AI research tools are still too eager to turn public signals into certainty

One thing I keep noticing with AI research tools: they’re very good at finding something interesting, but not always good at admitting when that “signal” is weak. I’ve been using Komo AI for company research, and the part I genuinely like is how quickly it can move from a company name to a compact packet of recent signals with the underlying sources kept close to the summary. For scanning accounts or getting oriented before a deeper research pass, that is much nicer than juggling a pile of tabs. The useful features for me are: - company-level research instead of a generic web answer - recent events and signals grouped in one place - source pages attached to the claims - faster prioritization when several companies need to be reviewed But there are still pain points that apply to Komo and most tools in this category. A public event is not the same thing as intent. A hiring page can be stale. A funding announcement may have nothing to do with the problem you care about. A technology mention can describe a partner or an old stack rather than current usage. And a clean summary can make a shaky inference feel more certain than it really is. My workaround is to split the job across tools: Komo handles discovery and builds the source packet. Claude or ChatGPT argues against the initial interpretation. Codex checks required fields, dates, and structured outputs when I need the process to repeat reliably. I make the final call after opening the strongest source myself. The audit prompt is intentionally simple: "Separate what the source directly says from what you inferred. Show the strongest evidence against the conclusion. If a claim depends on missing or stale information, mark it unresolved." What I’d like research products to improve next is contradiction handling. Don’t just show the newest supporting signal—surface evidence that weakens it, show when sources disagree, and make “not enough evidence” a first-class result. Komo saves me time at the discovery stage, but I would not treat it—or any research AI—as a source of truth by itself. The feature I value most is not the summary. It is being able to get back to the evidence quickly. For people using AI research tools: which matters more in practice, better discovery or better uncertainty/contradiction handling?