The state of the tech industry in 2026 - LDX3 New York

Summary

In his keynote presentation at LDX3 New York, Gergely Orosz synthesizes observations from on-the-ground visits to leading AI laboratories and technology companies—including OpenAI, Anthropic, Cursor, Ramp, and Linear—to map the structural transformation of the software engineering landscape. Framing his discourse across four thematic pillars—what changed, what remained constant, what broke, and what lies ahead—Orosz examines how the shift to agentic engineering has fundamentally restructured development workflows. He illustrates how raw code generation has shifted almost entirely to AI models, traditional heavyweight IDEs have yielded to cloud-based agent harnesses, multi-year migrations are executed in weeks, and project teams are consolidating into one- or two-person squads managing parallel AI agents.

To counterbalance exaggerated industry narratives, Orosz systematically identifies enduring software engineering fundamentals, asserting that collaborative human teams remain the indispensable unit of ownership, rigorous planning remains critical to avoid architectural missteps, and testing demands equal effort to code synthesis. He debunks claims that non-engineers are shipping production code and highlights the rediscovery of classic modular design paradigms used to steer agent reasoning. However, this rapid operational acceleration has introduced severe friction across the ecosystem: pull request volumes have overwhelmed peer review into performative ‘zombie code reviews’, software reliability has degraded with continuous small regressions, supply chain bottlenecks have expanded into severe CPU hardware shortages, and developer context switching has heightened cognitive fatigue.

Progressing to future trajectories, the narrative pivots to strategic guidance for engineering leadership navigating organizational restructuring and executive burnout. Orosz argues that development environments will shift decisively to the cloud and that organizations will transition toward shipping verified code without manual line-by-line inspection via automated evaluation pipelines. Emphasizing that pure execution ‘intelligence’ is becoming commoditized, he contends that architectural ‘wisdom’ and persuasive ‘charisma’ are the defining differentiators for engineers. The keynote culminates in an urgent call for engineering leaders to remain hands-on builders, implement organization-level agentic infrastructure, and rediscover the core joy of building software.

Topics

Introduction and Scope of Industry Research

• Gergely outlines his keynote based on firsthand visits and conversations inside OpenAI, Anthropic, Cursor, Ramp, Uber, and Linear.
• The presentation adopts a four-part framework: what changed, what has not changed, what broke, and what comes next.

What Changed: The Agentic Engineering Shift

• The majority of production code is now AI-generated via models such as Opus 4.5 and GPT-5.2 rather than manually written.
• Hardcore engineers routinely manage 5 to 10 parallel agent sessions across concurrent worktrees.
• Heavyweight IDEs are rapidly declining into legacy products as teams adopt agent harnesses like Cursor, JetBrains Air, and OpenAI Codex.
• Startups and large tech companies are building proprietary internal agent harnesses integrated directly into Slack workflows.
• OpenAI and others have deployed autonomous agentic software factories and performance optimization pipelines.
• Large-scale codebase migrations (e.g., Python to Rust, test framework rewrites) that once took years are now completed in weeks or months.
• Engineering specializations are flattening, individual project staffing has shrunk to 1–2 engineers, and junior hiring continues to slow down.

What Has Not Changed in Software Engineering

• Two-pizza teams remain the vital organizational unit for accountability, on-call ownership, and collaboration.
• Architectural planning and customer alignment remain essential to avoid rapidly building the wrong systems.
• Testing and verification continue to consume roughly equal time and focus as code generation.
• Non-engineers are still not deploying production code independently despite low-code and AI hype.
• Classic software architecture concepts (e.g., deep modules, tracer bullets) are being rediscovered to prompt AI agents more effectively.

What Broke: Reviews, Quality, Infrastructure, and Leadership

• Massive code volume surges have rendered traditional peer code review obsolete, giving rise to rubber-stamped ‘zombie reviews’.
• Product quality and application reliability have deteriorated under a steady accumulation of agent-generated bugs and paper cuts.
• Hardware constraints have expanded beyond GPUs to acute CPU server procurement shortages with multi-month lead times.
• Relentless context switching across multiple agent threads is straining developer focus and productivity.
• Engineering leaders face severe burnout, elevated turnover, and shifting corporate expectations amid downsizing and lagging enterprise AI adoption.

What’s Next and Strategic Advice for Leaders

• Development environments and agent harness platforms are migrating entirely toward cloud infrastructure.
• Engineering organizations will shift toward shipping code without manual line-by-line reading by leaning heavily on automated eval suites.
• Hiring criteria increasingly mandate AI fluency and active positivity toward agentic tooling.
• Maturing organizations are shifting focus from isolated task automations to cohesive team- and company-level agent architectures.
• Because technical execution intelligence has become cheap and accessible, architectural wisdom and persuasive charisma are the new core differentiators.
• Leaders must remain hands-on builders, deploying internal AI infrastructure and actively shipping to production.

[00:00:00] Introduction and Scope

Gergely introduces his keynote presentation at LDX3 New York, noting his recent visits to AI labs and startups like OpenAI, Anthropic, Cursor, and Ramp. He sets up the talk’s four-part roadmap: what changed, what stayed the same, what broke, and what is coming next.

