever larger, centralized models) or scale out (distributing intelligence across numerous smaller devices). Historically, computing trends have favored scale out strategies. Transition from mainframes to personal computers, and from centralized networks to distributed internet, suggests that AI will likely follow a similar path.
AI’s trajectory indicates that models will become increasingly efficient and accessible, enabling their deployment on personal devices. This mirrors how the internet evolved, initially reliant on massive infrastructure before becoming a decentralized network that anyone could access. Implications of this are profound: it means AI will become deeply integrated into everyday tools, not just cloud based services controlled by few companies.
Another critical aspect of AI’s development is how governments choose to regulate it. Policy missteps seen in the past, such as export controls on chips and export controls, demonstrate futility of trying to restrict technological diffusion. The AI race should not be about limiting others but about accelerating domestic innovation.
During the internet’s formative years, regulators in the United States made decisions that allowed for its rapid expansion, resisting the urge to impose heavy handed controls that could have stifled innovation. AI requires a similar approach: rather than attempting to constrain its growth through excessive regulation, policymakers should focus on creating an environment that supports open research, investment in AI infrastructure, and a competitive marketplace.
As AI continues to evolve, focus will shift from model development to application deployment. Just as the internet was not just about protocols but about services built on top of them, AI’s value will be determined by how effectively it is integrated into real world use cases. Companies that successfully combine AI capabilities with well designed applications will lead the next wave of technological advancement.
Industries that embrace AI at enterprise level will also play a significant role in shaping its adoption. Features such as enterprise grade security, compliance, and integration with existing workflows will be essential for widespread AI implementation. Best positioned companies will be those that understand both technical capabilities of AI and practical needs of businesses and consumers.
AI is not merely the next big thing, it is the next foundational technology that will underpin countless innovations. Comparing it to Sputnik may capture its geopolitical implications, but a more accurate analogy is the internet. AI’s growth, like that of the internet, will be determined by how it is nurtured through open collaboration, smart regulation, and willingness to embrace new movements. As we navigate these shifts in technology, the most important lesson is clear: the AI revolution, much like the internet revolution before it, will be shaped not just by those who build technology but by those who harness its potential in transformative ways.
