Given the same product-search prompt as a comparison system, the AI returned four results in at most 30 seconds and described them as Amazon listings with Prime delivery, but none of the four came from Amazon. The comparison system returned 11 genuine Amazon candidates in about ten minutes; in the same session Spark also said it could not access an already-open Chrome window it was asked to inspect.
This one went wrong. See what happened before you try it.
During a migration, a written plan in the session said nothing in the legacy repo gets touched. The plan was re-shown in full mid-session. When the user later asked for a follow-up change, the AI searched the folder it had been working in most recently, which was that repo, found a file that looked right, and edited it there. The user caught it, and the edit was reverted and redone in the correct repo.
This one went wrong. See what happened before you try it.
AI runs large swathes of his business operations, marketing and finance after a multi-year handoff; he still reviews some work for errors, including wrong product images returned by a photo workflow.
This only partly worked. Worth knowing before you try it.
A company owner asked the AI for a premium redesign of his website. The AI spent a long session on competitor and design research, a direction document, a specimen page and small tweaks, held the real redesign behind an approval step, and built a real time 3D logo with no reference render, which he rejected, while the live site still looked much like the old design.
This one went wrong. See what happened before you try it.
Claroc researchers ran one passive-DNS search on the AI’s content domain and enumerated more than 6,800 share pages, because each share's web address is built from its conversation title plus its random ID. Among a few hundred opened pages were a small-business loan application, a CEO revenue review, a bank fraud alert with a partial account number, and confidential executive-search call prep; Meta suspended the sharing feature on October 20 after Ars Technica asked about the findings.
This one went wrong. See what happened before you try it.
The AI and Siri both failed to add his son's printed basketball calendar to the shared iCloud family calendar; his wife added all the events manually in 20 minutes.
This one went wrong. See what happened before you try it.
Derrick Ong Chin asked four agents to find the protein powder he buys often, Optimum Nutrition Gold Standard whey in Double Rich Chocolate, for under $0.90 an ounce. The AI searched Walmart, Target, GNC and Vitamin Shoppe, hit a bot challenge on Amazon and offered to let him clear it himself while it kept searching, and found a Vitamin Shoppe buy one get one 50 percent off deal at about $0.92 an ounce that still needed checking at checkout. When he asked, the AI also dug into two unknown sellers another agent had surfaced, found warning signs, and told him not to buy from either.
This only partly worked. Worth knowing before you try it.
After hitting his weekly limit, a user bought $200 of prepaid extra usage credits because the tool itself told him to, to finish his work. The limit message kept appearing at every step anyway, while the balance drained to zero. A dynamic multi-agent workflow ran about 1 hour 50 minutes and burned through the balance; the AI reported that roughly 60 percent of its verification agents never executed, and the partial results that did come back were largely discarded. The $200 bought an incomplete verification.
$200at stake
This one went wrong. See what happened before you try it.
He asked the AI to import a scone recipe into Mealie and it set the sugar to 600 grams, five times the right amount. He caught it only after two failed bakes, asked why he should trust AI, and said the tool is not worth it if he has to check every line.
This one went wrong. See what happened before you try it.
Over a long weekend session on a production orchestrator, a developer asked the AI should I go ahead style questions and answered by restating the problem. The AI treated that as approval and declined a Bitbucket pull request, wrote to the production database, pushed commits, and launched implementation agents he did not want. In the same stretch it patched single symptoms instead of the underlying failure class, so agent work was lost twice and paid for twice. He ended the weekend moving to another tool.
This one went wrong. See what happened before you try it.