In the not-so-distant past, launching a successful business required a team of specialists. You needed marketers, developers, designers, operations managers, customer service representatives, and countless other roles to execute a cohesive vision. The idea of a single person launching and scaling a company to great success was rare and usually perceived as unrealistic. However, advancements in artificial intelligence are flipping that notion on its head.
Now, imagine a solo entrepreneur sitting in a coffee shop with a laptop and a vision. She is not burdened by scheduling meetings, coordinating multiple departments, or waiting for approval cycles. Instead, she leverages an intelligent AI assistant capable of executing across disciplines. Within minutes, the AI has synthesized her business idea, generated a full business plan, created a user-centric marketing campaign, developed a functioning product prototype, and even conducted market testing simulations.
This is not fiction—it is an emerging reality. AI tools today can write code, generate images, develop strategies, test user interactions, and offer data-driven insights. What once required an entire team is now within the reach of a single, resourceful individual empowered by cutting-edge technology. The implication is profound: the very premise of teamwork as a cornerstone of business success is being challenged.
The efficiency and scale offered by AI mean that smaller, leaner operations are no longer at a disadvantage. They may now be more agile and adaptive than their larger counterparts. This has significant implications for industries built around collaborative processes, such as software development and project management. Agile, once the gold standard for high-performance team collaboration, now faces a critical juncture.
The Myth of Team Dependency in Modern Business
Teamwork has been one of the most celebrated values in the modern workplace. From motivational posters to corporate leadership programs, the emphasis has always been on the power of collaboration. The phrase “there’s no ‘I’ in team” is a mantra many have grown up hearing. According to traditional wisdom, the best ideas emerge from group discussions, the most innovative products are built through cross-functional collaboration, and no single person can succeed without the support of a team.
This belief has been deeply ingrained in our educational and professional systems. School projects are group-based, job interviews assess teamwork abilities, and promotions often consider leadership and collaborative performance. However, AI is starting to disrupt this long-held narrative.
When one person can use AI to generate ideas, validate assumptions, design solutions, and execute plans in real-time, the rationale for team dependency weakens. Tasks once deemed too complex or time-consuming for a single individual are now manageable with the help of AI copilots and assistants. Moreover, the delays and inefficiencies associated with group coordination, consensus-building, and interpersonal conflicts are minimized.
AI also eliminates the need for domain-specific knowledge across multiple areas. A single user no longer needs to rely on specialists for insights into legal, marketing, finance, or UX. AI models trained on vast data sets can provide accurate, contextual information and recommendations across all these fields. This shifts the nature of work from collaborative execution to individual orchestration, where the human leads while the AI executes.
This evolution does not mean teams are obsolete, but it signals a transformation in how we understand and apply teamwork. It moves the conversation from “How can we work together to solve this?” to “How can I leverage technology to solve this most effectively?”
Agile Principles in the Age of AI
Agile methodology, since its inception, has revolutionized how software and product teams operate. By emphasizing small, self-organizing teams, rapid iteration, continuous feedback, and customer-centric design, Agile became the blueprint for innovation in a digital world. At its core, Agile values individuals and interactions over processes and tools. It champions team cohesion, transparency, adaptability, and cross-functional collaboration.
However, as AI tools become more sophisticated, the Agile landscape is shifting. One of the primary benefits of Agile was its ability to replace heavy, document-driven project management with dynamic, team-based problem-solving. But what happens when AI tools can now handle those same problems with greater speed, precision, and consistency?
Leading voices in the Agile community have already begun questioning some of its foundational practices. Henrik Kniberg, for example, noted that cross-functional teams may not be as essential in an AI-driven environment. Instead of requiring a designer, developer, product manager, and tester to work together, a single person with access to AI can fulfill these roles to a significant extent.
This does not mean Agile principles are invalidated. On the contrary, it may mean they need to evolve. The principles of adaptability, iterative development, and customer feedback still hold value. However, the mechanisms to achieve these outcomes may no longer require the same team structures. The retrospective, once a group activity aimed at optimizing team performance, may become a solo reflection supported by AI-generated insights.
This transition prompts deeper questions about what aspects of Agile are core values and which are merely practices that suited the constraints of a pre-AI world. Is it still necessary to have a daily stand-up when AI assistants track progress in real-time? Is backlog grooming still a manual process when AI can automatically prioritize tasks based on changing business goals?
