If Harvey reached a $15.5 billion valuation, the legal AI startup would be worth more than Clio, Relativity, Ironclad, and Everlaw — combined. #legaltech
A golden retriever suffering from seizures seemingly lost her spunk — until one day her owners brought home a miracle cure — a duck named Louie. @SteveHartmanCBS is On the Road.
I've now added Wispr, Notion, Granola, Fireflies, Zoom, Gemini, Cluely, Otter, Fathom, Read AI, and Wingrep to every meeting.
Each produces slightly different meeting notes.
I send all of them to Claude and ChatGPT, which push the summaries into Slackbot and Salesforce AI. Linear agents automatically create tickets while Notion agents create follow-ups in Notion. A Zapier workflow then uses 500k tokens to draft a follow up email in Superhuman.
None of it works and all of it is wrong but man, is it beautiful.
AGI is here.
I've seen some serious scrutiny on this post. The human cost assumptions may be too high. Fine. Cut them by 99%.
The $4,200 contract redline becomes $42. Against the table's 12¢ AI run, that is still a 350× gap, assuming comparable quality.
The benchmark may be imperfect, but do not mistake that for not having signal, here.
These numbers should scare you.
A human reviews an NDA in 6.2 hours for $2,480. AI does it in 11 seconds for 3 cents. contract redline, 35,000x cheaper. patent search, 45,000x. same quality, passes adversarial review.
Here's what that actually means, and it's bigger than "AI is cheap."
For decades every company checked a sample and trusted the rest. not from laziness, from math. a human reviewing every contract, every invoice, every line was never affordable. so you spot-checked and hoped.
These numbers just killed that math. when review costs three cents instead of thousands, "we could only check some of it" stops being a reason.
Freehand is the first company to build an entire product on that collapse, reading 100% of a company's invoices instead of a fraction, catching the overcharges and duplicates that always hid in the sample nobody had time to read. already live at Meta, Apple and J&J.
The table is scary because it's cheaper. the real story is that sampling, the way business has run forever, just stopped making sense.
I’m officially calling this the Gauntlet Loop.
The agent (not you!!) breaks the goal into parts, gives each part a specialist builder and a ruthless blind critic sub-agent, with a mandate to only pass if the generated artifact is better than some real-world equivalent.
I can speak from experience, this is very true for products that need scale.
It’s faster than most sales cycles and it will be the expectation for custom solutions going forward.
If you’re not stripping down and re-writing product dev, regardless of application, you’ll be left in the dust.