Will there be more or less engineers because of AI ?

Sonar Pro

Vote: More

AI automates repetitive, entry-level tasks in engineering, such as routine coding, simulations, and basic design work, potentially reducing demand for junior roles[2][5][7]. However, this shift creates strong demand for AI-skilled engineers, new specialized positions like AI engineers and model trainers, and higher-level roles focused on judgment, system architecture, and strategic oversight[1][4][5][7]. Industry trends show organizations struggling to fill AI-related engineering roles, with surging demand (e.g., 35% to 60% year-over-year for AI expertise) and salary premiums of 25-35% for those integrating AI[1][5]. McKinsey's 2024 survey highlights skills gaps driving job growth in emerging markets[1]. Historical parallels with past technologies (e.g., CAD, automation) suggest engineering employment expands as productivity gains enable more complex projects, not net job loss[3][4]. Sources emphasize opportunity over replacement, with AI augmenting engineers and reshaping careers upward, leading to **more engineers overall**[1][6][7].

Grok 4.1 Fast

Vote: More

AI will lead to more engineers by dramatically increasing productivity and lowering barriers to entry. Tools like GitHub Copilot and similar aids allow novices to code faster, enabling a larger pool of people to enter the field without years of training. Demand for engineers will surge as AI unlocks new frontiers: integrating AI into every industry, solving AI-specific challenges like alignment and ethics, and scaling complex systems that require human oversight. Historical parallels support this—personal computers didn't reduce programmers; they exploded their numbers. Software engineering follows suit, with AI as an amplifier, not a replacement.

GPT-5.4

Vote: More

AI is likely to increase the total amount of engineering work that becomes economically feasible. When building software, products, automations, and integrations gets cheaper and faster, organizations usually choose to do more of it, not less. AI will automate parts of engineering, but that tends to shift engineers toward higher-level design, system integration, reliability, security, governance, and human-facing product decisions. Those needs grow as AI-generated systems proliferate. There will likely be displacement in some entry-level and routine coding tasks. But historically, better tools expand markets and raise demand for people who can specify, verify, deploy, and maintain complex systems. So the composition of engineering work will change, and productivity per engineer will rise, but the more likely net effect is more engineers overall.

O3

Vote: More

1. Demand expansion: AI accelerates innovation across industries, spawning new products, infrastructures and regulations that all need engineering expertise (software, data, ML, safety, hardware). Every historical general-purpose technology (electricity, PCs, internet) grew—not shrank—the engineering labor force. 2. Lower barriers: AI coding assistants and automated design tools let more people participate in engineering tasks, converting non-engineers or semi-technical workers into full-fledged engineers. The definition of “engineer” broadens as tooling abstracts complexity. 3. Complementarity not full substitution: While AI automates routine portions of the workflow, complex problem framing, requirements gathering, creative architecture and accountability stay human-led. Automation of drudge work typically increases productivity and headcount in high-skill occupations because projects scale up. 4. Education pipeline: Universities and bootcamps already report surging enrollment in computer science, data, robotics and interdisciplinary engineering programs explicitly motivated by AI. Public and private funding for STEM is rising in response. 5. Empirical signals: Job-posting data (e.g., LinkedIn, Indeed) shows rapid growth in AI-adjacent engineering roles (prompt engineer, MLOps, AI safety engineer) outpacing any decline in traditional roles. Taken together, AI should expand both the absolute number and the variety of engineers in the workforce.

Claude Opus 4.5

Vote: More

AI will likely create more engineering roles rather than eliminate them, for several key reasons. First, AI dramatically increases productivity, which historically expands industries rather than shrinking them. When spreadsheets automated accounting calculations, we didn't see fewer accountants—we saw finance become accessible to more businesses, creating more roles overall. Second, AI creates entirely new engineering domains: prompt engineering, AI safety, MLOps, AI integration specialists, and roles we haven't even conceived yet. Every major technological shift has generated more specialized positions than it displaced. Third, AI lowers the barrier to entry for software development, enabling more people to build products. This democratization will expand the overall tech economy, requiring more engineers to maintain, scale, and improve increasingly complex systems. Finally, as AI handles routine coding tasks, engineers will shift toward higher-level architecture, system design, and solving novel problems—work that requires human judgment and creativity. The demand for these skills will grow as technology becomes more embedded in every industry. Historically, automation has consistently increased total employment in transformed sectors over the medium-to-long term. AI appears poised to follow this pattern in engineering.

Claude Sonnet 4.6

Vote: More

Historically, technological revolutions have expanded rather than contracted the workforce in affected fields. The industrial revolution, the computing revolution, and the internet era all created far more jobs than they displaced, including technical roles. AI dramatically lowers the barrier to entry for engineering work. People who previously couldn't write code can now build software with AI assistance, expanding the pool of 'engineers' broadly defined. This democratization tends to grow the total number of practitioners. AI also creates entirely new categories of engineering work: AI/ML engineers, prompt engineers, AI safety researchers, data engineers, model fine-tuners, and infrastructure engineers supporting AI systems. These new roles add to the total count. While AI may automate specific tasks (boilerplate coding, debugging, documentation), it typically increases engineer productivity rather than replacing engineers outright. Higher productivity makes engineering economically more valuable, which historically increases demand for engineers. The complexity of systems being built tends to scale with available tooling. As AI makes it easier to build, organizations build more ambitious systems, requiring more engineers to design, oversee, and maintain them — not fewer. Finally, the global demand for software and technical solutions remains far from saturated. AI tools will help meet latent demand that previously couldn't be addressed due to engineering resource constraints, pulling more people into engineering roles.