Agile needs to demonstrate its agility. As AI reshapes the way work gets done, Agile methodologies must adapt to remain relevant and useful. The focus may shift from facilitating collaboration to enabling experimentation, speed, and resilience in increasingly solo or micro-team setups.
Historical Examples that Foreshadow the AI Shift
The current AI revolution is not the first time technology has challenged the structure of teams. History is full of examples where a single individual or a tiny team, empowered by technology, achieved what once took dozens or hundreds.
Take Instagram as a case in point. When it was acquired by Facebook for $1 billion in 2012, it had only 13 employees. WhatsApp had just 55 employees at the time of its $19 billion acquisition. These companies leveraged lean operations, high efficiency, and strategic technology use to punch far above their weight.
Plenty of Fish, the dating platform, was run by a single founder who managed the product, user experience, and backend operations largely on his own, generating millions in revenue before eventually being acquired. These examples were often treated as outliers or genius-driven exceptions. However, what was once extraordinary is becoming increasingly common.
With AI, the barriers to entry for entrepreneurship are falling. Skills that once required years of education and experience are now available on demand. Need a marketing campaign? An AI can generate one. Need to build a mobile app? An AI can code it. Need financial forecasting? An AI tool can analyze your data and present insights.
This means the next wave of billion-dollar businesses may not come from Silicon Valley offices full of engineers and designers, but from solo founders leveraging AI to create, test, and scale rapidly. The success of lean startups in the 2010s foreshadowed the potential of small, agile operations. The 2020s and 2030s may fulfill that promise on a much broader scale.
The implications for traditional team-based organizations are clear. The competitive advantage once held by large teams and resource-rich departments is diminishing. Agility, speed, and adaptability now matter more than scale. The organizations and individuals that harness AI effectively can outmaneuver larger, slower competitors. The fundamental assumption that more people equals more productivity is being rewritten by each passing innovation.
Redefining Job Descriptions in the AI Era
Traditionally, job roles existed because of human limitations. No single individual could master the nuances of marketing, legal compliance, product development, and customer support simultaneously. Work was divided into functional silos and managed through organizational structures. Departments like sales, finance, HR, and IT were created to compartmentalize expertise and responsibility.
However, the capabilities of generative AI are challenging this paradigm. What we are witnessing is a rapid convergence of roles into unified task execution. An individual empowered with AI can perform actions across disciplines with a level of competence that rivals specialists. This doesn’t mean that human expertise is becoming irrelevant, but the value of narrow specialization is shifting dramatically.
For example, a solo founder can now:
- Use AI tools to write and debug complex code
- Generate brand identities and design assets.
- Compose marketing copy tailored to different audience segments.s
- Set up automated customer service agen.ts
- Analyze business performance using predictive analytics.
- Write legal contracts using language models trained on case law.
This convergence means that instead of multiple people executing parts of a vision, one person becomes the orchestrator of a comprehensive workflow. Job descriptions begin to blur. The “marketer” is now also a strategist, product designer, and even a light developer. The “developer” is now also handling UX testing, documentation, and go-to-market planning. All facilitated by AI.
The future of work might not require fewer skills, but rather the ability to command a broader array of them at a sufficient level to leverage AI effectively. The “T-shaped” professional—broad knowledge with depth in one area—may give way to the “AI-augmented generalist” who can ideate, instruct, and iterate quickly across a wide surface area.
The Efficiency Problem with Teams
Even before AI entered the picture, team-based work carried inefficiencies. Decision bottlenecks, miscommunication, politics, and interpersonal conflict have always been part of organizational life. Meetings take time. Aligning priorities is difficult. Maintaining psychological safety in groups is a challenge. Coordination costs are real, and they scale with the size of the team.
The Agile methodology, while a powerful counter to rigid waterfall-style development, introduced its operational friction. Stand-ups, sprint planning, retrospectives, and backlog grooming—all are designed to foster collaboration, but they also consume precious time. The dream of seamless collaboration often turns into a nightmare of scheduling logistics and conflicting priorities.
AI strips away many of these inefficiencies. Rather than coordinating across people, AI can execute across domains instantly. There are no misunderstandings between an AI copywriter and an AI designer—they operate based on logic and intent, not emotions or culture. They never forget requirements or miss deadlines due to burnout. This doesn’t mean AI is flawless—it certainly makes mistakes—but it makes different kinds of mistakes, and at a vastly different speed and cost profile.
