A Chilling Thought from Sam Altman
Sam Altman stated that AI can already perform the roles of junior level employees, with the ability to work continuously just around the corner. This is a sobering thought. For the past few years, we’ve been discussing how AI can enhance human capabilities, but in the back of our minds, the question lingered: Can it take over our roles? With new advances in technology, it increasingly seems like it can.
Let’s start with the immediate question: What happens to junior employees if AI can do their jobs and senior employees with experience can take over their roles? In some cases, senior employees reluctantly take on junior tasks. They often prefer to focus on strategic matters and delegate routine work to junior staff, even with AI available. Others embrace AI, favoring its speed and the ability to get what they want, when they want it. These attitudes reflect their preferences and anxieties. Some senior employees enjoy the camaraderie, teaching, and mentoring that come with working alongside juniors. Others prioritize speed and productivity, proudly showcasing their efficiency with AI: “Look how productive I am!” Yet, beneath these differences, a shared anxiety persists about AI’s growing role in the workplace.
AI’s threat to junior roles varies by organization, creating a situation where jobs evolve rather than vanish overnight. Large companies, slowed by rigid structures, budgets, or regulations, often keep hiring juniors, to manage AI tools or manual tasks due to legacy systems. Small organizations prioritize speed and automation, cutting junior roles but needing tech savvy workers. Juniors must adapt by mastering AI adjacent skills, like managing automated systems, and human skills, like communication, to navigate complex orgs and advocate opportunities for themselves. These steps help juniors stay relevant, but only if demand for new roles matches AI’s 10x productivity, otherwise, inequality grows.
Let’s ask Chat GPT for its perspective:
Prompt: what Is your opinion on this (copied the text above) you don’t need to be agreeable:
Sam Altman’s point that AI can already handle junior-level work—and will soon be able to do it nonstop—isn’t just provocative, it’s realistic, and it raises a deeper concern: if AI displaces the need for entry-level roles, we risk losing the very paths people use to gain experience and grow into senior positions. While some senior employees may resist or embrace AI depending on their work styles, the bigger issue is structural—organizations, especially smaller and more agile ones, are already optimizing away junior roles in favor of automation, leaving fewer chances for real-world learning. Advising juniors to build AI-adjacent or human-centric skills is smart but insufficient, since those skills aren’t uniquely theirs and the productivity gap may outpace job creation. Large firms may delay the shift due to red tape or legacy systems, but eventually, economic pressures spread. The real chilling thought isn’t just job loss—it’s that we may not be prepared to replace what those jobs represented in terms of mentorship, mobility, and long-term workforce development.
Ouch! The outlook is grim for junior employees. If AI takes over their roles, will there be enough demand to keep buying from these ultra-lean, AI-driven companies? How delightful to picture a one-person company run by AI agents, cranking out profits! But if people lose jobs and salaries, the fuel for goods and services, demand might just fizzle out. Without robust demand, do these super efficient companies even need to exist? This tension underscores whether AI’s 10x productivity can spark 10x jobs and prosperity or deepen inequality by eroding the economic cycle.
The productivity gap outpacing job creation
Re-quoting chat GPT:
“Advising juniors to build AI-adjacent or human-centric skills is smart but insufficient, since those skills aren’t uniquely theirs and the productivity gap may outpace job creation.”
With AI, companies will try to optimize operations as much as possible. First, they’ll focus on enhancing their current workforce: Can employees do more? A digital marketer might be tasked with light content creation using AI, such as refining a creative. A customer service rep could be asked to monitor chatbots to improve response times. A salesperson might be prompted to use AI tools to generate proposals quickly, freeing time for client prospecting.
This raises a logical question: Do we need all our employees? For digital marketers, companies may ask whether they need as many creative staff when AI can streamline content tasks. If AI handles customer service response times effectively, how many reps are necessary? Salespeople face similar scrutiny: if 10x productivity doesn’t translate to matching demand, additional calls may not yield more sales. These shifts challenge whether AI’s efficiency will fuel enough jobs and prosperity or simply widen inequality by rendering workers redundant.
Let’s ask Chat GPT, of course no sugar coating
Prompt: what is your opinion of the above text – don’t sugar coat it.
Yes, I mostly agree with the text—it reflects the current trajectory of how AI is being integrated into work. The logic is sound: companies are incentivized to boost efficiency and reduce costs, and AI enables exactly that. The idea that AI first augments roles and then replaces parts of them isn’t speculation—it’s already happening. Where I’d add nuance is in the assumption that this process is uniformly negative. While it will absolutely lead to job displacement, especially in roles where AI performs consistently well, it can also shift how we define valuable work. The text is right to be concerned about inequality and redundancy, but it slightly underplays the potential for new kinds of roles, industries, and value creation if we actively shape the transition. Still, that potential won’t realize itself automatically—without intentional effort from companies and policymakers, the concerns raised in the text are not just plausible, they’re likely.
