Last year, I participated in a discussion at Christie's on what is originality in the age of AI. To my left sat Alexander Reben, OpenAI's first artist-in-residence. To my right sat Nicole Sales Giles, former Director of Digital Art at Christie's. Artists described how their creative processes are changing as AI makes once virtuosic media effortless to generate. Meanwhile, the curators argued that we lack meaningful archives in the AI space, which is what leads to understanding of originality and value in the same way that it did in mediums before AI.
Of course, there was no consensus, but the discussion revealed a deeper problem. While AI enables individualistic expression, its creation lives in an algorithmically ephemeral, context-stripped, memoryless state. Analog creation left behind archives. Sketches, drafts, and material experiments. These artifacts preserved process, making technique available for reuse. Creation with AI leaves behind almost nothing. Without this substrate, proof-of-work for distinguishing genuine experimentation from what we call 'slop' (Merriam-Webster's 2024 word of the year) doesn't exist.
proof of work pic.twitter.com/1p74gNaf4S
— corn (@ttiimmees) January 23, 2026
Process as proof. Without the record, there's no distinction.
On TikTok, 'how I made this' content regularly outperforms the art itself. The comment sections on viral AI media overflow with requests for the 'workflow.' If process has always been central to promoting craft and discerning originality, the prevalence of this behavior on social media reveals more than curiosity. It points to a structural gap in how AI creation is captured and remembered. The medium lacks the shared systems that would preserve not just outputs, but the tools, models, and inputs behind them.
Archiving does more than separate slop from quality after the fact. Its presence is one of the primary ways quality emerges. Creative breakthroughs often come from building on someone else's technique. With AI, you might get the prompt if you're lucky, but not the model version, sampler settings, or which LoRAs (Low-Rank Adaptation models for fine-tuning AI) stacked in which order. Without systems that preserve technique, you're left rebuilding from a dusty screenshot instead of a living document. What's missing isn't individual memory but collective memory. An infrastructure through which creative practice compounds rather than resets with each new practitioner.
Why now? These systems are being built as you read. The open source ecosystem is blooming. Creative tools are multiplying. But the connective tissue that would let this ecosystem compound rather than fragment doesn't exist yet. In six months, a handful of platforms will have made architectural decisions that lock in extraction. Instagram didn't build creator tools until after it had captured the network. YouTube didn't add revenue sharing until creators had no alternative. The window to build open infrastructure is now.
What is TITLES?
TITLES is a creative platform where your work compounds over time. Artists own the AI models they train. Techniques can be shared while creators get credited and paid. Access extends beyond tools to technique itself. See how work is made. Build on artists you admire. They get rewarded when you do.
An open network for artist-trained and open source AI models
TITLES brings together open source foundation models and artist-trained systems in a shared network. When Mad Dog Jones fine-tunes a model on his neon-soaked cyberpunk aesthetic, he retains ownership. That model becomes an asset. Others can use it, and he receives attribution and revenue automatically. Unlike platforms where trained models disappear into someone else's infrastructure, models on TITLES are portable, working across any connected tool, their lineage preserved as others build on them.
A living record of creative process, not just outputs
TITLES approaches generation differently. Every generation records which models, parameters, and upstream assets were used. The result is an artifact of not just the output but the process. Think of the mess and iterations inside an artist's studio. We're building the digital equivalent, but alive. Others see how you made it. When they build on your technique, that connection is preserved. Payment flows automatically. No negotiation, no invoices, just infrastructure that routes value to creators.
All delivered inside Studio, our flagship creative tooling
Studio is where artists experience TITLES. It's a creative interface with access to the full model network. Generate images, video, and text. Browse and remix works other artists have shared. Before using any model, see exactly what it was built on. Unlike tools that hide process behind a prompt box, Studio makes everything explicit. Creation feels less like prompting into a void and more like working inside a living system where tools, techniques, and prior art are shared.
TITLES Studio tree view - visualizing creative lineage and model relationships
Public infrastructure that supports an ecosystem of creative tools
TITLES isn't just Studio. It's open infrastructure any application can build on. A workflow editor could connect and inherit the same attribution system. A mobile app could use TITLES models while routing payment to creators. A portfolio platform could display works with full provenance. By making inference publicly accessible and composable, we're enabling creative tools to live in one collective environment that share models, preserve attribution, and route payment across platforms. We're not building a walled garden. We're building the substrate that makes memory and ownership possible across all creative tools.
Closing
The problems facing AI creation share a common root: the absence of public memory. Without it, we cannot discern slop from quality, originality struggles to emerge, and creators lose ownership to platforms that control the only infrastructure that remembers. TITLES is a bet on the bazaar, not the cathedral. While much of the AI landscape races to build monolithic systems that centralize creation, memory, and value behind closed walls, we're building open infrastructure where artists participate directly in shaping the medium. Each model trained, each workflow shared, each inference recorded becomes part of a collective memory that catalyzes originality and returns ownership to creators. For artists, it's infrastructure that finally remembers. For everyone else, it's a way into a medium that used to be closed.
We'd love to have you along for this journey.
If you have any questions, please reach out to me directly.
If this vision resonates with you, join the waitlist below.