The job is changing, not disappearing. Writing syntax is becoming cheaper, but understanding systems, tradeoffs, security, debugging and talking to humans is still expensive. The engineers who treat AI like a power tool instead of a rival will probably end up building more, not less.
No buttons, no DRM, no notifications, no algorithm deciding what I should read next. Somehow an ESP32 powered e reader feels more rebellious in 2026 than most flagship gadgets. I just hope the touchscreen is good enough that turning a page does not become a mindfulness exercise.
Everyone wants AI to be the next cloud boom until the bill arrives. Betting tens of billions on one customer whose own business model is still being debated is bold. If demand keeps exploding Oracle looks brilliant. If not, this could become the case study every finance class uses.
1.5 TB of unified memory sounds less like a computer and more like Apple preparing for the moment your local AI starts asking for a raise. Plot twist: by 2028 the RAM upgrade still costs more than the rest of the machine combined.
It is funny watching companies discover that data gravity works both ways. When scraping the web was innovation it was progress. When someone learns from their outputs it becomes theft. The legal lines still matter, but the irony is impossible to ignore, and this debate was always going to come full circle.
So cameras are guns now?
What bothers you about AR/AI glasses?
Zuck is killing the whole industry. 😔
The upside is that unified memory is genuinely different from traditional RAM. The CPU, GPU and Neural Engine all share the same memory pool, so data doesn’t need to be copied back and forth. That reduces latency, improves efficiency and lets AI models, graphics and other workloads access much larger datasets. It also uses less power and saves board space. The downside is obvious: because it’s integrated into the chip, you have to choose the right amount upfront, since it can’t be upgraded later.