Is the next era of cybersecurity machine vs. machine? @shardul_shah of @IndexVentures tells @EquityPod that as AI-powered attacks get faster and cheaper, human-in-the-loop defense won’t be enough. The result could be an entirely new security stack.

Today, we're announcing @thatch_ai raised a $108M Series C at a $1B valuation from @thegp, @IndexVentures, @generalcatalyst, @a16z. The idea we're betting on: healthcare serves whoever controls the dollar. For 80 years that's been the employer. We think it should be you. Employers typically choose one health plan for everyone. We think there's a better model: give people a health budget and let them choose the insurance and care that fits their life. That is the idea behind Thatch. ARR has grown nearly 7x in the last twelve months, and more than 5,000 employers now use Thatch. None of this happens without our team, or without the employers who believe that their people know what their families need better than they ever could. We're building a healthcare system people love by changing how we fund it.

Wonderful has raised a $550M Series C at a $5 billion valuation. This latest funding was led by @insightpartners, with @salesforce joining as new investors and @IndexVentures, @BessemerVP, @IVP, @VineVenturesLP, and @9yardscapital returning. In 20 months since launch, we're in production with hundreds of agents, systems, and workflows across a dozen verticals in over 30 markets. With this new capital, we’ll be overdelivering for our customers, hiring hundreds of cracked engineers, FDEs, and former founders, and expanding our product capabilities across the entire enterprise. Stay tuned 👀

Announcing the Artificial Analysis Search Index, benchmarking how search API providers perform on quality, cost, and speed when used by an agent. We are initiating coverage with Parallel, Exa, Firecrawl, You (dot) com, Tavily, Keenable, and Brave Search is one of the most important tools for agents. Search providers make different choices about how they search, rank, and package results, and those choices change what the model reads and how it acts. We are expanding our benchmarking coverage to search APIs, so developers can pick a search provider on measured quality, cost, and speed. Each provider result pairs a search API provider with the same model, GPT-5.6 Luna (medium). The model runs inside Stirrup, our open-source agent harness, with tools for searching and fetching pages from the web - only the search provider behind the search tool changes. At launch, the leaderboard covers 11 results across 7 search providers, and we’ll keep expanding coverage as we look to provide the most accurate and comprehensive benchmarking of search providers for AI agent usage. Key elements of the Artificial Analysis Search Index: ➤ Three equally weighted benchmarks: the Search Index is the average of DeepSearchQA (900 broad research questions that need many searches, graded with an F1 score over answer items), BrowseComp (a 200-sample hard subset of facts that need multi-hop browsing), and AA-Omniscience (a 600 question private subset, balanced across 6 domains) ➤ Same agent, different search provider: the agent has 25 turns available to complete each task. Its web search tool returns the search provider's native response payload (with content modes standardized to snippets), with a maximum of 10 results and contamination sources filtered out ➤ Model-only baseline: we compare search agent results to the same model answering single-shot without tools, showing how much each provider lifts the model above its internal knowledge ➤ Cost and Time per Task: we aggregate the time and cost spent on both model inference and search. This is key - search APIs have different cost and latency structures, but these can be offset where they help an agent use fewer turns and save on costly language model inference Key results: ➤ Parallel, Exa, and Firecrawl have the strongest overall performance, with Artificial Analysis Search Index scores of 75, 74, and 73 respectively at launch ➤ All search providers tested substantially improve knowledge-based benchmark performance: the model only baseline scores 33 on the Search Index, while search-included provider results score between 65 and 75 ➤ Focused search results reduce spend on model inference: Parallel Search (advanced) search costs more per task than Parallel Search (basic) but less per task in total ($0.084 vs $0.11). Higher quality results cut the model's token use by over 40% in this case, more than offsetting increased search costs while reaching higher benchmark scores ➤ Fast search calls do not guarantee fast tasks: Parallel Search (turbo) has the fastest average search calls among Parallel's tiers (0.51s per query vs 1.03s for Parallel Search (basic)) but the basic tier scores higher on quality (73 vs 67) and the two land close on total time per task

1/ Today, we introduce Faraday, a 27B-parameter AI Scientist that extends the capabilities of coding agents with a layer of scientific intuition. Trained via long-horizon RL, Faraday outperforms Claude Opus 4.8 and GPT-5.5 on the task of replicating research papers. 🧵

Intelligence cofounder @grx_xce wants to build an intelligence marketplace for AI capabilities, not just a leaderboard that tells you which model is best. Today, Design Arena users submit creative tasks and have multiple AI models attempt it. The company then collects users' preference about which result was best, which gives Intelligence data about which models perform well for particular kinds of work. Grace thinks that in the future, it will become way easier for companies and individuals to customize models for one narrow purpose and sell access to it on a marketplace. She explains: