Databricks'
@alighodsi on AI risk and adoption:
Ali isn't losing sleep over the existential risk debate.
He says a number of conditions would all have to be true simultaneously to enable an actual runaway takeoff scenario, and currently several opposite conditions exist.
Each frontier training run requires significantly more resources. Power, GPUs, engineers - and some attempts fail, burning up huge piles of money with them. Until that reverses, he doesn't see the self-improving loop happening.
Cyber is what he's watching most closely and where he anticipates real impact. Most orgs are not equipped for the coming change in agentic capabilities. The time between a vulnerability being published and being weaponized has collapsed from years to hours.
On adoption, he believes most companies don't need a smarter model. The models are already smart enough. The gap is context they don't absorb - the things an employee who's worked at a company for five years learned by osmosis.
If the frontier stopped advancing today, he thinks it wouldn't meaningfully change the value most are extracting from AI anyway.
In conversation with a16z's Martin Casado and Sarah Wang:
00:00 Intro
00:48 Why Ali places the AI risk near zero
05:05 The word "pacing" was a mistake
10:50 What 10k agents and $100m can do
12:20 What would change his mind on AI risk
14:20 More GPUs, more ways to fail
18:05 US export controls on PlayStations
20:10 The damage everyone expected by now
24:30 Public vulnerabilities weaponized in hours
30:15 Why labs can't grade each other
37:15 Why most of RSI isn't actually RSI
40:05 Why nobody really needs a smarter model
41:50 The AI use cases nobody argues about
47:30 Google Search solved this 25 years ago
50:55 Nobody has privileged knowledge now
55:10 Same model, new harness, 2x cost
58:15 Open source: 5% of spend, 60% of tokens
1:05:30 90% of new databases are created by agents
YouTube:
piped.video/GzEtpAKYRvE
@databricks @martin_casado @sarahdingwang