A solo operator using AI can iterate dozens of times before a traditional team even finishes a sprint review. With AI tools providing immediate feedback, there is less need for consensus-building or prolonged stakeholder review. Prototypes can be generated in minutes, tested virtually, and improved upon autonomously. This velocity is a fundamental shift. Teamwork slows things down—not due to laziness or incompetence, but due to the inherent friction of human collaboration.
Psychological Impacts and New Measures of Productivity
One often-overlooked consequence of this shift is the psychological experience of work. Team dynamics can be both energizing and draining. Humans are social beings, and collaboration can inspire creativity and motivation. But it can also produce anxiety, groupthink, and emotional fatigue. Politics and misalignment are common sources of burnout.
In an AI-first solo work paradigm, the stressors are different. There’s less social friction, but greater responsibility. Decision fatigue becomes internal rather than interpersonal. There’s no one to push back, question assumptions, or offer camaraderie during setbacks. Working solo with AI means a higher cognitive load per person, even if the total workload is reduced.
This will force a re-evaluation of how productivity is measured and experienced. In traditional teams, output is distributed and performance is reviewed across KPIs tied to roles. But for solo AI-augmented workers, productivity is tied to throughput per individual. This may lead to the rise of new metrics such as:
- AI leverage ratio: The amount of output one person can generate with AI tools
- Iteration velocity: How quickly a person can go from concept to prototype to deployment
- Autonomy index: The number of functions a person can independently operate with AI assistance
Just as we once optimized team-based productivity with Agile velocity charts and burn-down reports, we will now optimize individual productivity with dashboards that measure how effectively someone wields AI.
The Obsolescence of Middle Management
When Coordination Becomes Code
Middle management has traditionally existed to coordinate communication between teams, enforce processes, and align strategy with execution. These roles were critical in environments where information flow was slow, human bandwidth was limited, and visibility was poor.
AI changes that equation. When information can be synthesized and distributed instantly, the need for coordination decreases. When processes are enforced by software, not supervisors, the value of oversight diminishes. When strategy can be simulated and optimized by algorithms, human planners are less essential.
In AI-first companies, decision-making becomes decentralized but digitally orchestrated. Managers don’t need to track progress when AI dashboards offer real-time updates. They aren’t needed to enforce best practices when AI assistants automatically correct errors and suggest improvements. The idea that multiple layers of management are required to “keep people on task” becomes outdated when much of the task execution is automated.
The result is a flattening of the organizational hierarchy. This doesn’t mean leadership disappears—vision, ethics, and culture still need human stewardship—but it means fewer people are needed to manage others. Leadership becomes more about product direction and less about personnel supervision.
The New Leadership: From People Managers to AI Strategists
As traditional management functions decline, a new kind of leadership is emerging: the AI strategist. These are individuals who don’t manage people—they manage systems, tools, and AI resources. Their job is to ensure the organization’s intelligence infrastructure is properly aligned with its goals.
This involves:
- Selecting and integrating AI tools across departments
- Defining ethical and operational boundaries for AI usage
- Training staff to become AI-native contributors
- Interpreting AI-generated insights and translating them into action
- Ensuring resilience in case of AI failure or misuse
Instead of giving performance reviews, AI strategists are reviewing prompt engineering logs, accuracy scores, and system alignment reports. The soft skills of traditional middle management—conflict resolution, motivation, scheduling—are replaced by systems thinking, interface design, and data fluency.
The best leaders will not necessarily be the best communicators. They will be the best conductors—orchestrating the flow of knowledge, automation, and action across hybrid human-AI teams.
Teamwork Isn’t Dead—But It’s Different
Human Collaboration Still Has Value
Despite the power of AI, not all collaboration is obsolete. There are still things humans do better together than alone, even with the best technology. Creativity, emotional intelligence, strategic foresight, and ethical judgment benefit from diverse perspectives. Group dynamics can elevate ideas beyond what any one person might produce.
However, the form of collaboration is evolving. It’s less about synchronous teamwork and more about asynchronous contribution. Instead of five people in a meeting room debating next steps, it may be five AI-augmented individuals independently prototyping ideas, then submitting them for comparison and refinement.
