AI Creates Jobs Through Increased Complexity and Management Demand

This conversation, featuring insights from advertising industry analyst Brian Wieser and economist Nancy Lazar, challenges conventional fears surrounding Artificial Intelligence by reframing it as a catalyst for job creation through increased complexity, not job displacement. The core implication is that instead of fearing AI's automation capabilities, we should prepare for a future where managing AI-driven processes demands more human oversight, mirroring past technological shifts. This perspective is crucial for business leaders, policymakers, and individuals seeking to navigate the evolving economic landscape, offering a strategic advantage by anticipating a demand for new skill sets and management roles rather than widespread unemployment. It suggests a hidden consequence of AI is not obsolescence, but rather a demand for more sophisticated human management.

The Unforeseen Demand: How AI Could Actually Create Jobs

The prevailing narrative around Artificial Intelligence is one of impending job losses, a fear that automation will render vast swathes of the workforce obsolete. However, this conversation introduces a compelling counter-argument, most notably from Brian Wieser, a seasoned analyst of the advertising industry. Wieser posits that AI, rather than decimating employment, will paradoxically necessitate more human involvement due to the inherent complexity it introduces. This isn't just a hopeful outlook; it's a perspective grounded in historical precedent, particularly within the ad tech sector.

Wieser points to the automation of media buying, known as ad tech. The expectation was that this technology would streamline and automate the entire industry, leading to fewer jobs. Instead, the reality proved different. The introduction of sophisticated automated systems created new layers of complexity in managing campaigns, analyzing data, and optimizing performance. This complexity, Wieser argues, didn't eliminate the need for people; it shifted the demand towards individuals capable of managing these new, intricate processes. The ad tech revolution, in his view, became a job formation engine, not a destroyer.

"I argue it's going to cause a need for more people, because that's more complexity. And the complexity is what requires people to manage the processes. We already saw this in ad tech. Ad tech, meaning the automation of those core media buying. It was meant to automate the whole industry. No, it caused the need for more people."

-- Brian Wieser

This insight has profound implications. If AI follows a similar pattern, the immediate, visible impact of automation might be overshadowed by a less obvious, but more significant, downstream effect: a surge in demand for human capital to oversee and manage the AI systems themselves. This is where conventional wisdom falters. It focuses on the direct automation of tasks, failing to account for the systemic response to that automation. The system doesn't just accept the automated output; it requires human interpretation, strategic deployment, and continuous refinement, especially as AI's capabilities evolve. This creates a feedback loop where increased automation leads to increased complexity, which in turn leads to increased demand for skilled human managers.

Nancy Lazar, an economist with Piper Sandler, echoes a constructive sentiment about the broader economy, suggesting a potential reacceleration supported by policy efforts and a healthier employment landscape. While her focus isn't solely on AI, her observation that the US economy is "actually okay" and moving away from a bifurcated state provides a fertile ground for Wieser's thesis. If the economy is strengthening and small businesses are benefiting, there's a greater capacity to absorb the new roles that AI complexity might create. Lazar's "constructive feel about the multiplier effect of artificial intelligence" hints at this broader economic benefit, suggesting that AI's impact could extend beyond mere efficiency gains to genuine economic expansion fueled by new industries and job categories.

The conventional fear often centers on AI replacing creative roles as well. Wieser suggests this too will be a pattern of complexity creation, not elimination. The tools might change, but the need for human creativity, strategic direction, and nuanced understanding of audience and market will likely be amplified by the need to effectively leverage AI outputs. This isn't about AI doing the creative work, but about humans using AI as a powerful, complex tool that requires expert handling.

"And you're going to see that in the creative side of the agencies as well. I can't emphasize enough how important that comment is, whether you agree or disagree, or even if it's removed from advertising and media and all that. We don't have a clue what AI is going to do. And there's somebody who's saying it's going to be a job formation machine. We will see."

-- Brian Wieser

The advantage for those who grasp this perspective lies in proactive preparation. Instead of investing solely in defending against job displacement, organizations and individuals can focus on developing the skills needed to manage, interpret, and strategically deploy AI. This might involve training in data science, AI ethics, system integration, or even specialized creative roles that leverage AI as a co-pilot. The delayed payoff of this foresight--building a workforce equipped for the complex AI-driven future--could create a significant competitive moat. Those who anticipate this demand for management and specialized oversight, rather than succumbing to fear of obsolescence, will be better positioned to thrive. The "hidden consequence" of AI isn't mass unemployment, but a sophisticated evolution of the labor market demanding more, not fewer, skilled humans to navigate its intricacies.

Key Action Items

• Immediate Action (Next 1-3 Months):

  • Skill Assessment: Conduct an internal assessment of current team skills against the potential demands of managing AI systems (e.g., data analysis, AI interpretation, prompt engineering).
  • AI Literacy Training: Implement foundational AI literacy programs for all employees to demystify the technology and foster a proactive mindset.
  • Pilot AI Integration: Identify a low-risk process for piloting AI tools, focusing on understanding its operational complexity and the human oversight required.

• Short-Term Investment (Next 3-6 Months):

  • Targeted Upskilling: Invest in specialized training for key personnel in areas identified as critical for AI management (e.g., AI ethics, advanced data analytics, AI system integration).
  • Process Redesign: Begin redesigning workflows to incorporate AI tools, explicitly mapping how human oversight will be integrated rather than replaced.

• Medium-Term Investment (Next 6-18 Months):

  • Develop AI Management Roles: Create new job descriptions and roles focused on managing AI systems, analyzing AI-generated insights, and ensuring ethical AI deployment.
  • Strategic Partnership Exploration: Explore partnerships with AI technology providers or consultants to gain deeper insights into managing complex AI deployments and their associated human capital needs.
  • Long-Term Skill Forecasting: Develop a 2-3 year forecast for the skills required to manage an increasingly AI-integrated business environment, informing future hiring and development strategies. This pays off in 12-18 months by building a team ready for complexity.