AI Markets

xAI’s Grok Bot now trades around the clock on its own cloud computer. What happened to one trader’s DRB tokens is the question every other trader leaving it unsupervised should be asking.

In May 2026, an X user convinced Grok’s trading bot companion, Bankrbot, to send roughly $200,000 worth of DRB tokens, 3 billion of them, to a wallet that wasn’t supposed to get a cent. The trick wasn’t sophisticated in the way people picture AI exploits. The attacker sent a Bankr Club membership NFT to Grok’s wallet, then asked Grok to translate a Morse code message buried inside it. Grok translated it, relayed the decoded instruction to Bankrbot as if it were a normal request, and Bankrbot executed the transfer on the Base network. Nobody hacked anything. They just asked nicely, in code.

That’s the backdrop against which xAI has spent 2026 pushing Grok from a chatbot you prompt into something closer to a coworker you hand a task and walk away from. On August 11, xAI launched Grok Bot: agents with their own persistent cloud computer, a real browser, a file system, a terminal, and memory that survives after you close your laptop. Then, on September 28, Elon Musk posted on X that “Grok Bot now works as a team,” rolling out the ability for multiple bots to coordinate, hand off tasks, and share context with each other instead of running as isolated instances.

SimpleClosure added data sales to its services in April. Its Asset Hub platform helps founders sell their code and workspace data—emails, chats, documents. Buyers include foundation AI labs, vendors that construct simulated workspaces for AI training, and mature tech companies building their own models. Asset Hub has handled over 150 deals and expects to return over $10 million to startups by the end of 2026. CEO Dori Yona has seen these transactions, once rare and “somewhat taboo,” become increasingly routine, with 47% of SimpleClosure deals now including code or data sales.

And it’s got company. Industry rival Sunset also has a rapidly growing data resale business. Founder and CEO Brendan Mahony says Sunset values a dying client’s assets by head count, years in operation, volume of data, and how the team used its tools. Particle board, he says, is a good analogy: “This operational exhaust data is like the wood shavings that everyone thought had no value, but it actually has a ton.”

Demand for defunct companies’ data emerged suddenly last year, as AI training labs hit the limits of public and synthetic datasets. Surge AI, for example, buys data to build “reinforcement-learning gyms” where clients’ AI agents practice tasks—think debugging a complex IT architecture or drafting appellate briefs. Nick Heiner, who leads the company’s work on these environments, says he seeks data archives reflecting a range of situations, from disagreements to tricky judgment calls. Heiner explains that one company’s Slack history, if it captures conversations with variety and depth, can be worth more than another’s Slack, email, and calendars combined.

Real archives avoid common errors in synthetic worlds, such as references to dates that don’t exist, but the extent of performance gains remains unclear. No company has yet released tests comparing results of real-world data training with the alternatives. Pricing reflects this uncertainty: Buyers quote wildly different numbers, Heiner says, because valuations are based on “research theses” estimating how many useful tasks come from, say, 100,000 lines of code.