How AI is Challenging Gatekeepers and Identifying Trends

Arts entrepreneurship is changing fast as art tech platforms challenge the old gatekeepers of galleries and auction houses. On the Arts Entrepreneurship Podcast, we talk with Penelope Sonder, COO of NALA, an online art marketplace built as a “networked arts learning algorithm” that matches artists with collectors and interior designers. The core promise is simple and provocative: help people discover art by taste, then let them buy directly from the artist. For working artists, that means more visibility and a clearer path to revenue. For first-time buyers, it means less intimidation and more confidence when exploring contemporary art online.

NALA’s product design borrows from familiar consumer behavior: discovery through swiping, similar to a dating app, where buyers react to what they genuinely like. That seemingly playful interface points to a serious problem in the art market: most people do not know the “right” keywords, movements, or gallery names to search. Penelope emphasizes that recommendation should work even when someone is not well versed in art history or collecting. Instead of pushing status symbols, the platform aims to reduce friction between artists and buyers, and to rebuild a sense of conversation around art that feels personal rather than prescribed.

A major talking point is NALA’s artist-first business model. Penelope argues that if an artwork sells for $5,000, the artist should receive $5,000, which flips the traditional gallery commission structure. NALA supports operations through subscriptions, with options for artists and a professional subscription dashboard for interior designers. The tradeoff is not hidden fees, but participation: artists need to keep profiles updated, document and photograph work well, and tell their story so the platform can present them effectively. It is not passive income; it is a tool within a broader studio practice and creative business strategy.

The episode also dives into how NALA’s AI differs from common keyword-driven discovery. Each uploaded artwork is analyzed with a mix of proprietary data and art world expertise, aiming to capture stylistic and art historical signals alongside practical factors like medium and price. Penelope notes that the recommendations frequently contradict her own intuition, which she sees as proof that taste is diverse and that a single curator’s viewpoint should not decide what succeeds. That humility matters in arts entrepreneurship: better systems do not replace judgment with automation, they widen access so more artists can find the right audience.

Finally, we explore what trend data could mean when it comes from broad human behavior rather than a small circle of decision-makers. NALA sees patterns that may not yet show up in the market, but Penelope frames the goal as educational partnerships with museums and universities, not simply monetizing demographics. On marketing, the team focuses heavily on digital storytelling on TikTok and Instagram, interviewing artists and nurturing discussion rather than declaring what is “hot.” Even in physical settings like Context Miami, NALA’s approach breaks conventional curation rules to show how an algorithmic feed can become a group show that sparks new ways of connecting buyers, artists, and the art market. ​