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The Great Bottleneck

Innovation suppression in AI

The mythology of innovation emerging from garages and dorm rooms has definitively ended for AGI. The minimum viable budget for meaningful AGI research now exceeds $100 million annually, just to maintain a small team with basic computational resources. This doesn't include the costs of mistakes, failed experiments, or the inevitable scaling required to remain competitive.

Young innovators with breakthrough ideas find themselves forced to join existing organizations or abandon their research entirely. The academic pathway, once a source of fundamental breakthroughs, has been decimated as universities cannot compete with industry salaries or provide necessary computational resources. Brain drain from academia to industry has reached critical levels, with entire departments losing their top researchers to tech giants. 


Homogenization of Research Approaches

The concentration of resources has led to a dangerous homogenization of research approaches. With only a handful of organizations capable of large-scale experiments, the diversity of approaches to AGI has narrowed dramatically. Alternative architectures that might prove superior but require different infrastructure go unexplored. Unconventional ideas that challenge the dominant paradigm receive no funding or computational resources for validation.

This homogenization extends to research culture and values. The organizations controlling AGI development share similar cultural backgrounds, business models, and incentive structures. This lack of diversity in perspectives may blind the field to alternative approaches or important safety considerations. The wisdom of crowds, which has driven scientific progress through competition of ideas, has been replaced by the wisdom of a very small, homogeneous elite.

In summary, when AI is dominated by a handful of centralized actors, innovation converges on their priorities. Alternative architectures, grassroots experiments, and pluralistic approaches are marginalized, leading to intellectual monoculture and suppressed creativity.

The absence of plurality stifles creative experimentation and innovation. When intelligence is centralized into a few dominant paradigms, alternative models — symbolic reasoning, hybrid approaches, collective intelligence frameworks, or culturally grounded AIs - are marginalized. This creates an intellectual monoculture that limits the evolutionary potential of AI as a field.


Innovation bottlenecks

The oligopolistic structure of AGI development creates innovation bottlenecks that may slow progress toward beneficial AGI. With few organizations capable of large-scale experiments, the exploration of solution space becomes limited. Breakthrough innovations that require different approaches or challenge existing business models may never receive necessary resources for development.

This bottlenecking effect compounds over time. As technical debt accumulates in dominant approaches, switching costs to potentially superior alternatives grow prohibitive. Path dependencies lock in suboptimal solutions because the costs of change exceed any individual organization's resources. The market mechanism that normally drives innovation through competition fails when competition itself becomes impossible. 


The Integration Failure of Cognitive Approaches

Current large language models represent only one approach to intelligence - large scale brute force pattern matching. While this paradigm has demonstrated impressive fluency and adaptability, it remains fundamentally one-dimensional. Intelligence is not a singular property but an emergent phenomenon arising from multiple cognitive faculties - memory, reasoning, abstraction, perception, adaptation, and causal inference. By overcommitting to one cognitive mode, centralized AI has created a powerful but narrow intelligence that cannot generalize beyond its paradigm. Several established approaches to AI demonstrate strengths where LLMs are weak, yet integration remains absent. Example: Symbolic reasoning: Excels at formal logic, structured problem-solving, and rule-based consistency, but is excluded from the gradient-based architecture of LLMs; Evolutionary algorithms: Capable of exploring vast search spaces and optimizing novel solutions, but incompatible with the deterministic differentiability of transformer training.

The absence of these modes creates structural gaps: LLMs can simulate reasoning but not verify it, mimic solutions but not generate novel optimization strategies. This integration failure creates capability gaps that no amount of scaling can address. LLMs cannot perform reliable arithmetic despite seeing billions of mathematical examples. They fail at basic logical reasoning that simple symbolic systems solve trivially. They cannot learn from single examples the way humans do, requiring thousands of similar patterns to generalize. Scaling the same architecture does not close these gaps, it only magnifies fluency without resolving foundational deficits. The very assumptions that make LLMs scalable - dense differentiability, uniform optimization, end-to-end training, are the same assumptions that prevent integration of diverse cognitive approaches.

A genuinely general intelligence requires plural architectures - systems of systems, where different cognitive modes coexist, interact, and compensate for each other’s weaknesses. Centralized, monolithic AI trajectories actively resist this plurality, doubling down on scale instead of integration.

Architectural Dead End

The current monolithic model paradigm is inching towards an architectural dead end:

  • It cannot evolve into a comprehensive intelligence by scaling parameters or tokens.
  • It cannot integrate fundamentally different cognitive faculties without breaking its core training dynamics.
  • It locks research into an optimization trap, where improvements in scale yield diminishing returns while ignoring fundamental gaps.


The Integration & Interoperability Crisis

The AI ecosystem has fragmented into incompatible islands of functionality. Models cannot communicate with each other except through lowest-common-denominator text interfaces that lose semantic richness.

