Can Magnus Resch’s A.I. App Crack the Art Market’s Transparency Problem?

July 14, 2026 / Sophie Clearwater /

Magnus Resch. Courtesy Magnus

The art industry’s lack of transparency, particularly around pricing and final transactions, has been cited by many as a key factor preventing the market from expanding its collector base and reaching the new buyers it needs to sustain accelerated global growth. Several apps and startups have tried to address the issue, but none has succeeded in democratizing a sector that has become a very visible lifestyle phenomenon while still running on a level of secrecy and exclusivity.

Art market economist and entrepreneur Magnus Resch built an entire career around the art market’s blind spots, writing extensively about the systems that determine artistic success, gallery sustainability and collector behavior. His latest venture is the update and re-release of an app called Magnus, which bills itself as the “Shazam for art.” Originally launched in 2013 as a regularly updated guide to art world happenings, the app went offline during COVID when museums and galleries shut their doors. Resch recently introduced a new A.I.-powered version, currently available in beta, that builds on the original premise with image recognition and a large art-price database. Take a photo of an artwork and you’ll receive, within seconds, information about the artist, title and—most importantly—price.

“The art market doesn’t have an attention problem. It has a conversion problem,” Resch told Observer. “People visit galleries and art fairs, but many don’t buy because they don’t feel informed enough. We answer three simple questions: What’s the price? Is it fair? And is it a good investment?” He sees a younger generation interested in art but unsure of how and when to buy, not only because prices are opaque and transaction costs are high, but also because the buying process can feel intimidating and exclusive.

The research leading to the redevelopment of his eponymous app confirmed how fragmented information in the industry is but also shined a light on how value is created. There was, he said, a lot of boots-on-the-ground gruntwork involved. For more than 15 years, he and his team visited galleries around the world to manually collect prices. “That’s data that is not accessible anywhere else. We combined those with more than 50 years of auction results,” he added, asserting that the app offers users access to “the world’s most comprehensive art price database,” with millions of gallery prices paired with secondary market sales data.

“We cleaned [the data], connected duplicates and built a unique identifier for every artwork,” Resch explained. On most databases, if a work sells twice, you see two entries; Magnus shows a complete history of that artwork, including where it has been exhibited, when it appeared at auction, sale prices and whether it was previously offered by a gallery. Context, in this sense, is what sets Magnus apart from other apps and data aggregators. “It depends on context: who represents the artist, where the work has been exhibited, which museum owns it, how comparable works have performed and how momentum develops over time.”

As to why bring Magnus back now, recent advances in A.I. have dramatically improved both the speed and accuracy of image matching. ID’ing works of art is relatively straightforward when someone is looking at a famous painting or sculpture, but it is much more difficult in the art market, where works may be newly made, unique, under-documented or visually similar to other works by the same artist. Artists also influence one another and may have similar visual languages, Resch noted. “Training a model on a thousand Basquiat works doesn’t mean it can recognize a thousand and one. It also has to distinguish it from thousands of visually similar works by other artists.”

Another challenge was how to track identity, as there is no universal identifier for artworks comparable to an ISBN for books. “We had to build that ourselves,” Resch said, but once he and his team had a database of millions of uniquely identified artworks, Magnus’s matching accuracy improved dramatically. “Google Image Search, ChatGPT or Claude can often identify the artist’s name, but they never give you the price. We do.”

Discussions around A.I.’s impact on the art world usually focus on image generation and questions of authorship, but Resch sees artificial intelligence as the key to achieving market transparency. He’s adamant that it’s a business system, not a creator. “As we showed in our Science study, an artist’s success depends less on the artwork itself than on reputation, visibility and professional networks. A.I. may generate beautiful images, but it cannot replace the human relationships that create cultural and economic value.”

Except, maybe, the relationship between collector and advisor. “Magnus learns what users like and recommends artworks based on their taste, budget and location,” Resch continued. (If someone consistently scans affordable abstract paintings with a lot of green, the app can recommend similar green works at nearby galleries or anywhere else in the world.) “No human advisor can continuously monitor the entire art market. A.I. can. That is a much more practical use of A.I. It gives people access to information and discovery tools that were previously available only to insiders.”

Ultimately, what the A.I.-powered version of Magnus offers is information; what people do with that information is up to them. More informed buyers, Resch acknowledged, can be more skeptical ones. “I think that’s healthy. Transparency doesn’t weaken the art market. It expands it,” he said. “More people will buy art if they understand what they’re looking at and feel they are participating in an open rather than a closed system. The art market doesn’t need more visitors. It needs more buyers. Transparency is how we get there.”