Brian Richards imagines a 2026 to 2036 decade shaped by AI-driven productivity, round-the-clock stock trading and automated financial decisions. His 10% annual market-return assumption is a scenario, not a promise.
In Brian Richards' imagined 2036, one company has fewer than 5,000 employees and 40 million agent direct reports. The person in charge of those agents could earn more than the CFO. It's a deliberately startling picture of how artificial intelligence might change the economics of running a business.
Richards, of Team Rule Breakers, frames ten predictions for 2026 to 2036 as a portfolio thought experiment. They are not established outcomes. His central bet is that AI could lift productivity and company earnings before official government statistics fully register the change. At Meta Connect 2026, Meta presented Muse as a central part of its AI strategy. The company plans for the assistant to work across phones, computers and glasses, as CNN's Connect coverage reported. That possibility is worth considering. It is not a dependable market forecast.
Meta said it expects to offer more than 100 styles of AI glasses by the end of 2026 and plans to bring Muse to wearable devices in the coming months.
AI reshapes market power
Richards' numbers are ambitious. He expects the stock market to return about 10% a year over the decade, roughly its long-run average. He also imagines Meta's market value topping $10 trillion, with Apple, Amazon, Alphabet, Meta and Anthropic as the world's five largest companies. These are predictions, not present-day rankings or promised returns.
His Meta forecast rests on a specific business model. Every image on social platforms becomes shoppable. AI agents anticipate what users want, and Meta earns a cut from idea through purchase. That would take the company beyond advertising and into product discovery and transactions.
Some steps in that direction have already been taken. On September 29, Meta expanded Muse for small businesses with integrations for Shopify, QuickBooks, Stripe and Canva. The move is part of a strategy to diversify revenue beyond advertising, according to Reuters' business report. The prediction still depends on consumer adoption and Meta's ability to capture value across that chain. These developments do not establish that either will happen.
Muse launched on September 8, 2026. Its basic version is free, while plans for heavier use cost $20 and $100 per month; Meta also expects to earn a small fee on transactions the agent completes for users.
Richards also imagines 19 exchange-traded funds marketed with some version of the word "mat," including one with 10-times leverage. The detail is comic. The risk is not: a leveraged fund magnifies exposure and can make losses more severe. A crowded label does not make a strategy sound. Valuation readings can flag risk without telling investors when to exit, a distinction explored in this market-risk analysis.
Automation moves inside companies
In Richards' corporate future, "Director, Agent Workforce" becomes a standard job title. Some people in the role earn more than the CFO. At one large S&P 500 company, fewer than 5,000 human employees work alongside 40 million agents overseen by that director. Richards imagines the company's all-hands meeting fitting in a hotel ballroom.
That is the tension. Could companies grow their output through software agents without growing their human workforces at the same rate?
The forecast also brings AI risk into household finances. Richards imagines the first AI-caused personal bankruptcy making national news. A lawsuit follows and settles for an undisclosed sum. He predicts that "agent discretion limits" then become a standard field in brokerage onboarding. This is a hypothetical chain, not a report of an actual bankruptcy or legal settlement. It points to a practical concern for investors: software can lose money when it makes financial decisions. Users may need clear limits on what an agent can do.
His prediction about AI-written websites offers a different signal. Richards expects more than 80% of web pages to be authored by AI. He imagines human writers adding intentional typos to prove their work is genuine. If that scenario comes to pass, polished prose will offer readers less reason to assume a human wrote it. The prediction gives no method for verifying content. Source checks and corroboration would matter more than style alone.
Markets and access change
Richards expects U.S. stocks to trade 24 hours a day, 365 days a year. In his forecast, the final closing bell rings in 2031 and is auctioned to Ken Griffin for display in his seventh home. He also imagines a sports-outcome ETF growing larger than the biggest actively managed value fund, despite the SEC's displeasure. The fund's manager sponsors College Gameday, and the public accepts the arrangement. Both scenes are forecasts about market structure and investor appetite, not accounts of current policy or fund assets.
Continuous trading could make markets easier to access across time zones. It would also end the familiar daily pause between sessions. A fund tied to sports outcomes raises a different issue: investors would be buying exposure to event results, not a conventional portfolio of operating businesses. In Richards' scenario, sponsorship and cultural familiarity could make the product seem normal even when regulators object.
His final prediction reaches beyond investing. Passwords disappear after quantum computing and Q-Day change how people access accounts and information. Future generations, he suggests, will need passwords explained alongside landlines and the blue links people clicked when they "Googled" something. The forecast points to a possible break in how authentication works. It does not say what would replace passwords or when that change would arrive.
The value of Richards' collection is not the precision of any one prediction. It puts assumptions about productivity, market access and financial delegation to the test. A 10% annual return is a scenario, not a planning guarantee. The imagined winners also depend on business models and technologies that may develop differently.
Investors can use the exercise to examine how much their portfolios rely on a narrow group of AI beneficiaries. They should keep diversification and downside risk in view. Take the mechanisms seriously. Don't mistake a vivid 2036 for a map of the next ten years.
