Speaker
Theo
Appearances over time
3 episodes
Episodes
3
Podcasts
Quotes & moments
Muse Spark 1.1, GPT Live 1, Grok 4.5, and the GPT-5.6 Sol/Terra/Luna family all dropped within a single week, creating unprecedented model evaluation pressure.
Theo spent $131,700 and Ben spent $93,000 in API tokens testing GPT-5.6 Sol during the early access period.
Microsoft invested $2.5 billion in hiring approximately 6,000 forward-deployed engineering experts to help companies adopt AI.
Instead of the planned 14-day Fable trial window, users got 3 days before the export ban, and then 7 days at half the rate limit — effectively ~7.5 days total vs. the promised 14.
Alibaba allegedly scraped 28.8 million Claude exchanges via 25,000 fraudulent accounts to use as model training data.
A $200/month Codex subscription can yield up to $20,000 worth of token usage per month when factoring in usage resets that restore a full week of compute.
Fast mode multiplies token usage by 2.5×, meaning a single prompt that might use 10–15% of a 5-hour window with Fast off can hit 40% with it on.
Anthropic's secondary safety classifier increases compute per request by 27% to 80%, prompting them to build a cheaper first-stage check costing only ~0.05% of compute.
An analysis of Theo's session logs comparing GPT-5.6 and Claude Fable-5 concluded Fable is the stronger strategic advisor while 5.6 is the stronger day-to-day coding agent.
Anthropic's classifiers reduced jailbreak success from 95% to 4%, and then their latest two-stage system brought it down to under 0.5%.
Fable (Mythos) dropped from its originally announced ~$125 per million output tokens to ~$50, making it 30–40% more expensive than Opus but often cheaper due to lower token usage.
When tasked with comparing plans, Fable-5 voted for its own plan 6-0 even when it could acknowledge benefits of the alternative, showing it's worse at critiquing its own work.
Theo spent roughly $150–$200 of Fable API usage to review, close, rebase, or land over 30 pull requests in a 5-hour agentic loop.
Moving agents to Linux eliminates macOS's aggressive process monitoring of Codex subagents, which was causing syspolicyd to consume 215% CPU on an M5 Max.
Apple's hardware chief exec Peng Tang was poached by OpenAI and reportedly brought 40 colleagues with him, and is alleged to have continued accessing Apple's confidential files after leaving.
The fundamental problem with letting AI write your Claude Code skills is that models write what they already do by default. Skills are supposed to steer the weights toward behaviors the model doesn't naturally exhibit. An AI generating a skill for itself is writing a no-op.
Not adopting AI isn't just slow — it's fatal. Unlike moving from COBOL to Go (a 2–3x output gain you could offset by hiring), AI creates a gap so large that a company without it simply cannot compete with one that has it. The difference isn't just performance; it's existential.
Alibaba allegedly acquired 28.8 million Claude exchanges via 25,000 fraudulent accounts — likely by offering subsidized Claude Code subscriptions that route all traffic through a data-collecting forwarder. It's not necessarily Alibaba doing it directly; third-party brokers may sell the data to them.
Tools like ViProxy let users load-balance multiple Claude Code accounts to maximize access to Fable during its limited window. But Theo predicts Anthropic will kill this method in 2–3 weeks by requiring a rolling cryptographic signature in Claude Code's special OAuth endpoint — and Alibaba is to blame for accelerating that.
OpenAI offering the Trump administration a 5% stake is the most performative move Theo has ever seen — but also probably the right one. The calculation is simple: make the government feel important enough to let the labs work, and buy time until people who actually understand AI can build real oversight systems.
The 7-day Fable window at half rate limits wasn't a consolation prize — it was a calculated GPU provisioning experiment. Anthropic needed exactly one full week of data to see usage rates across every day of the week so they can plan how to allocate compute between enterprises and subscribers. The FOMO from the export ban was also a feature, not a bug.
Running Fable subagents from a Fable orchestrator is a fast path to burning 30% of your weekly limit on nothing useful. The right pattern is Fable commanding cheaper models — Sonnet 5, GLM-5.2, or similar — for implementation work while Fable handles planning, context, and review.
Anthropic's rerouting system has two stages: a cheap first-stage classifier (~0.05% compute) that watches which expert pathways activate in the model, and an expensive second-stage classifier (27–80% extra compute) that only fires when the first flags a request. The result: jailbreak pass rates dropped from 95% to under 0.5%.
Fable dropped from $125 to $50 per million output tokens, making it 30–40% pricier than Opus per token — but far cheaper overall because it uses fewer tokens. The result: Opus is dead. Theo won't maintain a $200/month sub for Opus alone, and thinks Opus 5 needs to be cheaper or it won't survive either.
GLM-5.2 is fast, cheap, and produces clean code easily controlled by a smarter orchestrator. Sonnet 5 edges it out only in one scenario: very long-running jobs requiring recursive self-calling and review loops. Outside that, GLM-5.2 wins on price-performance — and even Theo would pick it over Sonnet if he still had Fable.
Theo gave Fable his entire backlog of stale, conflicted, and draft PRs and told it to figure out which ones to keep, merge, rebase, or kill. Without needing step-by-step instructions, the model categorized overlapping work, wrote plans for missing features, and landed or closed over 30 PRs in a single 5-hour loop — for about $150–$200.
Theo, mid-episode, gave Sonnet 5 a live task: use Codex computer use to browse the web and order a custom shirt that says 'Sonnet 5 made this shirt.' The model navigated a custom apparel site using browser automation, auto-filled Theo's zip code, and nearly completed the order — getting about as far as anyone expected.
Microsoft is spending $2.5 billion to hire 6,000 forward-deployed engineers — essentially consultants whose job is to show up at companies and force AI adoption. The real insight: FDEs don't work because they're eloquent; they work because they lock in 3–5 people per month and physically make them use the product.
Running 50 simultaneous Codex subagents on macOS means 50 separate MCP processes, causing syspolicyd to consume 215% of CPU on an M5 Max. Linux has none of these process-monitoring penalties, so you can spin up dozens of subagent threads simultaneously with no performance hit.
The best skills come from analyzing your full usage history, finding repeated correction patterns, and writing guidance that steers the model away from documented failures. Writing a skill once and never revisiting it is almost always wrong — treat skills like living documents.
Analysis
What they talk about
- Technology 84%
- Business 10%
- Government 3%
- News 3%
Connections
Shows they appear on and people they share episodes with. Drag to explore.
