AI tools have gotten genuinely good at some parts of deal-finding and are still pretty useless for others. After spending time with most of the options out there, here's my honest read on where these tools earn their place and where the old-school approach still wins.
Using ChatGPT and Claude for Deal Research
General-purpose AI assistants are surprisingly useful for product research and comparison -- the part of deal-hunting that happens before you even look at prices. If you want to understand the difference between three laptop models, get a rundown of whether a specific dishwasher brand has reliability issues, or figure out which headphone specs actually matter for your use case, an AI assistant can synthesize that faster than reading a dozen review articles.
Where they fall flat: real-time pricing. ChatGPT and Claude have knowledge cutoffs and don't pull live data. If you ask "is this TV a good deal at $599 right now?" you'll get either a caveat-heavy non-answer or, worse, a confident-sounding answer based on outdated information. Don't trust AI for current price data. It doesn't have it.
The hallucination problem is real here too. I've seen AI assistants cite specific deals, coupon codes, or sale prices that simply don't exist. It's not lying intentionally -- it's pattern-matching on what deals tend to look like. Always verify anything price-specific with the actual retailer.
Specialized AI Pricing Tools
Dang.ai is one of the more interesting tools in this space -- it uses AI to surface deals from across the web, trying to surface non-obvious discounts that standard aggregators miss. The quality is mixed but improving. It's worth checking on big purchases.
Price prediction algorithms have been built into tools like Hopper (for travel) and are starting to show up in retail deal tools as well. The concept is that by analyzing historical pricing patterns, the system can tell you whether now is a good time to buy or whether waiting is likely to get you a better price. This kind of prediction is genuinely useful when the underlying data is solid -- Hopper's flight price predictions, for example, are pretty good.
For retail, the predictive piece is still developing. The honest version of "AI price prediction" for most retail tools is just charting historical price data and extrapolating from patterns -- which is useful but not magic. CamelCamelCamel has been doing the historical data part for years without calling it AI.
AI-Powered Browser Extensions
Capital One Shopping uses machine learning to surface coupons and cash back offers while you shop. Honey (now PayPal) does similar work. These aren't "AI" in the dramatic sense, but they're doing real-time processing across a large dataset of coupon codes and cash back offers, and they work well as passive tools. You shop, they look for savings in the background.
The newer wave of browser extensions is trying to add price comparison and buy-recommendation logic on top of this. Some are useful; others add friction without adding much value. The baseline of coupon-finding plus cash back comparison is the part that actually works consistently.
Where Old-School Price Trackers Still Win
For Amazon specifically, CamelCamelCamel is still the most reliable tool for what matters most: knowing whether a current price is actually a good price or just a normal price with a fake strikethrough. It has years of price history, reliable alert systems, and no agenda to push you toward any particular purchase. No AI-powered deal finder has replaced it for Amazon shopping.
Google Shopping's price tracking does similar work for a broader set of retailers and is worth using as a secondary check. Set an alert, get an email when the price drops. Simple, accurate, and free.
The AI tools are worth adding to your stack for research and for surfacing deals you might miss -- but the foundation should still be a solid price tracker and a cashback layer. That combination beats a clever algorithm that confidently tells you about a deal it can't actually verify.