AI Agents and the Intelligent Software Economy: Welcome to the Age of Identic Systems

May 02, 2025

Based on emerging consensus from major technology research groups and market watchers [1][2][3], it appears we are approaching a “ChatGPT moment” for AI agents—a moment so defining, it could reset the entire software value chain. At Chaintum, we argue that the global software economy is undergoing its most profound shift since the invention of the cloud. And unlike previous waves of disruption, this one isn’t just about scale or speed—it’s about delegation. The intelligent delegation of cognition, judgment, and execution to autonomous agents.

To begin with, AI agents aren’t your usual bots. They’re not just passive digital workers. They are anticipatory, reasoning systems trained not only to respond, but to decide, to plan, and—crucially—to act.

This subtle distinction has dramatic implications for the software economy. While traditional software was rule-based, command-triggered, and often context-blind, AI agents embody a more nuanced, adaptive, and context-rich design as shown in Graph 1. Think of them as proactive employees rather than reactive tools. Employees who never sleep, never forget, and never stop learning

Graph 1

Source : Ark Invest

From Siloed Systems to Identic Intelligence

Based on research from ARK Invest [4], we can classify AI agents into three evolutionary phases:

Siloed Agents—narrow AIs performing discrete, well-defined tasks.
Platform Agents, which integrate seamlessly across enterprise software suites, orchestrating workflows across departments, from HR to finance.
Universal Agents: flexible, context-aware, general-purpose intelligences trained to assist across domains with human-like adaptability.

As we move toward the latter stage, AI agents are less app-specific and more identic—malleable, highly personalized, and always-on digital counterparts as shown in Graph 2.

Consequently, the implications of this shift are far-reaching. In healthcare, Recursion Pharmaceuticals (RXRX) and Absci (ABSI) use AI agents to simulate millions of molecular interactions—cutting years off drug discovery timelines and massively reducing R&D costs [5], [6]. Absci’s “zero-shot” antibody generation approach is a case in point, showing how machine intelligence can skip steps once considered immutable in biopharma pipelines. In software engineering, GitHub Copilot and GitLab Duo are mere foreshocks of what’s coming. Microsoft’s ambition to become an “AI agent factory” via Azure AI Foundry is indicative of the massive industrial infrastructure forming behind this trend.

Graph 2

Agent-as-a-Service: Rewiring Monetization

Yet, it remains to be seen whether the economic models supporting these agents can evolve as quickly as the agents themselves. Traditional SaaS (Software-as-a-Service) monetization may struggle to keep pace. Instead, we may witness the rise of Agent-as-a-Service (AaaS): subscription-based, outcome-oriented AI services tailored to perform multi-step processes with minimal human oversight. In parallel, AI marketplaces—where services, tasks, and expertise can be bought, licensed, or rented by agents via APIs—are likely to become critical infrastructure. In this model, agents transact autonomously, reshaping not only business operations but commerce itself.

Based on data from McKinsey [7], we estimate that AI agents could boost global productivity by over USD 4 trillion annually by 2030. That’s not just a statistic—it’s a strategic imperative. Enterprises that fail to build or integrate agent ecosystems may find themselves unable to compete in labor markets increasingly defined by algorithmic labor.

Consider Klarna, which recently reported that its AI customer service agents resolved issues 20% faster than humans and cut operational costs by 40% [8]. This isn’t marginal innovation—it’s operational upheaval. The company didn’t just add an agent; it replaced significant chunks of its workforce. At Palantir, intelligent agents have transformed defense intelligence and logistics planning, replacing rigid dashboards with autonomous simulation and response systems.

The Rise of Synthetic Labor Markets

Consequently, if this trajectory continues, we may find ourselves living in a world where every company is partially run by a team of software agents—recruiters that screen candidates, financial advisors that optimize capital allocation, legal clerks that draft contracts, and R&D consultants that spin up synthetic trial scenarios. In such a world, the distinction between software and labor collapses, replaced by a spectrum of AI labor markets defined by domain, trust, and adaptability.

Despite widespread optimism, several hurdles remain. Ethical design, explainability, data privacy, and control mechanisms will become front-and-center debates. Agents will operate not just in the background but inside our calendars, inboxes, decision loops, and, potentially, our legal identities. Who’s liable when an agent misfires? How do we verify the authenticity of an action when it’s performed by a synthetic self? These are not academic questions. They are design mandates.