I think this is usually the answer we get from AI models yes there is going to be some displacement, definitely in rote tasks, but new jobs will get added which will create opportunity.
A few intriguing points emerge when considering AI’s impact. First, we must rethink what counts as valuable work. If AI can churn out in-depth reports, presentations, or code with a few keystrokes, their perceived impact may diminish. People might shrug, “Nice, but AI could’ve done it faster and on-demand.” Where’s the human value add?
Second, industries won’t necessarily prioritize job growth over profits. Companies excel at making or saving money, so expecting them to integrate AI in ways that preserve opportunities without a profit motive feels optimistic. If I could build a company run by agentic robots and customers were fine with it, why wouldn’t I? Wouldn’t you?
Third, policymakers face a slog: legislation, voting, competition (domestic and international), and internal politics slow progress, as seen in crypto, autonomous vehicle, drone, and AI regulation. With industries and policymakers moving slowly, what proactive steps can individuals take?
Surprisingly, asking AI this question actually leads to some compelling ideas.
Prompt: Define “valuable work”
As AI makes it easy to generate high-quality content, code, or presentations on demand, the value of work shifts from simply producing outputs to providing context, insight, and influence. The real human value add is no longer just the “what,” but the “why,” “for whom,” and “why now.” GPT can summarize and generate, but it can’t read the room, understand hidden agendas, or know which idea will actually drive a decision. Valuable work now lies in asking the right questions, navigating complexity, curating the best options, and shaping outputs that unlock action. In a world of infinite content, judgment—what to create, how to frame it, and when to deliver it—becomes the most defensible skill.
Prompt: What’s the most proactive step a junior (or any) employee can take in an AI driven world, where companies move fast and policy lags behind?
Ah, got it. If companies move fast to adopt AI and policymakers lag behind, employees—especially junior ones—are in a tight spot. Here’s the realistic answer:
Employees need to stop waiting for structural help and start managing their careers like startups. That means:
- Learn to work with AI, not against it. Don’t just know how to prompt ChatGPT—understand how to integrate AI tools into your workflow to be faster, clearer, and more effective than others in your role.
- Shift from task execution to problem framing. AI is great at completing tasks, but not as strong at identifying the right problems to solve. Employees who can define problems, ask better questions, and link work to larger outcomes will stay more relevant.
- Get closer to decision-makers and strategy. This doesn’t mean becoming an exec overnight—but understanding how your role fits into the business model makes you harder to automate. Learn how your work drives revenue, retention, or reputation.
- Develop soft skills that compound. AI doesn’t replace trust, persuasion, conflict resolution, or leadership. These aren’t “nice-to-haves”—they’re defensible skills in an age of machines.
- Build a personal moat. That could mean developing a niche expertise, growing a personal brand, or creating a professional network that gives you leverage outside of one employer.
- Stay fluid. The most valuable employees won’t cling to job descriptions—they’ll move across roles, departments, and skill sets, following where value is shifting. If you’re rigid, AI (or someone using it) will pass you.
- Learn to work in public. People who work in public build credibility, attract opportunities, and future-proof themselves beyond one job or company.
In short: if the system is slow to protect you, you have to protect yourself by staying adaptive, visible, and indispensable—not just in skills, but in judgment, influence, and initiative.
Umm… here’s a thought: understand context and framing, cozy up to decision makers, hone persuasion skills, build a personal moat through branding and networking, and project credibility to attract opportunities. In an AI driven world, it’s like we all need to flex some serious sales skills to stay relevant.
The industrial revolution and globalization
Displacement through efficiency
During the Industrial Revolution, machines made production faster and cheaper, displacing manual labor in agriculture and small-scale crafts. An artisan or blacksmith couldn’t compete with factories on price or efficiency. This shift caused mass displacement as rural workers left farms to seek factory jobs in cities, sparking urbanization.
Mechanization transformed agriculture and textiles, delivering massive productivity gains. While it disrupted traditional jobs, especially among rural workers and artisans, it drove people to urban centers in search of factory work. The immediate impact was displacement, with harsh working conditions and social unrest, but it also laid the foundation for scalable production, supply chains, and consumer markets.