This fragmentation prevents the composition of AI capabilities that would enable true general intelligence. A vision model that perfectly identifies objects cannot directly share its understanding with a reasoning model that could use that information. A specialized medical diagnosis model cannot leverage insights from a general language model without complex, error-prone translation layers. The lack of standards means that combining AI capabilities requires custom integration work that few organizations can afford.

The economic cost of fragmentation compounds across the ecosystem. Every organization must rebuild basic infrastructure that can be shared. Developers waste countless hours writing translation layers between incompatible systems.

Unlike the internet, which succeeded through open protocols like TCP/IP and HTTP, the AI ecosystem lacks fundamental protocols for global or internet level agent communication, capability discovery, and resource negotiation. Agents cannot advertise their capabilities in standardized ways. There's no protocol for one AI system to query another's expertise or confidence levels. Resource sharing and load balancing happen through ad-hoc mechanisms that don't scale.

Recent developments have introduced basic protocol-like mechanisms, but these are:

  • Confined within organizations: internal orchestration frameworks for agent communication inside a single company’s infrastructure.
  • Narrow cross-organization use cases: limited interfaces for specific tasks or API chaining, which rely on rigid assumptions about context, inputs, and outputs.
  • Not designed for open scaling: they lack the generality, resilience, and neutrality that made internet protocols successful. Instead, they are assumption heavy solutions optimized for short-term integration rather than global interoperability.

This means the current “protocols” of AI are closer to closed plumbing for siloed systems than the kind of open, foundational standards that could enable a true Internet of AI. Without such protocols, AI remains fragmented, fragile, and unable to evolve into a pluralistic, decentralized network of intelligences which will truly fix the interoperability crisis.

Inefficiency from Lack of Capability Discovery

This protocol vacuum creates systemic inefficiencies. Specialized models with excess capacity cannot offer their services to others who need them. General-purpose models are repeatedly tasked with solving problems that specialized models already handle more effectively. This creates multiple inefficiencies:

  • Higher cost: General models consume far more compute and energy to produce answers that narrow models can deliver at a fraction of the cost.
  • Shallower accuracy: Instead of providing the domain depth that specialized models achieve - in fields like medical imaging, symbolic math, or legal reasoning - general models often produce surface-level approximations.
  • Resource redundancy: Scaling general models to approximate specialized skills duplicates effort, wasting compute, data, and capital that could be directed toward integration and coordination.

The Missing Division of Cognitive Labor

Because there is no protocol for capability discovery, AI systems cannot advertise expertise, query one another’s confidence levels, or delegate tasks automatically. As a result, users must manually route queries to different models. This manual mediation is brittle and inefficient: the system cannot automatically recognize that a specialized model would be cheaper, more accurate, and more context-appropriate for a given task. As a result, AI ecosystems default to expensive generalism over efficient specialization, undermining both economic sustainability and overall performance.

In human societies, Adam Smith’s principle of the division of labor unlocked productivity by allowing individuals and institutions to specialize, exchange, and coordinate. This specialization created depth of expertise, efficiency of production, and collective scalability far beyond what generalists could achieve alone.

By contrast, today’s centralized AI follows a model of expensive generalism: building massive systems that attempt to do everything internally, rather than fostering networks of specialized intelligences that cooperate through open standards. The absence of capability discovery prevents AI from evolving its own division of cognitive labor, where diverse agents complement and reinforce one another.

Consequences of Monolithic Design

Without specialization and delegation, AI ecosystems:

  • Remain fragile, as single models attempt to cover all domains but fail in depth and adaptability.
  • Become economically unsustainable, inflating costs while suppressing efficiency.
  • Block the emergence of a plural, networked system of intelligences, akin to an Internet of AI, where modular agents can discover, negotiate, and collaborate at scale.

Just as economies stagnate without specialization, AI risks stagnation without capability discovery. True progress requires moving beyond monolithic models toward a distributed ecosystem of specialized AI forms, coordinated through open protocols that enable a genuine division of cognitive labor.

The absence of such economic protocols proves particularly damaging. There's no standard way for AI forms to negotiate computational resources, deligate, seek knowledge, pay for services, or establish trust relationships. This prevents the emergence of AI economies where specialized AI could sustainably offer services to others. The infrastructure for machine-to-machine economic transactions, essential for distributed AI networks, remains primitive compared to human economic systems.

The Composability Challenge

Software succeeded through composability - small programs combining into larger systems. Current AI models resist composition. It seems like there is no belief in the fact that connecting multiple AI models create a model that is larger than sum of its parts.

The non-composability of current AI systems forces monolithic development approaches. Organizations must build ever-larger singular models rather than combining specialized components. This violates fundamental principles of system design that favor modularity, separation of concerns, and incremental development. The software engineering practices that enabled reliable, scalable systems cannot apply to monolithic AI models.

The economic implications of non-composability prove severe. Organizations cannot leverage existing AI components but must rebuild capabilities from scratch. Specialized expertise cannot be packaged and reused across different applications. The combinatorial explosion of possible AI applications cannot be addressed through combinatorial composition of capabilities. Each new use case requires training new models rather than assembling existing components.