Defense startups rising, and how Cribl secured a $3.5B valuation

Top News

• Y Combinator backs its first defense startup: Ares Industries, who joined YC S24, aims to “build low-cost cruise missiles that will be compatible with existing launch platforms” and will be “10x smaller and 10x cheaper” than today’s solutions, TechCrunch reports. In light of current conflicts in Europe and the Middle East and recent big-ticket investments (see Anduril), expect to see more defense technology startups pop up.

• Cribl raises a new round of funding: The data infrastructure startup, which “organizes companies’ data for IT and security teams” raised a $319 million Series E at a $3.5 billion valuation, led by GV (formerly Google Ventures). How do you boost your late-stage valuation in 2024? Turn in what Cribl reports is 163% compound annual growth over the last four years, driven in part by net dollar retention of more than 130% over the last four quarters. That’ll do it.

• Cost savings drive startup growth: Companies wanting to save money on their cloud bills are helping power startups like nOps, which has built a tool to help AWS customers send less of their checking accounts to Amazon. The great optimization push that saw cloud infra growth slow for a few quarters may be behind us, but the business of saving other companies money on their compute and storage needs could be big business for nOps and its peers. nOps just raised $30 million, sharing along with the news that its customer base has scaled 450% in the last six quarters.

The latest from Anthropic

Yesterday we added Winnie to the TWiST500 thanks to its rapid growth and huge market. Today we’re adding Cribl thanks to its massive late-stage raise and super-quick revenue growth. nOps is a maybe, but I think a bit more research into its market is required first.

Elsewhere in TWiST500-land, Anthropic. The AI model giant — backed by Google, Amazon, SK Telekom, Spark Capital, G Squared, Menlo Ventures and others — made news recently by sharing the system prompts that underpin its models. LLM makers use system prompts to tune, or block certain outputs.

Anthropic’s Claude 3.5 Sonnet model, for example, is governed by language including the following:

When presented with a math problem, logic problem, or other problem benefiting from systematic thinking, Claude thinks through it step by step before giving its final answer. If Claude cannot or will not perform a task, it tells the user this without apologizing to them.

Sometimes, the dictates are stylistic; other times, they deal with security. For example, Anthropic has told Claude that it should “always [respond] as if it is completely face blind.” So much for abusing the model for facial recognition, I reckon.

Sharing the system prompts was a big deal, and could lead to a bit more openness from closed-source AI models.

— Alex

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