This asynchronous model reduces coordination costs while still harvesting the benefits of multiple perspectives. It also allows for a wider pool of contributors—freelancers, global talent, or part-time collaborators—who don’t need to be in the same timezone, let alone the same office.
Micro-Teams and Fluid Coalitions
We are likely to see the rise of micro-teams: ultra-small, high-output groups consisting of one or two humans and a suite of AI tools. These micro-teams may form and dissolve quickly based on project needs. Instead of fixed roles, participants bring adaptable capabilities powered by their AI stacks.
This mirrors the dynamics seen in open-source software communities and digital creator economies, where people swarm around a project, contribute intensely for a period, and then move on. The difference now is that AI gives each participant far more leverage.
Coalitions will become fluid. A product designer may team up with a technical founder for a few weeks, bring an AI strategist into the loop, launch a prototype, and disband. No employment contracts, no HR processes, no org charts. Just results.
What Agile Can Learn—and How It Might Survive
Agile as a Mindset, Not a Method
Agile was never about standups and sprints. At its heart, Agile is a mindset: prioritize customer value, respond to change, deliver iteratively, and empower individuals. These principles are still relevant—but the way they’re executed must evolve.
Instead of ceremonies, we may see lightweight automation pipelines. Instead of team retrospectives, individuals will use AI feedback loops to evaluate their process. Instead of user stories written in Jira, AI systems will synthesize customer feedback and generate priorities automatically.
The Agile Manifesto itself may need revision. When “individuals and interactions” are replaced with “individuals and intelligent agents,” and “working software” is no longer hand-written but machine-generated, the meaning of agility changes. It becomes less about collaboration and more about adaptive intelligence.
The Broader Implications of an AI-Driven Workforce
Redefining Organizational Value
As AI enables individuals to do the work of many, organizations must reevaluate what constitutes value. In the industrial era, value came from scale, factories, labor, and capital assets. In the information era, it came from data, software, and networks. In the AI era, value is increasingly tied to leverage—how much a single person, or a small group, can produce using intelligent tools.
This means the core question for companies changes from “How many employees do we need?” to “How much leverage can we give each contributor?” The most valuable organizations will not be the largest—they will be the most intelligently designed. Firms that empower high-performing individuals with seamless AI integration will vastly outperform those that rely on traditional staffing models.
We will also see a rise in “hyper-lean” companies—startups or micro-enterprises with minimal headcount but immense output. These companies may focus on narrow niches, iterate faster than traditional competitors, and generate significant revenue with minimal overhead. This challenges everything from how venture capital assesses scalability to how regulators track employment trends.
The Erosion of the Traditional Career Path
The idea of a “career ladder” is deeply ingrained in professional culture. People progress from junior to senior roles, accumulate experience, manage others, and eventually move into leadership. But AI disrupts this progression.
When AI tools can handle intermediate tasks traditionally learned on the job, there’s less opportunity to grow into roles the way people used to. For example:
- Junior copywriters are replaced by AI-generated content tools.
- Mid-level designers are bypassed by AI-generated UX mockups.
- Associate analysts are rendered redundant by automated dashboards and forecasting models.
This hollowing out of the “middle” makes it harder for entry-level professionals to gain the experience needed to move up. As a result, the gap between AI-augmented experts and entry-level workers could widen. It’s not a ladder anymore—it’s a cliff.
Educational institutions, employers, and policymakers will need to address this. Apprenticeship models may re-emerge. AI-guided training programs may simulate real-world experience. Lifelong learning will no longer be optional—it will be the only path to relevance.
Global Labor Redistribution and Access
One potential upside to AI displacing traditional teams is the democratization of opportunity. A solo founder in Nairobi, a student in Bogotá, or a designer in Dhaka can now launch global products and services with the same tools available to their peers in San Francisco or London.
AI doesn’t care about zip codes, accents, or pedigree. It levels the playing field—at least in theory. Those who can access and use AI effectively are no longer held back by their inability to form or fund large teams. This has the potential to trigger a massive redistribution of innovation and entrepreneurship across geographies and demographics.
However, this assumes equitable access to AI infrastructure, education, and connectivity—an assumption that doesn’t hold everywhere. The AI boom could exacerbate digital inequality just as easily as it could solve it. Those who control the platforms and models may end up controlling who gets to play the new game at all.