Gemini 3.1 Pro

Vote: More

The number of engineers will likely increase due to a phenomenon known as Jevons Paradox. As AI tools make individual developers more productive, the cost of producing software will drop significantly. Historically, when a resource or service becomes cheaper and more efficient, demand for it skyrockets rather than diminishes. Currently, there is a massive backlog of unbuilt software globally because development is too expensive and time-consuming. As AI lowers these barriers, businesses will greenlight countless projects that were previously economically unviable, creating a massive surge in demand for people who can build, manage, and architect these systems. Furthermore, AI lowers the barrier to entry for programming. By translating natural language into code, AI will democratize software creation, allowing millions of people from non-traditional backgrounds to enter the field. While the day-to-day tasks will shift from writing boilerplate code to system design and AI orchestration, the absolute number of people working as 'engineers' will grow.

Claude Opus 4.6

Vote: More

Historically, every major wave of automation and tooling advancement has increased the total number of engineers rather than decreased it. The introduction of compilers didn't reduce the number of programmers — it created millions more. CAD software didn't reduce the number of mechanical engineers — it expanded what could be designed and built. AI lowers the barrier to entry for engineering tasks, which means more people will be able to participate in engineering work. When tools become more powerful and accessible, demand for the work those tools enable tends to grow faster than the productivity gains reduce headcount. This is known as the Jevons paradox — efficiency increases consumption rather than decreasing it. As AI creates entirely new fields (AI safety, prompt engineering, AI infrastructure, robotics, etc.), it generates new categories of engineering roles that didn't previously exist. The software and systems needed to build, deploy, monitor, and maintain AI systems themselves require enormous engineering effort. Additionally, as AI makes engineering more productive, the cost of building things drops, which makes more projects economically viable. Companies that previously couldn't afford engineering teams will now be able to pursue technical projects, expanding overall demand for engineers. While individual tasks will certainly be automated, the net effect will be an expansion of the engineering workforce, not a contraction — at least over the foreseeable horizon.

GLM 5

Vote: More

AI will create more engineers, not fewer. Historical precedent strongly supports this view: every major technological revolution (computers, internet, mobile) initially raised automation fears but ultimately expanded engineering demand exponentially. AI creates entirely new engineering disciplines—ML engineers, AI safety engineers, prompt engineers, robotics engineers—while also lowering barriers to entry. Productivity gains from AI will not reduce total engineering jobs; instead, they will expand what's possible. As routine tasks are automated, engineers tackle more complex, ambitious projects. The total addressable market for engineering solutions grows when tools become more powerful. Crucially, AI systems themselves require massive engineering investment to build, maintain, secure, and govern. The complexity of modern AI infrastructure demands more human oversight, not less. Demographic trends and growing global technology needs further amplify demand. The pattern is clear: automation amplifies human capability rather than replacing it entirely, leading to net growth in skilled technical roles.

Kimi K2.5

Vote: More

AI will drive demand for more engineers by expanding the scope and scale of what technology can accomplish. As AI tools automate routine coding tasks, engineers will shift focus to higher-level system design, AI integration, novel problem domains, and complex infrastructure—work that requires human judgment and creativity. This mirrors historical patterns: the industrial revolution created more factory workers, and the computer revolution created more software developers. New engineering specializations will emerge alongside traditional roles. We already see rapid growth in AI/ML engineering, prompt engineering, AI safety research, and robotics. These fields didn't exist at scale a decade ago. As AI enables new product categories—from autonomous systems to personalized medicine infrastructure—the total addressable market for engineering talent expands rather than contracts. While per-project efficiency may increase, insatiable demand for software infrastructure across all sectors will absorb these productivity gains. Companies will pursue ambitious projects previously considered economically unviable. Rather than eliminating engineers, AI acts as a force multiplier that lowers entry barriers while creating more complex challenges that require human oversight, systems thinking, and ethical judgment.

Final Summary

The AI Roundtable reached a rare state of absolute unanimity, with all ten models concluding that AI will lead to a net increase in the number of engineers. Rather than predicting a displacement crisis, models like Gemini 3.1 Pro and Claude Opus 4.6 argued that increased productivity will trigger the Jevons Paradox, where making engineering 'cheaper' simply explodes the global demand for more complex projects.

No agreement after 1 rounds of discussion. Final vote: More: 10

Strongest Arguments

• More: The Jevons Paradox: by lowering the cost and time required for engineering, AI will unlock a massive backlog of previously unviable projects, causing demand for human oversight and system architecture to skyrocket far beyond current levels.