Africa's Leapfrog Moment

In Africa, where digital infrastructure is still uneven, the opportunity to leapfrog via AI agents is significant. Just as M-Pesa bypassed traditional banking rails, identic agents could democratize access to expert services—from financial planning to healthcare triage. They offer scalable, context-specific interventions for informal markets, helping manage irregular incomes, coordinate community projects, and even facilitate cooperative lending structures.

And yet, agents must be localized. The cultural, linguistic, and economic contexts of African users demand agents trained on datasets that reflect real-world challenges. In Kenya, for example, where informal labor dominates, agents could be designed to assist jua kali artisans with inventory management, pricing decisions, and mobile-based marketing. These aren’t Western-style enterprise use cases—they’re foundational, life-enhancing tools.

If agent adoption continues along current trajectories, by 2030 we may witness the emergence of Autonomous Commerce: economic systems partially governed by synthetic agents acting on behalf of both producers and consumers. In such a system, human attention becomes the rarest resource—everything else, from negotiation to execution, becomes machine-mediated. AI agents won’t just serve us; they’ll transact, negotiate, and decide in our name.

The Rise of Agent-First Startups

So, what comes next? We foresee a new class of agent-native startups. Not just AI-first companies, but AI-agent-first platforms that position their agent architectures as the user interface, business model, and delivery channel. These platforms will serve as agent operating systems, competing not just on features, but on the intelligence, adaptability, and trustworthiness of their agents.

And eventually, these agents will stop feeling like tools. They’ll feel like coworkers. Or perhaps even alter egos—trained on your preferences, habits, goals, and context. In this future, intelligence won’t live in distant servers—it will be ever-present, invisible yet proactive, highly personal, and deeply entangled with how we work, play, and live.

Whether we call them identic systems, intelligent twins, or cognitive collaborators, one thing is clear: the age of AI agents is here. And for those who move early, build deeply, and localize intentionally, this may be the defining opportunity of the next decade.

References

[1] Prudhomme, Claire. 2025. “[the AI Show Episode 141]: Road to AGI (and Beyond) #1 — the AI Timeline Is Accelerating.” Marketingaiinstitute.com. Marketing AI Institute. March 27, 2025. https://www.marketingaiinstitute.com/blog/the-ai-show-episode-141.

[2] Flaningam, Eric. 2024. “Nvidia: Past, Present, and Future.” Generativevalue.com. Generative Value. December 2024. https://www.generativevalue.com/p/nvidia-past-present-and-future.

[3] Deal Room. 2025. “THE 2025 EUROPEAN DEEP TECH REPORT.” https://dealroom.co/uploaded/2025/03/2025_Dealroom-Deeptech-Report.pdf?x98550

[4] Bhushan, Rahul. 2025. “How AI Agents Are Redefining the Software Economy.” ARK Invest Europe. March 13, 2025. https://europe.ark-funds.com/2025/03/ai-agents-and-the-shift-in-the-software-economy/.

[5] AMD. 2025. “Advanced Insights S2E3: Absci on AI Accelerated Drug Discovery.” YouTube. April 16, 2025. https://www.youtube.com/watch?v=-MGp6hpfrjk.

[6] Devansh. 2024. “How Recursion Pharmaceuticals Is Using AI to Revolutionize Drug Discovery.” Medium. August 30, 2024. https://machine-learning-made-simple.medium.com/how-recursion-pharmaceuticals-is-using-ai-to-revolutionize-drug-discovery-b115c88f783c.

[7] Chui, Michael, and Lareina Yee. 2023. “AI Could Increase Corporate Profits by $4.4 Trillion a Year, according to New Research | McKinsey.” Www.mckinsey.com. July 7, 2023. https://www.mckinsey.com/mgi/overview/in-the-news/ai-could-increase-corporate-profits-by-4-trillion-a-year-according-to-new-research.

[8] Doerer, Kristen. 2025. “Klarna Credits AI for Slashing Customer Service Costs.” CX Dive. May 21, 2025. https://www.customerexperiencedive.com/news/klarna-ai-slash-customer-service-costs/748647/.

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