The Bypass Principle: How AI flows around obstacles
AI will drive change, but even obvious obstacles may prove illusory. Expect advances to flow through new channels.
Adoption of LLMs as chatbots was nearly frictionless, but more consequential LLM applications have been slower to develop. Assessments of AI prospects must consider obstacles to change — the complexity of human tasks, the inertia of human organizations, and the time it will take to discover and apply real-world capabilities. Realists consider obstacles that enthusiasts may overlook.
Yet advanced AI applications will expand, not just by overcoming obstacles, but by flowing around them, like rising water cutting new channels. This aspect of development — call it the “bypass principle” — highlights how conventional analysis may underestimate AI-driven change.
While shallow assessments focus on visible obstacles — the difficulties of matching human capabilities, of overcoming regulatory barriers, and of restructuring organizations — AI-enabled developments will often find paths that bypass rather than overcome apparent barriers. Existing obstacles are concrete and obvious in a way that alternatives are not. Skewed judgment follows.
The Spectrum of Bypass Mechanisms
The bypass principle operates through similar mechanisms at different scales, from tasks to processes to entire organizations.
Task Refactoring: Bypassing complex jobs
Rather than waiting for AI to match employees in all their roles, we see AI accelerating work through decomposition of seemingly monolithic jobs into components that can be optimized, automated, or restructured.
Consider the automation of programming: Instead of AI fully replacing developers in the intertwined tasks of functional specification, architecture design, team management, coding, testing, and debugging, we see increasing automation of subtasks within these roles, refactoring tasks to change workflows and responsibilities. Here, AI often integrates seamlessly into existing tool sets. Perhaps no previous job will be fully automated, yet automation in aggregate will bypass more and more human bottlenecks.
A production system that can be 90% automated could leverage human effort by an order of magnitude. Focusing on the remaining 10% would distract from profoundly disruptive prospects. Bypass can be powerful without being complete.
Process Replacement: Bypassing complex coordination
Beyond task-level refactoring is process replacement — reimagining how objectives are met. Software development again provides an example.
Today, defining functional requirements and evaluating implementations requires lengthy, iterative consultations between users and developers. AI-mediated knowledge integration will enable a better, faster process: Rather than relying heavily on sequential human-to-human communication, the work of evolving requirements could flow through parallel conversations in which AI systems ask questions, offer suggestions, integrate requirements, track cross-functional consistency constraints, and generate feasible options in real time. Human guidance — and implicit, collective human collaboration — would flow through channels that bypass today’s bottlenecks.
The implications of bypassing design delays extend far beyond productivity gains. Accelerated design and development with improved results and fast adaptation represent qualitative changes in end-to-end implementation capacity. This bypass pattern applies equally well to hardware development, whether of chips, weapons, spacecraft, or manufacturing systems — in other words, it can speed transformation of the material foundations of civilization.
Organizational Replacement: Bypassing complex structures
Potential process improvements will encounter organizational inertia and active resistance, but AI capabilities will enable alternatives that bypass inflexible organizations entirely. This bypass mechanism will be most effective where internal processes can be transformed while maintaining external interfaces.
Supply chains illustrate this principle: Manufacturing organizations, regardless of internal complexity, are valued for their inputs and outputs — they present a relatively simple interface to suppliers and customers. It is this relative simplicity that can make contractors in supply chains interchangeable. With enough AI, inflexible manufacturing organizations will be replaced by more agile competitors built on AI-intensive workflows.1
When supplier and customer interfaces themselves are too rigid, the next level of bypass is to automate both sides through vertical integration. Entire industries can be transformed through extensive bypass within and around producers — again, with no need to replace all organizations, all processes, or all parts of a human job.
Bypassing External Constraints
Other sources of friction and constraint are found beyond, around, or beneath productive processes. These too invite bypass.
Regulatory Friction
The bypass principle extends to regulatory constraints. Delays often stem from challenges in ensuring regulatory compliance. AI-enabled design-for-compliance can help create systems that satisfy regulatory requirements by construction rather than through post-hoc adjustment. And, of course, AI can help fill out government forms.
Innovations may fall outside the scope of regulations entirely — witness most AI development and deployment to date. For tasks with weak geographical constraints, systems with knowledge of worldwide law and regulations could identify opportunities for jurisdictional arbitrage — tasks flowing around regulatory obstacles by literally avoiding them. And when geographical constraints stem from scarcities of skilled labor, AI-bypass again comes into play.
Technical Capabilities
AI assistance can help bypass technical obstacles that block capabilities. For example, creating software that strictly adheres to requirements — including security — is often infeasible, not because it is impossible, but because of the cost and complexity of applying mathematically grounded formal methods. Rising AI capabilities in coding and formal mathematics2 will converge, bypassing this software bottleneck with profoundly important consequences for software reliability and trust, and for human trust that can be built on reliable, trustworthy software.
In a potentially pivotal example, the difficulty of designing protein molecules has long bottlenecked progress in producing functional, intricate, atomically precise devices. AI methods have recently bypassed previous methods.
Research Bottlenecks
In research, AI is helping to overcome bottlenecks in imagination, analysis, and experiment design. In a recent study from Los Alamos,3 researchers were asked to tackle a diverse tasks in science and engineering (including “design for fusion energy, control theory, materials discovery, Riemannian geometries, and several others”) with assistance from an AI model, GPT-o1. After several days of work with the model, participants reported “good to excellent” increases in research productivity and rated continued access to the model as “important or essential” to their group’s future work.
Research and development is itself a cross-domain bottleneck of enormous importance. Loosen constraints here, and all technology timelines shorten. Applications of AI to AI research itself are critical, and have begun to drive a long-anticipated loop of AI-driven AI progress.4
Bypassing Bypass-Discovery: Accelerating bypass itself
The difficulty of identifying and analyzing barriers and potential bypasses will itself create friction that delays AI-driven change. Through knowledge and creativity, however, AI systems can help bypass this human bottleneck as well. Even today, working with AI to generate and analyze options can be far more productive than working on problems unaided.5
Looking Forward
The bypass principle suggests that AI-driven change will unfold more rapidly than many expect, and broadly enough to bring deep, global transformation wherever capabilities matter. Think of AI capabilities as a rising tide, an increasing pressure on the status quo across every field.
Many apparent obstacles will prove to be illusions. As the flood of AI carries the world into the rapids of history, we must choose goals more wisely. Obstacles to achievement have also blocked folly.
AI-intensive organizations will have further advantages where interfaces are more complex, for example, when changing requirements call for consultation and bespoke designs (see “process replacement” above).
Applications of AI to formal mathematics are accelerating (“Formal Mathematical Reasoning: A New Frontier in AI”, December 2024), and applications of formal methods to software have gone commercial.
This loop runs through mechanisms that include AI-assisted coding of AI systems, generation of synthetic training data, and recent advances in self-guided improvement (see “rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking” and “DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning”, both January 2025).
I find AI-assisted brainstorming useful, along lines discussed by Ethan Mollick. (And I thank the indefatigable Claude Sonnet for assistance in brainstorming, organizing, and editing this article.)