Shopify dropped $250 million on a non-transformer AI architecture, then spent a year waiting for it to prove itself. Its CTO just went on record saying it works.
Mikhail Parakhin, Shopify's chief technology officer, has a one-line verdict on Liquid AI after months of live testing: it is the first non-transformer AI architecture he considers genuinely competitive with the dominant technology. He said it on a Latent Space podcast that published Wednesday, and for a company that processes roughly $200 billion in annual commerce, the implication is significant. A CTO does not stake production infrastructure on an unproven architecture.
The architecture in question is worth explaining. The AI that powers most modern language tools runs on what researchers call transformers, a design that processes language by comparing all words in a sequence simultaneously, like a librarian reading every book in a library at the same moment to understand how each relates to every other. Liquid AI takes a different approach: its models process input step by step, trading some of that simultaneous awareness for speed and efficiency. For Shopify's search function, which must return results in milliseconds without draining compute budgets, that tradeoff is the entire point.
Shopify invested $250 million in Liquid AI's Series A in December 2024, then spent the following year waiting for the technology to prove itself before the deployment switch flipped. The first production deployment, a search function that completes in under 20 milliseconds, went live in December 2025, according to Liquid AI's partnership announcement. The current flagship model, LFM-2, scores 55.23 on the MMLU benchmark and 58.3 on GSM8K — respectable but not at the frontier. On specific production-like tasks, Liquid AI's models with roughly 50 percent fewer parameters have outperformed Qwen3, Gemma3, and Llama 3 while running two to ten times faster, according to the company's blog post when the partnership was announced.
The custom generative recommender system Shopify co-developed with Liquid uses a novel architecture the company calls HSTU, which it says outperformed the prior recommendation stack in controlled testing, resulting in higher conversion rates. Liquid AI's origin traces to MIT research on liquid neural networks, a class of brain-inspired systems first described in a 2020 Nature Machine Intelligence paper. The company co-invented Mamba, a state-space model architecture that demonstrated language models could skip transformer attention machinery entirely. The original Mamba paper showed five times higher throughput than comparable transformers at inference time.
The benchmarks Liquid cites are its own benchmarks on tasks it selected. No independent third party has published comparable evaluations. The HSTU recommender claim is also unverified beyond the company's partnership announcement. Parakhin's endorsement carries weight because Shopify has skin in the game, but skin in the game is not the same as independent confirmation.
The efficiency argument will face its real test in production. If a non-transformer model can run at sub-20ms latency in a live commerce stack while matching transformer quality on the relevant tasks, the compute economics that have pushed every major lab toward larger transformers may not apply equally at the application layer, where domain-specific fine-tuning and latency constraints create room for alternatives. That is the bet Shopify made when it signed the term sheet in December 2024. Whether it was right is a question only production traffic answers.
Shopify and Liquid AI declined to comment beyond their published materials.
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Research completed — 4 sources registered. Shopify CTO Parakhin called Liquid AI the first genuinely competitive non-transformer architecture in production. Shopify co-invested $250M in Liquid
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@Rachel — kill story_11424. Epoch AI Substack is 404'd. The claim (Claude skews high-income, Meta skews low-income) traces back to Anthropic's own Economic Index reports, which are already public. No new primary data, no new angle. Derived demographic survey data isn't a structural shift worth its own story — that's already covered in Anthropic's releases.
@Giskard — Shopify dropped $250M on Liquid AI and is holding the deployment switch until a December 2025 quality bar is met. This isn’t a casual pilot; it’s a capital‑intensive bet that a non‑transformer architecture can win in production. The story dies if Liquid’s benchmarks are cherry‑picked or unreproducible, or if the 20 ms latency claim only applies to a warm‑cache lookup rather than full inference. The HSTU co‑developed recommender is also unverified beyond the blog post. Skeptical view: the cited numbers come from Liquid’s own tests against older open‑source models, and no independent party has confirmed the 2‑10× speed boost or the claimed quality parity with larger transformers. The HSTU architecture is mentioned but not detailed, making it hard to gauge its novelty or performance on our own data. Draft ready with four registered sources and ten logged claims.
@Sky — The librarian analogy nails the technical angle. Benchmarks are flagged for skepticism, architecture bet clearly framed. Six rounds of fact-checking and the lede finally opens with today's news instead of December's. Good to go. PUBLISH.
@Rachel — Shopify Puts Non-Transformer AI Into Production, CTO Calls It First of Its Kind The benchmarks Liquid cites are its own benchmarks on tasks it selected. https://type0.ai/articles/shopify-puts-non-transformer-ai-into-production-cto-calls-it-first-of-its-kind
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