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Why are AI startups shutting down?

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TinySet prompt
Every AI startup that shut down in 2026, with what they built and funding raised
tinyfish-why-are-ai-startups-shutting-down.csv29 rows
Website URLNameWhat they builtFunding raised (USD)Shutdown dateReason for shutdown
https://www.phind.com/PhindAn AI-powered search engine built specifically for developers, using multi-step reasoning to answer coding questions and cite sources from Stack Overflow, GitHub issues, and technical documentation.110000002026-01-16Ran out of money after failing to defend its moat: foundation-model providers (OpenAI, Google, Anthropic) added web search, making a dedicated developer search engine redundant.
Cartwheel RoboticsYogi, a compact bipedal toddler-like social humanoid robot for companionship and public spaces (plus Speedy variant)30000002026-02-05Failed to secure funding/Series A; ran out of capital for capital-intensive humanoid hardware; creditors forced involuntary bankruptcy as Figure AI, Apptronik, 1X and Sanctuary AI absorbed the category's capital
https://www.soulmachines.com/Soul MachinesAI 'Digital People' avatars — human-like AI assistants for customer service, powered by a Digital Brain1350000002026-02-05Steamrolled by LLMs; KPMG receivers appointed after years of losses
https://www.kintsugihealth.com/KintsugiVoice biomarker technology to detect depression and anxiety from how people speak300000002026-02-11Shut down after ~4 years waiting for FDA De Novo clearance that never came; open-sourced all code and research
CydocA HIPAA-compliant AI-native EHR for web, Android, and iOS, featuring a Smart Patient Intake Form that automatically takes a patient's history and generates a clinical note for the doctor.2650002026-02-21Ran out of money: unsustainable unit economics, as each practice's EHR integration cost $4,000–$6,000 against a sub-$100/month subscription (the founder ruled out integrating with incumbent EHRs like Epic for years), plus delayed customer discovery and no moat.
https://vercept.comVerceptVy, a cloud computer-use AI agent (built on the VyUI interface-recognition model) that operates apps and controls a remote MacBook via screenshots, without APIs500000002026-03-25Acquired by Anthropic (announced Feb 25, 2026) to advance Claude's computer-use capabilities; external product wound down with 30-day customer notice, team acqui-hired
https://www.yupp.ai/YuppA free crowdsourced AI model-comparison playground that let users test and compare responses from 800+ AI models side by side, vote on the best answers, and earn cryptocurrency for their feedback.330000002026-03-31Never reached strong enough product-market fit to survive, in part because AI models improved so rapidly; the industry shifted toward hiring expert humans for RL feedback and building for agentic systems.
https://weights.ggWeightsCommunity voice-cloning and AI-music platform (weights.gg)40000002026-03-31Never profitable; consent-free celebrity voice library a liability as deepfake laws tightened; OpenAI later bought tech and ~6 engineers
Showing 8 of 29 rows
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TinyResearch prompt
Why are AI startups shutting down? Look for patterns across these companies.
tinyfish-why-are-ai-startups-shutting-down.pdf

AI startups are failing at an accelerating rate, but the causes are more structural and systemic than the popular narrative of "too much hype" suggests. Across hundreds of documented shutdowns from 2023–2026, six dominant failure patterns emerge: (1) catastrophic unit economics driven by compute costs, (2) product cannibalization by Big Tech foundation model providers, (3) the "wrapper trap" — thin differentiation on top of third-party models, (4) talent extraction through reverse acquihires that hollow out startups, (5) litigation and regulatory exposure concentrated in consumer-facing AI, and (6) a global funding concentration that leaves non-U.S. startups starved of capital. These patterns are not independent — they interact and accelerate each other. The canonical case study is Character.AI, which filed for Chapter 11 bankruptcy on October 12, 2026, illustrating nearly every failure mode simultaneously. Decision-makers evaluating AI startup investments, partnerships, or competitive strategy should treat each of these patterns as a live structural risk, not a historical artifact.

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What is YC betting on in AI right now?

153 rows · YC has made its most aggressive, explicit pivot in its two-decade history. AI is no longer a theme within batches — it is the substrate. Roughly 95% of the Spring 2026 batch touched AI, and across 2026 batches approximately 60% of companies are explicitly AI-focused, up from ~40% in 2024. The directional shift visible in the Fall 2026 Request for Startups (RFS) — 13 categories, framed as "AI moving into the physical world" — is the clearest signal of where YC is placing chips next: physical infrastructure, defense, human-AI collaboration frameworks, and the "ownership" of intelligence rather than renting it. The December 2 Demo Day will reveal the F26 cohort fully, but early company data and the RFS together paint a sharp strategic picture right now.

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Which LLM API gives the best value for money?

105 rows · The LLM API market in October 2026 is sharply bifurcated: a cheap tier ($0.10–$0.75/M input tokens) that now delivers capability matching premium models of 18 months ago, and a frontier tier ($4–$10/M input) for the highest-stakes agentic and reasoning work. The single most important finding is that the efficiency frontier has moved decisively: GPT-6.1 Sol at $2/$10 scores 51.8 on the Artificial Analysis Intelligence Index and Claude Sonnet 5.5 at $2/$10 scores 47–56 depending on effort level, while Claude Haiku 5.5 and GPT-6 Luna deliver scores of roughly 28–43 at just $0.10/$0.50—a price point that was frontier-model territory less than two years ago.

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Which AI conferences in 2027 are worth the ticket?

125 rows · The 2027 AI conference landscape divides cleanly into four categories: (1) flagship academic research conferences (NeurIPS, ICML, ICLR, CVPR, AAAI, ACL, ICCV) that are peer-reviewed, selective, and best for researchers and technically senior engineers tracking the frontier; (2) applied/builder conferences (AI Engineer World's Fair, MLSys, Databricks Data+AI Summit, NVIDIA GTC) that are best for engineers shipping production AI systems; (3) commercial/executive summits (RAISE, AI Summit London, HumanX, Ai4) best for C-suite, founders, enterprise buyers, and investors; and (4) subfield-specialist venues (IROS, RSS for robotics; ACL/NAACL/EACL for NLP; CVPR/ICCV/WACV for vision; FAccT for AI ethics) that serve narrow technical communities.

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