Human intelligence leaped forward when language enabled horizontal scaling (collaboration). Current AI development is focused on vertical scaling (creating bigger 'individual genius' models). The next frontier is distributed AI that can share intent, knowledge, and innovation, mimicking humanity's cognitive evolution.
As models become more powerful, the primary challenge shifts from improving capabilities to creating better ways for humans to specify what they want. Natural language is too ambiguous and code too rigid, creating a need for a new abstraction layer for intent.
Broad improvements in AI's general reasoning are plateauing due to data saturation. The next major phase is vertical specialization. We will see an "explosion" of different models becoming superhuman in highly specific domains like chemistry or physics, rather than one model getting slightly better at everything.
While language models are becoming incrementally better at conversation, the next significant leap in AI is defined by multimodal understanding and the ability to perform tasks, such as navigating websites. This shift from conversational prowess to agentic action marks the new frontier for a true "step change" in AI capabilities.
Moving beyond isolated AI agents requires a framework mirroring human collaboration. This involves agents establishing common goals (shared intent), building a collective knowledge base (shared knowledge), and creating novel solutions together (shared innovation).
The next frontier for AI isn't just personal assistants but "teammates" that understand an entire team's dynamics, projects, and shared data. This shifts the focus from single-user interactions to collaborative intelligence by building a knowledge graph connecting people and their work.
Today, most AI use is siloed, with individuals prompting alone. The real value is unlocked when AI becomes a team sport, with specialists building systems that are shared, iterated upon, and used collaboratively across the entire organization.
A more likely AI future involves an ecosystem of specialized agents, each mastering a specific domain (e.g., physical vs. digital worlds), rather than a single, monolithic AGI that understands everything. These agents will require protocols to interact.
Current AI development focuses on "vertical scaling" (bigger models), akin to early humans getting smarter individually. The real breakthrough, like humanity's invention of language, will come from "horizontal scaling"—enabling AI agents to share knowledge and collaborate.
The idea that one company will achieve AGI and dominate is challenged by current trends. The proliferation of powerful, specialized open-source models from global players suggests a future where AI technology is diverse and dispersed, not hoarded by a single entity.
While projects like Agency and A2A solve crucial communication and identity problems for AI agents, these are foundational. The larger, unsolved challenge preventing distributed superintelligence is the semantic layer: enabling agents to establish shared meaning and intent.