In the evolving landscape of technology and business, a profound shift is underway. As articulated by Karri Saarinen, CEO of Linear, the "middle layer" in software development — the manual translation of requirements into code — is rapidly disappearing.
AI-powered coding agents now convert high-level specifications directly into functional code, compressing what once required extensive human intervention into near-instantaneous processes.
This leaves professionals focused on two critical ends: deeply understanding the problem at hand and effectively managing these agents. Addy Osmani from Google expands on this, emphasizing that top engineers have always been "merchants of clarity"—individuals who articulate problems so precisely that implementation follows naturally, now amplified by AI specifications.
This transformation isn't confined to engineering. It's permeating non-technical domains, where AI agents are automating routine tasks and enabling solo creators to build complex systems.
As we enter 2026, the bottleneck has shifted from coding to orchestration: coordinating fleets of agents, ensuring reliability, and embedding governance. This article explores this vanishing middle, supplemented with recent data on AI adoption and emerging platforms, to illustrate how it's reshaping industries and skillsets.
The Disappearing Middle in Software Engineering
Traditionally, software development involved a lengthy middle phase: interpreting user needs, designing architectures, and writing code line by line. Today, AI agents like those powered by Anthropic's Claude models are automating much of this. Claude, which captured 40% of the enterprise large language model (LLM) market share in 2025 — up from 24% in 2024 and 12% in 2023—has become a dominant force in code generation. Its Claude Code tool alone holds over 50% of the AI coding market, generating $1 billion in revenue within six months of launch.
This efficiency means what once took teams months can now be prototyped by individuals over a weekend. For instance, agents can decompose tasks into parallel subtasks, handle failure recovery, and iterate based on feedback. However, as Saarinen notes, the real value lies in tools that reduce ambiguity for both humans and agents, like Linear's workspaces, which serve as a coordination layer for product development.
Extending the Pattern to Non-Technical Teams
The "vanishing middle" extends far beyond code. In legal fields, AI agents are automating contract drafting and review, traditionally labor-intensive processes. Tools like Harvey AI and CoCounsel use GPT-4-based agents to perform end-to-end tasks such as redaction, compliance checks, and initial case briefs.
A 2025 survey found that 80% of legal professionals expect AI to have a "high or transformational impact" on their work over the next five years, freeing lawyers to focus on strategy and client relations.
Marketing teams are similarly transformed. AI agents orchestrate content operations, from generating personalized campaigns to analyzing performance metrics. Platforms enable marketers to build workflows that automate A/B testing, audience segmentation, and distribution across channels, reducing the need for dedicated development support.
In human resources (HR), the impact is particularly pronounced. AI agents handle onboarding, recruitment screening, and workforce management. For example, functional agents can screen resumes, conduct initial interviews, and even manage benefits enrollment.
By 2025, 47% of organizations were leveraging AI for workforce planning, with agents taking on roles like labor compliance monitoring and employee relations support. Gartner estimates that by 2030, 50% of current HR activities will be automated by AI agents, shifting HR from administrative tasks to strategic advisory roles.
These examples highlight a common thread: non-engineers no longer need to hire developers for automation. Instead, they can leverage no-code platforms to deploy agents, democratizing system-building across functions.
The New Bottleneck: Orchestration Infrastructure
With the middle layer compressed, orchestration emerges as the critical challenge. How do you coordinate 10, 50, or 100 agents? Manage state in long-running workflows? Ensure production reliability while incorporating governance and compliance?
The models themselves aren't the issue — Claude's rapid market growth demonstrates their efficacy. The gap lies in infrastructure. Emerging platforms are addressing this: Zapier offers no-code business orchestration with over 8,000 integrations; LangChain provides developer-focused tools for custom workflows; Prefect excels in data orchestration with task retries; and Amazon Bedrock enables cloud-based agent coordination. Kore.ai stands out for multi-agent orchestration, allowing agents to collaborate across enterprise systems with built-in governance.
Deloitte predicts the autonomous AI agent market could reach $8.5 billion by 2026, potentially growing to $45 billion by 2030 if orchestration challenges are met effectively. Companies controlling this layer—much like AWS for compute or Stripe for payments—will dominate intellectual system coordination.
Evolving Skillsets for Engineers and Beyond
For engineers, the required skills pivot from "how to implement" to "how to decompose." This includes breaking problems into agent-friendly subtasks, designing resilient workflows, and evaluating autonomous system quality. As Osmani suggests, clarity in problem definition becomes paramount.
Non-engineers gain unprecedented opportunities. Previously barred by technical barriers, they can now orchestrate agents independently. However, success demands understanding agent capabilities, workflow design, and ethical considerations like bias mitigation.
The Rise of Orchestration-Focused Companies
2026 is poised for breakthroughs in orchestration platforms. Rather than another coding agent, expect successes in "platforms for orchestrating 100 agents in regulated environments" or "layers for agent interoperability." Examples include Vellum AI for secure enterprise automation and AlignMinds for end-to-end agentic orchestration in operations and compliance. Linear's approach — building products equally suited for humans and agents — positions it as foundational infrastructure.
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Conclusion: Embracing the Orchestrated Future
The vanishing middle isn't about job loss but evolution. By compressing implementation, AI elevates human roles to problem-solving and strategic oversight. The true winners will master orchestration, turning agent fleets into reliable, scalable systems. As adoption accelerates — with enterprise AI spend hitting $37 billion in 2025, a 22x growth since 2023 — businesses must invest in these infrastructures to thrive. The middle may be gone, but the opportunities it uncovers are vast.