• Introduction by Gergely, host and writer of The Pragmatic Engineer.
• Recent on-the-ground research inside top AI labs and tech companies including OpenAI, Anthropic, Cursor, Ramp, Uber, and Linear.
• Agenda outline: what changed, what has not changed, what broke, and what comes next.

“In the last few months I went inside the HQ of open AI and Tropic cursor ramp.”

[00:00:53] What Changed: The Agentic Engineering Shift

Gergely details the extensive transformation over the past 12 months in software development workflows, driven by widespread adoption of agentic tooling, parallel agent execution, the sunsetting of traditional IDEs, automated agentic factories, rapid codebase migrations, shrinking team sizes, and the decline of narrow engineering specializations.

• Almost all raw code is now generated by AI models like Opus 4.5 and GPT-5.2 rather than written by hand.
• Engineers routinely run 5 to 10 parallel agents concurrently across multiple worktrees and tabs.
• Traditional heavyweight IDEs are treated as legacy interfaces, replaced by agentic harnesses such as Cursor’s pivot, JetBrains Air, and OpenAI Codex.
• Companies are building custom internal agentic harnesses and integrating workflows directly into Slack.
• Agentic software factories autonomously generate pull requests and optimize performance in production.
• Multi-year framework and language migrations (e.g., Python/Zig to Rust, JUnit, UI libraries) now take only weeks or months.
• Engineering specializations are flattening, individual project staffing is shrinking to 1–2 engineers, and junior hiring is declining.

“First thing nobody writes by code by hand anymore except for a a few of you folks” “It used to be a simple world, one mouse, one keyboard, one screen. You physically couldn’t work on more things than one. But no, we no longer have that limitation.” “On an individual project, you cannot often have more than one or two engineers working on it. This is because these engineers are already running several agents.”

[00:12:29] What Has Not Changed in Software Engineering

Gergely highlights structural fundamentals in software engineering that have remained consistent despite rapid AI adoption, arguing that collaborative teams, thorough planning, rigorous testing, and classic software design paradigms remain indispensable.

• Teams remain the fundamental unit of work for ownership, on-call accountability, and team cohesion.
• Planning and architecture are still essential for complex systems and infrastructure to avoid building the wrong thing.
• Testing and validation retain their equal time weighting compared to code generation to ensure trust.
• Non-engineers are still not shipping production-ready code despite hype around no-code/low-code.
• Classic software engineering principles and architectural patterns (e.g., deep modules, tracer bullets) are being rediscovered to prompt AI effectively.

“I still have teams whose job is to own a piece of software, own on call and so on and while each of these humans is supercharged by AI, the size and the shape is still similar.” “Before AI for production ready software, we spent about the same time writing tests as we did on writing code and this has not changed.”

[00:16:14] What Broke: Quality, Review, Infrastructure, and Leadership

Gergely discusses the negative side effects and structural fractures emerging in the industry, including overwhelmed code review systems, degraded software quality, hardware and CPU capacity shortages, developer cognitive exhaustion, and an exodus of engineering leaders.

• The explosion of code volume and agent PRs has broken traditional code review, leading to performative ‘zombie code reviews’.
• Software quality and reliability are deteriorating due to an influx of unreviewed, agent-generated changes and paper cuts.
• Hardware constraints now extend beyond GPUs to severe CPU and server procurement shortages.
• Developer focus and productivity are strained by high cognitive overhead and relentless context switching across multiple agents.
• Engineering leadership is experiencing high burnout and turnover due to unrealistic founder expectations, team downsizings, and slow enterprise AI adoption.

“Everyone is playing the theater of doing reviews, but with the volume of changes get thrown your way, he observed that people just find the path of least resistance.” “The ordering a CPU or a server used to take 1 to 2 weeks of of back ordering time. It’s now up to 6 months.”

[00:22:53] What’s Next and Advice for Engineering Leaders

Gergely presents forward-looking predictions and actionable advice for engineering leaders, urging them to build cloud-based agent platforms, transition toward testing-based code trust, cultivate deep domain wisdom, and remain hands-on builders.

• Development environments and agent harnesses will migrate almost entirely to cloud infrastructure.
• The industry will transition toward shipping code without manual line-by-line reading, relying instead on comprehensive eval and verification frameworks.
• Hiring criteria will mandate AI fluency, positive engagement with agentic workflows, and deep domain expertise.
• Organizations will move past individual task automations to mature, team-level and company-level agentic architectures.
• As technical execution (‘intelligence’) commoditizes, architectural ‘wisdom’ and persuasive ‘charisma’ will become the defining differentiators.
• Engineering leaders are advised to stay hands-on with production code, build internal AI systems, and embrace the return to building.

“What would it take for you to be comfortable shipping code without reading and understanding it?” “Now when intelligence becomes commonplace or you know with AI, the how to do it becomes just kind of very cheap, so widespread, wisdom and charisma become much more important.” “I got into software engineer because I like building stuff and now I can build faster and without some of the compromises I had before.”