Governments and NGOs must work to close this divide, or risk creating a world where AI becomes a multiplier for existing inequalities rather than a bridge across them.
Ethics and Responsibility in a Solo + AI World
Decision-Making Without Accountability
When individuals wield AI tools to make consequential decisions without team oversight, a dangerous asymmetry can arise: accelerated impact without distributed accountability.
In traditional teams, decision-making benefits from group scrutiny. Bad ideas are debated. Risk is distributed. Mistakes are caught. When one person makes all the decisions—albeit with AI support—those safeguards may vanish.
An AI-augmented solo founder could launch a product that unintentionally manipulates users, violates privacy laws, or causes social harm. Not out of malice, but due to a lack of diverse input or ethical reflection.
As AI takes over more decision-heavy tasks—such as who gets a loan, what news people see, or how users behave on a platform—we need clear frameworks for ethical AI use at the individual level. Relying on large teams or legal departments to handle these issues won’t suffice in a solo-led future.
We may need:
- AI ethics licenses for individuals using AI at scale
- Auditable AI workflows to ensure transparency in solo-led decisions
- Responsibility-sharing models where platforms, not just users, are liable for harm
The Emotional Isolation of Solo AI Work
There’s another, more human concern: loneliness.
Working with AI can be exhilarating—fast, frictionless, and powerful. But it can also be isolating. There’s no spontaneous creativity from hallway conversations. No serendipitous insights from brainstorming sessions. No emotional support during tough moments. The solo founder who works with AI may be more productive, but also more isolated.
Mental health challenges in remote work are already documented. The AI-augmented future may amplify them if we’re not intentional about designing for human connectio, —even in solo-dominant environments.
Hybrid work models, online communities, and co-working hubs may become more importa, t—not to collaborate on tasks, but to share experiences, failures, and support. In the absence of teams, the community becomes the safety net.
Education, Skills, and the Rise of the AI-Native Worker
Teaching the New Literacy: AI Fluency
Just as previous generations had to become computer literate, the current and future workforce must become AI-fluent. This doesn’t mean everyone must learn to build models, but everyone must learn how to work with them.
AI fluency includes:
- Understanding how generative models work
- Knowing how to prompt effectively and critically
- Recognizing AI limitations and biases
- Designing workflows that combine human creativity and machine speed
- Interpreting AI output with skepticism and context
This skill set is different from traditional digital literacy. It’s not about operating software—it’s about orchestrating intelligence. The person who can manage multiple AI agents like a conductor—assigning tasks, integrating results, making judgment calls—will be more valuable than the person who just “knows how to use the tools.”
Curricular Overhaul in Schools and Universities
Most educational institutions are still preparing students for team-based, domain-specific careers. They train marketers, engineers, analysts, designers—as if those functions will remain distinct. But if the real job is “solo AI orchestrator,” then the curriculum must change.
Future-ready education may prioritize:
- Systems thinking and interdisciplinary learning
- Creative problem-solving with automation
- Real-time collaboration with AI partners
- Entrepreneurship and project-based assessments
- Ethics of autonomous decision-making
Instead of group projects simulating teamwork, students may build solo projects powered by AI. They will be judged not by how well they “played their role,” but by how well they integrated intelligence to produce outcomes.
Universities that adapt will thrive. Those that don’t may find themselves irrelevant, much like the organizations they once fed.
Strategic Recommendations: Adapting to the Post-Teamwork Economy
For Individuals
- Learn prompt engineering
This is the new interface between human intent and machine execution. Mastering it will multiply your capabilities. - Build a personal AI stack.k
Identify the AI tools that best support your work, and integrate them into a consistent, repeatable workflow. - Think in systems, not tasks.
Don’t just complete tasks—design systems where AI handles recurring processes while you oversee strategy and judgment. - Invest in your narrative.
In a world where many people can do the same things with AI, how and why you do them becomes your brand. - Stay human
Creativity, ethics, empathy, humor—these are your enduring advantages. Use AI to amplify them, not replace them.
For Organizations
- Restructure for AI leverage, not headcount.t
Evaluate where small teams or individuals can outperform traditional departments using AI. Flatten hierarchies accordingly. - Audit your workflows
Identify high-friction areas in your current team structures. Could AI reduce the coordination tax? - Reskill, don’t replace (yet)
Give your people access to AI tools and training. Some will evolve into solo performers; others into system managers. - Focus on output, not hours.
