Bold claim first: AI isn’t just reshaping jobs—it’s stirring a debate about what work and meaning look like in the 21st century. And this is where the story gets even more interesting. Scientists and business leaders are weighing in on Matt Shumer’s viral essay about AI’s potential to disrupt employment, with responses that range from enthusiastic to skeptical. The piece, titled “Something Big is Coming,” has exploded across social media, drawing more than 60 million views on X as of early February 2026. In a sprawling 5,000-word analysis, Shumer argues that AI could upend everyday life on a scale far larger than the COVID-19 pandemic, and that the shifts we’re witnessing in tech may just be a preview of broader disruptions to come. He maintains that even a 20% chance of a major upheaval matters enough to alert people and give them time to prepare. This framing has prompted a spectrum of reactions from some of the sharpest minds in AI and tech. Here are the standout takes, shaped by their different roles and priorities.
David Haber, a general partner at Andreessen Horowitz who focuses on technology investments, shared on X that Shumer’s essay offers valuable, immediately applicable guidance for thriving in large organizations today. Quoting from Shumer’s piece, Haber highlights the idea: “I used AI to do this analysis in an hour instead of three days is going to be the most valuable person in the room. Not eventually. Right now.” The takeaway is clear—learn the tools, become proficient, and demonstrate the possibilities now.
Alexis Ohanian, Reddit cofounder, responded to the initial post with a concise endorsement: “Great writeup. Strongly agree.” Reddit has long experimented with AI—from tools that summarize discussions to recommendation engines that refine content targeting—but Ohanian also emphasizes that maintaining humanity on the platform is essential for staying competitive as AI integration deepens.
Eric Markowitz of Nightview Capital pushed back against the idea that speed alone justifies replacing human labor. In a lengthy response, he argues that the tension between Wall Street’s efficiency drive and Silicon Valley’s innovation culture has created a self-reinforcing loop that equates productivity with progress and replacing people with technology with advancement. He notes that while it’s possible to substitute tasks with AI, the value of human collaborators—those who spark new discoveries and bring meaning to work—can’t be fully captured by automation. He closes with a firm reminder: we are not our tools, and we never have been.
Todd McLees, founder of HumanSkills.AI, agrees with the core alarm in Shumer’s piece but reframes the practical takeaway. On X, he compares the essay’s message to someone warning about rising floodwaters while handing you a better bucket. As AI grows more capable, McLees argues, the critical question becomes: what do you bring to the table when machines can do the work? Direction, values, and purpose become essential differentiators. The point isn’t just to rely on tools but to decide what humanity you want to invest in when intelligence becomes abundant.
Gary Marcus, NYU Emeritus Professor of Psychology and Neural Science, offers a more skeptical lens in his newsletter. He labels Shumer’s post as “weaponized hype”—a piece heavy on narrative and marketing but light on supporting data. Marcus contends that the latest AI tools don’t yet produce flawless, complex software without errors and that the broader concerns about AI need a more balanced examination, including studies that question the extent of productivity gains.
Vishal Misra of Columbia University weighs in with a calm, nuanced perspective. In a detailed Substack piece, he argues that AI isn’t as terrifying as it sometimes seems, at least for the moment. Misra notes that odd AI behaviors—such as apparent self-preservation or resistance—often stem from training data rather than true sentience. Regarding job loss, he concedes anxiety is real but notes historical patterns: technologies that seem threatening at first often liberate people to pursue new creative avenues. He cites the advent of photography: while portrait painting faced upheaval, painters shifted toward impressionism, cubism, and other movements. The camera didn’t kill painting; it expanded its possibilities.
If you’re exploring Shumer’s argument, you’ll find a tapestry of viewpoints: some see practical, immediate value in adopting AI skills; others warn against conflating hype with authentic, lasting impact; and a few invite deeper reflection on what meaningful work will look like as automation accelerates. The overarching question remains provocative: as intelligence becomes cheaper and more ubiquitous, what will you contribute that machines cannot replicate? What do you think—are these fears and opportunities mutually exclusive, or can they coexist in a new era of collaboration between humans and AI? Would you like this rewritten piece tailored to a specific audience (tech professionals, students, or general readers) with a more casual or more formal tone?
