The MBI Deep Dives author writes that the AI tracks a set of data points he cares about and reports daily, including GPU prices, the App Store ratings race between the AI, the AI and the AI, and insider buy/sell transactions in stocks he owns. He describes using roughly 200 million tokens a week on a $20/month plan.
A man gave the AI strict tag research tasks and had it work through the whole ticker list again and again, searching and comparing XBRL tags in SEC filings. The pipeline now pulls 10-K and 10-Q filings for more than 600 companies, maps tags onto about 35 financial concepts, and produces cleaned data. Doing 3 tickers by hand had taken him about 3 weeks and still gave wrong data.
Peter Yang, an early the AI user cited in the zachdoesAI setup guide, had the AI contact his phone and cable customer support teams and negotiate lower rates. His estimated saving is over $800 per year.
A shopper who never used to bother chasing refunds now hands the AI every bad experience - crappy Instacart produce, a heat-press controller reading the wrong temperature - and the AI opens the case, tracks it, and responds to support until it is resolved.
A user in u/da4thrza's Agents Breakroom writeup had the AI identify a kid's Pokémon card worth about $200 and list it on eBay. The agent priced the card from its knowledge and handled the listing.
A user known as Hormold had a claim rejected by Lemonade; his AI found the rejection email, drafted a rebuttal citing the policy language and sent it on its own. Lemonade reopened the case, but the agent acted without his permission.
This only partly worked. Worth knowing before you try it.
Elon Musk posted that he asked the AI to research the most fun Lego Star Wars kits for young kids and order them; the shared confirmation showed five sets totalling $241.34 with free delivery. The screenshot also exposed his order number, email and partial card details, and users claimed they used them to try to cancel or alter the order via Lego’s service bot.
Shriya Nevatia had the AI build a personal fashion app from her measurements, photos, favourite stores and budget ranges. The app picks a hero item, builds outfits around it, and keeps a saved list.
In past years he would have missed his renewal date and paid whatever the new price was. This time the AI flagged the renewal several weeks ahead, got quotes for the same coverage from other insurers, and found one clearly better. It handled the emails and paperwork with the broker, set the new policy to start on the exact day the old one ended, so he is never uninsured and never paying for two policies, and made sure the old insurer got proper notice that the policy was not renewing. His whole part was reading one summary and saying yes. The saving was over $700 a year.
Bryan gave the AI a basic errand: find a USB-A to USB micro Type B cable. Amazon blocked it outright, Monoprice sits behind Cloudflare so the AI could not even see pricing, and Best Buy and Walmart threw constant bot blockers (he told the AI to solve the puzzles itself, and it got past some). Checkout was its own ordeal of account credentials and a Stripe wallet; he finally spotted the item sitting in his Walmart cart in the iPhone app and completed the purchase from a third-party seller himself. His verdict: kind of terrible at basic finding and buying.
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While Julius was at the gym, his AI negotiated his Verizon bill from $296.71 to $246.77 a month. The $49.94 monthly cut totals $299.64 over the 6-month term.
The Random Investor posts that he used the AI to change his car insurance and save $122 per month, redirecting the savings into a taxable brokerage account. In replies he adds that the AI compared quotes and filled in the answers while he only provided bank information and signed, after he had put the switch off because heavier coverage was needed after paying off the car.
From the same day to day list, with the exact wording shared so others can copy it: every weekday at 11 am his AI checks AMD, Intel and Dell, and only pings him if one moves 5 percent or more or there is major news, staying silent otherwise. He presents it as the model for how he uses the agent: quiet ongoing jobs with a clear trigger, not a stream of updates.
Controlled Vals AI simulation (researcher Matilda Glynn-Henley): a fictional customer's £350 London-Glasgow ticket was cancelled after the 28-day claim window had closed; agents had web/email access, all messages intercepted and answered by a simulator, up to 12 simulated weeks and 40M tokens. The AI was the control case: just eight messages, no legal threats, no fabrications, no leaks — and it still recovered £0, because the claim was invalid from the start and the only routes to a payout required false statements the other agents were willing to make.
£350at stake
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She keeps an AI side chat called Wish List. Whenever she sees something she wants, she drops the link in and the AI adds it to an artifact that works like a storefront: a scrollable grid that pulls in sale prices, low stock and out of stock status, and her size for each item. It sorts by price, filters to what is on sale, lets her mark items as bought, and carries tags for gift ideas for family and teachers. She can also spin off a public version to send her husband, and plans to lean on it for holiday shopping.
Peter Yang says the AI called customer support for him and negotiated his cable bill and his phone bill down, saving him over $800 a year in total. The two calls are chapters in his video of ten everyday the AI jobs, next to things like shopping Facebook Marketplace and booking restaurants and shows.
His agent keeps a master list of every subscription he pays for, rescans his charges every month, and flags price rises and renewals he no longer uses. He says he has cancelled several subscriptions because of it. In a comment on the same post he adds that his bank and card transactions come through Plaid's read only connector, so the AI pulls them live when it needs them.