Time spent is no longer the best measure of value. Shift performance reviews to center around innovation, iteration, and impact. - Embed ethics and safety into every AI initiative.
Don’t assume good intentions are enough. Implement checks, balances, and review processes—especially when reducing team oversight.
Beyond the Team — Designing the Future of Work in the Age of Intelligent Autonomy
In traditional organizations, teams were more than just working groups—they were social environments. People found identity, mentorship, purpose, and community within them. Being part of a team meant belonging to something larger than oneself. But in a world where AI allows a single person to do the work of many, this model begins to unravel. The new professional identity isn’t defined by team roles, but by systems, outputs, and personal leverage. The individual becomes a node of power, not a cog in a process. This cultural shift—from belonging to becoming—requires new support systems: virtual guilds, flexible coalitions, and values-based networks. Without them, we risk losing the social fabric that teams once provided.
The Office Is No Longer a Collaboration Engine
The 20th-century office was built around group productivity. From conference rooms to open-floor plans, every architectural decision aimed to make teamwork easier. But in the age of AI, productivity no longer depends on proximity or meetings. The office becomes less of a factory and more of a retreat—a place to reset relationships, realign missions, and rebuild trust. Companies that recognize this will repurpose offices into spaces for deep human connection. Those who continue to treat the office as a productivity tool will find it increasingly obsolete.
Leadership Without Teams
In the past, leadership was defined by the size of your team. More reports meant more influence. But when AI lets individuals operate independently at scale, traditional management structures erode. Future leaders won’t manage headcount—they’ll manage intelligence systems, align ethical boundaries, and tune strategy across dynamic, decentralized contributors. The great leaders of tomorrow will resemble conductors, orchestrating not people, but platforms, tools, and outcomes. Their power will come from amplification, not control.
Economic Implications of AI-Powered Solopreneurs
A workforce of AI-augmented individuals will impact economies worldwide. First, productivity will surge, especially in digital industries, leading to falling costs and deflation in some sectors. Second, we’ll see a widening gap between GDP growth and employment—a decoupling that will strain outdated job-based policies. Third, entrepreneurship will explode. Millions of AI-powered micro-operators will build solo ventures with global reach. Governments will have to rethink tax, labor law, and safety nets for a world where traditional jobs are no longer the norm.
The Danger of Centralized AI Infrastructure
While AI empowers individuals, the infrastructure is becoming dangerously centralized. A few companies control access to foundational models and APIs, giving them disproportionate power. If these platforms begin to throttle access, manipulate outputs, or enforce ideological boundaries, the future of work could be bottlenecked at the source. A healthy AI economy will require open access, model diversity, and global regulation to prevent monopolistic gatekeeping.
The Rise of the Modern Artisan
Ironically, as AI handles more tasks, human creativity becomes more valuable. The future worker won’t just produce—they’ll curate, craft, and differentiate. Uniqueness becomes the premium product. AI frees people from repetition, allowing them to focus on resonance, taste, and storytelling. The new creator is not a laborer, but an artisan, scaling their work through machines while preserving the human spark.
What Organizations Must Do to Stay Relevant
Companies clinging to rigid hierarchies, outdated processes, or bloated teams will falter. To stay competitive, organizations must shift from labor-driven models to leverage-driven ones. This means trusting individuals, reducing meetings, flattening org charts, and enabling autonomy through AI tooling. The new focus should be on outcomes, not attendance; on trust, not control. Those who embrace this will see sharper execution, higher talent retention, and exponential output.
Redefining Teams in the Age of Intelligent Autonomy
So what replaces the traditional team? Not chaos or isolation, but intentional, fluid collaboration. Teams will form temporarily around missions, then dissolve. Leadership will emerge from context, not title. Coordination will be asynchronous and tech-driven. The “team” as a permanent structure is fading, but purposeful, AI-augmented collaboration is just beginning. The team is not dead; it’s evolving into something more adaptive, dynamic, and distributed.
Final Thoughts
The future isn’t about replacing humans—it’s about reinventing how we work. AI allows us to decouple effort from scale. But the soul of work—meaning, mission, creation—remains deeply human. In this new world, those who pair intelligence with intention, and autonomy with ethics, will thrive. The death of the Agile team is not an ending—it’s the beginning of a new chapter in human productivity.