Sulaiman Ghori (Sully), xAI engineer. Describes xAI as an extremely fast-moving, high-leverage company with insane talent density, minimal bureaucracy, and hardware advantages that let them iterate models and products at breakneck speed.
tldr
- •no real deadlines beyond "yesterday" - ideas get implemented same-day, shown to Elon or teams for instant feedback
- •flat structure: mostly engineers (everyone contributes code), fuzzy team boundaries, bottom-up decisions, very few managers
- •biggest advantage is rapid infra (Colossus built in 122 days, doubled to 200k GPUs quickly)
- •daily (or multiple/day) pre-train iterations on novel architectures - shred conventional timelines
- •"Macrohard" project: human emulators for digital tasks - goal is to deploy millions cheaply
culture & pace
- •"no one tells me no" if the idea's good
- •people own huge chunks of stack quickly by shipping fast
- •high trust - you can touch almost any codebase
- •surges/war rooms common (Macrohard in "war room" 4+ months; slept in pods/bunks)
onboarding
- •Sully got a laptop + badge, no assigned task/team
- •wandered, helped on X integrations (Grok + X), then jumped projects
- •joined after his startup failed; recruited by Greg Yang
hardware edge
- •new racks train same-day/hours after setup
- •use mobile generators + batteries to handle power swings without grid issues
- •bets like "Cybertruck if training run in 24h" - won
- •temporary "carnival" land lease for fast permitting
model iteration
- •small models + fast iteration > huge slow ones (parallels Tesla FSD)
- •removing software overhead/latency is key
- •$2.5M value per main repo commit (Sully did 5 in a day)
macrohard (human emulators)
- •emulate humans for digital tasks (keyboard/mouse/screen decisions) so no software changes needed
- •insight: Tesla car computers are capital-efficient - potential to run emulators on idle fleet (4M+ cars, many HW4, idle 70-80% time)
- •pay owners to lease compute - enables 1k to 1M scale with minimal new buildout
- •focus on speed (much faster than human) over pure reasoning scale
- •tested as "virtual employees" (fun bugs: people pinging non-existent desks)
elon factor
- •predicts bottlenecks far ahead, works backwards from metrics (revenue/physical)
- •quick fixes via calls/patches
- •meetings: high-level product direction or deep low-level (e.g., latency/compute)
- •feedback often experiment-driven
hiring
- •broad "engineer" label (problem-solvers from anywhere)
- •hackathons, aggressive interviewing (Sully did 20+/week)
- •seek simple solutions, challenge bad requirements, use AI as multiplier
- •talent so high it's hard to keep up
sully's background
- •young tinkerer (fidget spinners business, 3D printers)
- •built liquid rocket engine in 24h push - almost set himself on fire