Agent Provocateur

“Gradually, then suddenly.” That’s how Hemingway described going bankrupt, and it’s how exponential technologies tend to transform industries. At first, change feels incremental—until we find ourselves in the steepest part of the curve, where everything shifts seemingly overnight.

Most technologies follow an S-curve in their development: slow initial progress, followed by rapid acceleration, and finally a plateau as the technology matures. AI’s rapid leaps in capability, efficiency, and cost-effectiveness suggest we may be hitting that inflection point right now. But how can we recognize it with confidence?

Signals of Exponential Growth

Let's break down how we can recognize these moments of acceleration, and why AI agents are positioned to drive the next phase of adoption.

Rapid Improvements in Performance: A telltale sign of exponential growth is when fundamental performance metrics—like processing power, model accuracy, or efficiency—start improving at a pace that outstrips predictions. When breakthroughs start arriving faster than anticipated year over year, it signals a shift into high-gear.

Sharp Declines in Cost per Capability: Major cost reductions often coincide with technical breakthroughs. In AI, the combination of optimized compute architectures (GPUs, TPUs, and specialized AI chips) and more efficient model training has slashed the cost per computation and inference. When both cost and performance improve simultaneously, it’s a strong indication that adoption will surge.

Expanding Ecosystem & Network Effects: A technology becomes truly exponential when an ecosystem builds around it—think open-source collaborations, third-party integrations, and complementary hardware/software. These network effects fuel adoption, making the technology more valuable as more participants join.

Industry “Pull” Instead of “Push”: When entire industries start demanding a technology—rather than its creators having to push it into the market—it’s often a harbinger of mass adoption. Competitive pressures, fear of missing out, and proven ROI drive this shift from early adoption to necessity.

These signals have historically preceded major technological transformations. But how do they apply to AI's current trajectory? Let's examine the evidence.

AI: Into The Exponential?

The AI industry isn't just displaying the four signals of exponential growth—it's showing them at an unprecedented pace and scale:

Performance: The acceleration in model capabilities has become stunning. While updates to OpenAI o1 and Claude 3.5 Sonnet showed remarkable progress, recent developments suggest we're entering an even steeper part of the curve. Consider DeepSeek's R1, which achieved state-of-the-art results at a fraction of the traditional cost, or S1's breakthrough in achieving competitive performance with just 1,000 training samples versus the typical hundreds of thousands. These aren't just improvements—they're fundamental shifts in how we achieve AI capability.

Cost: The economics of AI are being fundamentally rewritten at both ends of the spectrum. At the frontier, DeepSeek achieved state-of-the-art performance for $5.6 million using lower-tier hardware - a fraction of traditional costs. Meanwhile, S1 demonstrated that even a few dollars of compute can now yield impressive results through efficient fine-tuning. These cost breakthroughs are particularly significant because they suggest we're approaching a critical threshold where AI deployment becomes economically viable across a much broader range of applications and industries.

Ecosystem: The rapid proliferation of open-weight models, efficient training techniques, and novel architectures is creating a virtuous cycle of innovation. When DeepSeek can achieve competitive results without premium hardware, and research labs can fine-tune powerful models in minutes, it suggests we're entering a phase where innovation will compound dramatically.

Pull: Industries aren't just adopting AI—they're restructuring around it. The demand isn't just for better models, but for transformative capabilities that can be deployed at scale.

It may feel like we're seeing exponential improvements, but current developments suggest we're only at the beginning of the curve. Consider that today's breakthroughs—training high-performance models for millions instead of billions, or achieving state-of-the-art results with minimal fine-tuning—are still primarily about making existing approaches more efficient. The real exponential growth will likely come from fundamental innovations in architecture and training methods that we're just beginning to glimpse, combined with the emergence of new deployment mechanisms like agents that can multiply the impact of these improvements.

License To Skill

Foundational AI models provide raw intelligence, but AI agents represent the most promising mechanism for translating that intelligence into practical value at scale. Whether automating workflows, making decisions, or interacting with tools, agents will be the primary vector through which businesses deploy AI capabilities.

AI agents represent an even more promising vector for exponential growth. Here's why agents may actually deliver greater scale economies and impact than models alone:

Performance: Agents compound improvements by combining model capabilities with real-world tools, data, and APIs, creating multiplicative gains in practical capability. Unlike traditional software that requires manual updates to incorporate new capabilities, well-designed agents can automatically leverage improvements in their underlying models. + For example, when a new language model becomes available, every agent using it immediately gains enhanced capabilities across all their tasks - from better reasoning to improved tool use. This creates a powerful multiplier effect: the more agents you deploy, the greater the aggregate impact of each model improvement.

Cost: Agents currently face two major cost barriers: high development costs due to the complexity of ensuring reliable performance, and significant operational overhead from running sophisticated models. However, several trends point to rapidly declining costs: emerging standardized frameworks are simplifying development, while specialized models are reducing runtime costs for common agent tasks. As these mature and democratization accelerates, we expect to see agent deployment costs plummet - similar to how containerization and microservices transformed software economics by making deployment both cheaper and more scalable.

Ecosystem: Agents benefit from exponentially growing tool integrations, APIs, and frameworks, creating network effects that models alone cannot achieve. But the real power lies in emerging multi-agent systems, since they allow agents to work with other agents, autonomously. For instance, Microsoft's AutoGen framework enables multiple specialized agents to collaborate on complex tasks—one agent might handle user interaction, while others specialize in code generation, debugging, and documentation. These agent networks can dynamically reconfigure based on the task at hand, creating a level of adaptability and scalability impossible with traditional software.

Pull: Agents have a much more direct alignment with business processes and workflows, which will drive broader adoption. Unlike raw models that require significant integration work, agents can be designed to plug directly into existing business processes and tools.

The key insight is that while models represent exponential improvement in raw capability, agents represent exponential improvement in practical value delivery. This suggests that while model improvements are a crucial enabler, agents are likely to be the primary mechanism through which AI's exponential growth manifests in practical terms. They represent not just a "next thing" but a fundamental multiplier on the value of underlying model improvements.

The evidence for this is already emerging: while models like DeepSeek's R1 and S1 demonstrate remarkable technical achievements, it's the packaging of these capabilities into autonomous agents that's likely to drive the next wave of adoption and value creation. The real exponential curve may not be in model parameters or training efficiency, but in the compound effects of agents combining improving models with expanding tool sets and API ecosystems.

The Path Forward

History teaches us that waiting for perfect clarity around technological transitions is a recipe for falling behind. The signs of AI's exponential acceleration are starting to emerge, but the exact timing and shape of the transformation will only be obvious in hindsight. Rather than trying to perfectly time the market, the more practical approach is to start small but start now—building experience with AI agents through focused experiments and real-world applications.

The goal isn't to transform everything overnight, but to develop the muscle memory and organizational learning that will be critical when adoption accelerates. Pick specific, bounded problems where AI agents could add value. Test, learn, and iterate. Build familiarity with the technology's real capabilities and limitations. This hands-on experience will prove far more valuable than any abstract strategy when the exponential curve hits its steepest point.

After all, successful adaptation to exponential change rarely comes from perfectly timing a single big bet. It comes from accumulated experience and the ability to recognize opportunities as they emerge. The time to begin that learning process isn't when AI agents are already transforming industries—it's now, while we still have the luxury of learning through deliberate experimentation rather than desperate reaction.