Our main takeaway from hacking OpenAI: AI is reducing the amount of scarce expertise needed to develop exploits. Work that once took months can now take days.
Even leading AI labs can be vulnerable.
Defenders need to fix the architecture, patch faster, and limit the blast radius of connected things.
@nikillinit I’m not seeing a ton of organizational innovation once the companies reach escape velocity (say ~40 people) and have to introduce some type of management. This may be sampling bias and I am willing (and eager) to be wrong. One of my litmus tests is how decisions are made.
Your observations are dyads.
For 1) and 2), perhaps existing tools are OK but not good enough to accomplish their task, are not deployed with clear guidance (wall-to-wall licenses with the hope of increased productivity), or trustworthy enough to overcome switching costs.
3) may not be technically accurate, but the anxieties have some truth. 4) shows the incentive misalignment. If those arbitrary employees draw out the dotted line, what should they expect? Likely more output, similar pay, and “headcount efficiencies.”
Re: AI-native companies - agreed on the persona difference. One other advantage is, hopefully, company building in a way to avoid “institutional” process debt. For some fast-growing startups, they copy-paste existing playbooks because “that’s how it’s done.”
AI-native companies are also where new hybrid roles emerge, such as product-minded FDE, talent (recruiting ops) engineers, and GTM (sales ops) engineers.