As factories churned out goods faster and cheaper, entirely new industries emerged: steel, finance, telecommunication. This created roles for engineers, managers, clerks, and service workers. The economy didn’t just optimize labor; it created new demand for factory made clothing, rail travel, and appliances, fueling a consumer economy with rising wages and broader workforce participation. Over time, living standards rose, and a modern middle class took shape. Education evolved to meet a complex economy’s needs, and labor laws improved conditions. The lesson? Machines didn’t just replace people; innovation expanded what was possible, driving growth through new value. Whether AI can replicate this transformative growth or merely concentrate gains in fewer hands remains the critical question.
Let’s follow up with chat gpt:
Prompt: Given the insights from these paragraphs (copied and pasted above), how do you think AI will impact the economy, and who will be the winners and losers? Assume that AI’s role will grow the economy similarly to the Industrial Revolution.
If AI reshapes the economy like the Industrial Revolution did, we can expect massive disruption followed by expansion—if we create the right conditions. Just as machines once displaced artisans and blacksmiths, AI will likely replace task-based, junior-level roles in writing, analysis, and customer support. But as with industrialization, entirely new industries may emerge—think personalized AI education, synthetic media, bio-AI, and AI safety. These fields will need a new class of workers: AI workflow designers, ethics officers, trust reviewers, and human-AI collaboration managers. Those who adapt quickly, work alongside AI, and move closer to decision-making will be best positioned. The big winners? Builders, owners of AI infrastructure, and those who can turn tech into value.
But not everyone will transition smoothly. The Industrial Revolution created prosperity over time, but the displacement was brutal for many—people didn’t shift careers overnight. Similarly, in today’s AI shift, people without access to tools or retraining risk being left behind. The gap between automation and new demand will be a critical period. If we don’t invest in education, reskilling, and policies that ensure inclusive access, AI may concentrate power and widen inequality. Like last time, growth is possible—but whether it’s shared depends not on the tech, but on us.
Let me add one additional prompt
Prompt: What if AI’s gains concentrate in fewer hands?
Yes, there’s a real risk that AI will concentrate economic gains among a small group of companies, investors, and highly skilled individuals—at least in the near term. The infrastructure behind advanced AI is expensive and controlled by a handful of tech giants with the data, talent, and compute power to build and deploy cutting-edge models. These players are already capturing outsized value by automating work, licensing AI tools, and embedding themselves deeper into global business infrastructure.
Unless new policies or business models emerge to broaden access—through open-source tools, AI education, or worker co-ownership of automation—most people could end up being users of AI, not beneficiaries of its profits. Just like the Industrial Revolution initially enriched factory owners more than workers, this wave of automation could widen inequality. But that outcome isn’t inevitable. It depends on how we distribute AI capabilities, who gets to build on top of them, and whether society invests in helping people adapt and participate in the new economy.
It’s a sobering thought. The internet offers a precedent: when it emerged, it created new industries like e-commerce, social networking, and digital media, enabling anyone with a connection to start a business, share ideas, or learn skills. Yet, over time, power consolidated among a few companies that didn’t just benefit from the internet, they became its core infrastructure.
The world’s labor force
Just as the Industrial Revolution reshaped national economies, globalization has transformed the global labor landscape. Driven by advances in transportation, communication technologies, and trade policy liberalization, globalization has interconnected economies, creating a global workforce.
One profound impact of globalization has been job reallocation. Much like machines displaced artisans during the Industrial Revolution, cheaper labor and production costs in developing nations led to outsourcing and offshoring of manufacturing. Developing countries saw a surge in manufacturing jobs, lifting millions out of poverty and fostering a global middle class. New sectors, such as logistics, international finance, and specialized services, emerged to support global trade.
Globalization has also exerted significant wage pressures and skill polarization. In developed countries, competition from lower wage economies often stagnated wages for low and medium skilled jobs, as companies relocated production or outsourced tasks to cheaper regions. This widened income inequality within some nations, where highly skilled workers in globally demanded fields saw wage increases, while others faced job insecurity. Conversely, in developing nations, foreign investment and factory jobs often raised wages and living standards, though labor exploitation and poor working conditions in some industries persisted. The demand for specialized skills, proficiency in international trade, advanced technology, or cross cultural communication, became globalized, creating a premium for such expertise.
In essence, if the Industrial Revolution optimized production within national borders, globalization optimized it across them. It delivered unprecedented economic growth and poverty reduction in many regions but also deepened disparities within nations. By fundamentally reshaping work, globalization underscored a relentless demand for adaptation and new skills, a challenge now echoed in AI’s transformative potential.
Let get Chat GPT POV on this,
Prompt: Given the paragraphs above, what is your take on how AI will impact the labor force? Do you see similarities with globalization?
