Worth Magazine Covers Brian Solis Keynote at SXSW 2026 – Augmentation Is the New Productivity

At the South by Southwest (SXSW) festival in Austin, Texas, Brian Solis, a digital anthropologist and head of global innovation at ServiceNow, delivered a keynote that highlighted the evolving role of artificial intelligence (AI) in the workplace. His remarks centered on the idea that while many individuals use AI to enhance existing workflows, the true potential of AI lies in its ability to augment human intelligence, enabling users to explore new possibilities rather than simply automate routine tasks.

Solis pointed out a prevalent trend in how people utilize AI tools. He noted that many individuals approach generative AI primarily as a means to offload their existing responsibilities—such as using AI for summaries instead of reading full texts, or generating drafts instead of writing from scratch. While this instinct to streamline tasks is understandable, Solis warned that it limits the potential benefits of AI, effectively setting a ceiling on what can be achieved.

The Automation Trap vs. Augmentation

During his presentation, Solis articulated two contrasting futures shaped by AI. The first scenario involves AI as a tool for outsourcing cognitive tasks, leading to a scenario where knowledge work is merely accelerated and made cheaper. In this future, AI serves to replicate existing processes rather than innovate them. Conversely, the second scenario envisions AI as an extension of human intelligence, facilitating exploration and creativity in ways that would otherwise remain inaccessible.

Solis emphasized that the current discourse around AI at technology conferences often revolves around efficiency and productivity. Executives and investors frequently discuss the potential for AI to enhance productivity and the implications for job displacement. However, Solis proposed a different perspective: the divide may not be between humans and machines, but rather between those who leverage AI to enhance their cognitive capabilities and those who rely on it to replace their thinking.

Research supports Solis's assertions about the productivity gains associated with generative AI. For instance, studies conducted by Microsoft researchers indicated that developers using GitHub Copilot completed programming tasks approximately 55% faster than those who did not use the tool. Other findings suggested productivity improvements ranging from 14% to 40%, depending on the complexity of the tasks involved. Despite these improvements at the task level, economists predict that generative AI will only contribute modestly to overall productivity growth, estimated at just 0.5 to 0.7 percentage points annually over the next decade.

This paradox raises questions about how organizations are deploying AI. Solis referred to this phenomenon as the "automation trap," where the initial focus is on reducing labor costs and streamlining existing processes. While these applications yield quick, measurable benefits, they often fail to fundamentally transform the nature of work itself. Solis cautioned that if companies continue to view AI primarily as a means to automate past practices, they risk confining themselves to a limited future.

The Cognitive Consequences of AI Use

To counter this trend, Solis advocates for an approach centered on augmentation. He encourages workers to leverage AI not just for efficiency but to explore new avenues: generating alternative strategies, modeling complex scenarios, and testing innovative ideas. This distinction between automation and augmentation is critical, as it may determine who stands to gain the most from AI technology. Early evidence suggests that individuals who integrate AI deeply into their workflows can achieve significant advantages, with some studies indicating productivity differences of several multiples between advanced users and casual users.

However, the use of AI is not without its challenges. For example, a randomized study of experienced open-source developers revealed that those utilizing AI assistance sometimes took 19% longer to complete tasks than their independent counterparts. This delay is attributed to the need for additional review and debugging of AI-generated outputs. Thus, rather than eliminating work, AI often redistributes it.

The rise of generative tools has also led to an oversaturation of generic content across professional platforms, resulting in what Solis describes as "AI slop." This phenomenon complicates the identification of genuine expertise, as the ease of generating persuasive text has led to a proliferation of formulaic and indistinct content.

Furthermore, research indicates that heavy reliance on generative AI may alter cognitive processes. Studies have shown that individuals using AI tools exhibit lower cognitive engagement and weaker memory retention compared to those who write without AI assistance. This trend resembles earlier technological shifts, such as the widespread adoption of GPS, which led to a decline in spatial memory. Solis warns that generative AI could similarly diminish the intellectual engagement that characterizes human thought.

To address these challenges, Solis argues that effective AI adoption requires strong leadership. He notes that organizations are increasingly measuring "AI proficiency" to assess how well employees can utilize generative tools. While these metrics can be beneficial, they may inadvertently reinforce a mindset focused on incremental improvements rather than transformative thinking.

Solis proposes a thought experiment he calls WWAID—"What Would AI Do?" This exercise encourages leaders to envision how an intelligence native to a specific situation might approach problem-solving, prompting teams to explore innovative strategies rather than simply optimizing existing workflows. This shift in perspective could be crucial in determining which organizations thrive in an AI-driven landscape.

Despite the growing discourse around generative AI, its current impact on the economy remains limited. Data from the Federal Reserve Bank of St. Louis indicates that only about 5.7% of total U.S. work hours involve generative AI tools. While adoption is on the rise, the technology is still far from ubiquitous, suggesting that the productivity divide Solis describes is just beginning to take shape.

As the AI economy evolves, three distinct profiles are emerging: the AI-dependent, who rely heavily on AI for quick answers; the never-AI-ers, who maintain independent thinking but risk falling behind; and the AI-augmented, who effectively partner with AI to enhance their cognitive capabilities. According to Solis, the latter group is poised to succeed by combining speed with originality, ultimately defining the competitive advantage in an AI-driven world. "The work you want to do," he concluded, "is the work you couldn’t do without AI—and that AI can’t do without you."