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What do today's AI unicorns have in common?

TinySet prompt
Every AI unicorn, with valuation, total funding, founding year and what they do
tinyfish-what-do-todays-ai-unicorns-have-in-common.csv152 rows
WebsiteCompany nameValuation (USD billions)Total funding (USD millions)Founding yearWhat they doIs AI unicorn
https://www.anthropic.com/Anthropic9651615002021AI safety and research company building the Claude family of frontier modelsYes
https://openai.com/OpenAI8521974002015AI research and deployment company; frontier multimodal AI platform and ChatGPTYes
https://databricks.com/Databricks100218002013Unified platform for data and AI using a lakehouse architecture combining data engineering, analytics and machine learning.Yes
https://www.anduril.com/Anduril Industries61113002017American defense technology company building AI- and robotics-powered autonomous systems (drones, counter-drone, surveillance towers and Lattice command-and-control software) for the U.S. military.Yes
https://www.moonshot.cn/Moonshot AI5089002023Chinese AI lab building Kimi frontier language modelsYes
https://cognition.ai/Cognition4825002023Cognition builds Devin, an autonomous AI software engineer that plans, writes, tests and ships production code alongside engineering teams.Yes
https://www.figure.ai/Figure AI3919002022Figure develops AI-powered general-purpose humanoid robots (Figure 03) with its Helix embodied-intelligence model for household and commercial work.Yes
https://ssi.inc/Safe Superintelligence3280002024Research lab with one goal and one product: a safe superintelligenceYes
Showing 8 of 152 rows

Then TinyResearch on the dataset:

TinyResearch prompt
What do today's AI unicorns have in common, and what does it say about where AI value is being created?
tinyfish-what-do-todays-ai-unicorns-have-in-common.pdf

As of late October 2026, the global AI unicorn landscape has reached unprecedented scale and concentration. Approximately 272–308 private AI companies are valued at $1 billion or more, collectively worth an estimated $7.8 trillion, with AI companies accounting for roughly 24% of all newly minted unicorns in 2026 and 82.7% of all U.S. venture deal value through Q3. The cohort shares five defining characteristics: extreme capital intensity concentrated in a handful of frontier labs; rapid revenue scaling with thin margins; a gravitational pull toward enterprise workflows in specific verticals (legal, coding, finance, healthcare); infrastructure and data as durable value layers; and an emerging agentic business model that reprices labor rather than software licenses. These commonalities point to a fundamental shift in where AI value accumulates—away from pure model development toward infrastructure control, workflow embeddedness, and enterprise distribution at scale. The IPO pipeline is active, with Anthropic and OpenAI both filing confidentially in June 2026, signaling that the private-market valuation era for frontier labs may be drawing to a close.

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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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