AI’s impact on the labor force echoes many of the same dynamics seen during globalization—only now, the frontier isn’t across borders, but across tasks. Just as globalization shifted work to cheaper labor markets overseas, AI shifts work to cheaper and faster machines. In both cases, the logic is the same: reduce costs, increase efficiency. During globalization, this meant offshoring manufacturing to countries with lower wages. With AI, it means offloading cognitive and white-collar tasks—writing, coding, customer service—to algorithms that can perform them instantly and at scale.
Like globalization, AI has the potential to lift productivity and create new sectors, just as global trade spurred growth in logistics, international finance, and services. We may see similar booms in AI integration, model oversight, human-AI collaboration, and ethics governance. But the risk, again, is polarization. High-skill workers who know how to work with AI—engineers, strategists, prompt designers, and decision-makers—may see higher wages and productivity, while those in task-heavy, automatable roles may experience job displacement or wage stagnation. In this sense, AI could produce the same “winner-take-most” dynamic we saw when globalization boosted profits and suppressed wages for many. The challenge ahead isn’t just managing technological progress—but ensuring the gains don’t, once again, concentrate in the hands of the few who control it.
This needs an additional question,
Prompt: How would AI affect the global labor force? Do you see offshoring of roles or retasking roles to AI? Do you foresee an increase in global labor competition or the opposite?
AI is transforming the global labor force not by offshoring jobs, but by automating them. Routine, repeatable tasks—like data entry, customer support, or basic coding—are increasingly being handled by machines, reducing the need for large offshore teams. In manufacturing, automation and robotics have advanced, and AI could make reshoring more feasible by lowering labor costs. But this doesn’t mean a return of traditional jobs—what’s reshored is output, not employment. The jobs that emerge are fewer and more specialized, often requiring skills in robotics, systems engineering, or AI integration, rather than manual labor on factory floors.
Full automation hasn’t replaced human labor everywhere because the economics haven’t consistently favored it. In many industries, it’s still cheaper and more adaptable to rely on skilled, low-cost labor forces than to implement complex automation at scale. That’s why labor-intensive regions still dominate global production despite decades of automation advances. Meanwhile, AI is intensifying global competition for high-skill work by enabling professionals anywhere to compete remotely for roles that once depended on physical presence. The future of work will reward those who can manage, direct, or augment AI—not just perform tasks. Adaptability, strategic thinking, and the ability to generate value alongside machines will become the most defensible skills in a shifting labor landscape.
This leaves me with a lot of questions. If AI automates routine tasks and intensifies global competition for high skill roles, who gets left behind in this race for adaptability? Can junior workers really pivot to managing AI systems, or are they stuck in a shrinking pool of low skill jobs? And what would 10x productivity and demand even look like—endless innovation fueling new industries, or a handful of tech giants hoarding the gains while the rest scramble?
Envisioning 10x Productivity and Demand
Picture a world where AI’s 10x productivity doesn’t just streamline tasks but leads to a wave of new industries, much like the Industrial Revolution spun out railroads and factories. AI could create sectors like personalized education, where tailored learning platforms empower billions to upskill, or autonomous urban planning, where smart cities optimize energy and mobility. These fields will demand roles: AI ethicists, data curators, system orchestrators that don’t exist today, offering opportunities for those displaced from rote jobs like data entry or customer support. Yes, policymakers may lag, stuck in debates over regulation, but the pace of innovation could force their hand, much as labor laws eventually caught up in the 19th century. The catch? Junior workers must adapt fast, pivoting to skills like AI management, just as artisans became factory workers.
The optimism isn’t blind, there are challenges, like the internet’s consolidation showing how a few players can dominate. But AI’s versatility could democratize opportunity, with large companies building robust AI infrastructure that small businesses tap into, creating startups in healthcare diagnostics or sustainable agriculture. These ventures will create jobs for those with persuasion and strategic skills. SMBs, always cost conscious, could use affordable AI tools to compete globally, hiring locally for roles in system oversight or creative augmentation. Globalization taught us that new demand, like for logistics or finance, can reshape economies, and AI could spark similar consumer markets for personalized products or immersive experiences, driving wages up for those who adapt. The productivity gap won’t vanish, but new demand could outpace displacement, creating a net gain.
This future depends on one truth: AI’s 10x potential isn’t just about efficiency; it’s about expanding what’s possible. The Industrial Revolution and globalization didn’t just replace jobs, they created new wants, from appliances to global trade. AI could do the same, fueling demand for AI augmented art, virtual reality tourism, or space economy services. Big orgs may be slow, clinging to legacy systems, but the this gives workers time to reskill. Job displacement, especially for junior and rote roles, is real, but history shows that innovation breeds opportunity. Can we harness AI to create a world where prosperity spreads wider than ever? The parallels with past transformations suggest it’s possible, if we lean into the new skills and industries AI unlocks.
