1. The myth of junior employment
Nearly a third of surveyed Anthropic staff believe their Mythos Preview model could replace junior engineers and researchers within three months.1 Treat this as a directional signal that people closest to the frontier are starting to believe that a lot of junior‑level work is now in scope for automation.
2. Huge enthusiasm, limited adoption
AI inference costs at companies are approaching 10% of engineering headcount costs, according to Goldman Sachs. AI adoption is both inevitable and uneven. Excitement and FOMO are high, but adoption is lagging despite the investment. The only way to close that gap is through deliberate learning and experimentation.
3. Teams that learn to work with AI win big
One company nearly halved the cost of each code change they shipped and doubled weekly deployments over five months of intentionally building with AI.
4. Users feel the compute crunch
Claude API uptime fell to 98.32% in March, well below the 99.99% standard.
As we wrote earlier this month, users are feeling the compute squeeze. Across every major AI platform, usage allowances tightened last year – more tiers, stricter limits, changes that often showed up without notice.
