Four years after ChatGPT debuted, companies like NAB and Qantas are pursuing standard restructuring and offshoring to India rather than replacing workforces directly with automated tools. Tech platforms experience uneven exposure, with graphics services facing rapid substitution while enterprise management software proves far more resilient. Furthermore, major hyperscalers are being challenged by low-cost open-weight rivals like DeepSeek and Mistral that operate on a fraction of their capital.
Key points
Enterprise software providers remain far more insulated from artificial intelligence disp…: Enterprise software providers remain far more insulated from artificial intelligence displacement than consumer-facing subscription graphics platforms.
Massive corporate spending on data centres has failed to yield verifiable and widespread…: Massive corporate spending on data centres has failed to yield verifiable and widespread productivity gains across the market.
Large language models risk turning into low-margin digital utilities, which threatens inv…: Large language models risk turning into low-margin digital utilities, which threatens investor capital rather than overall employment levels.
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In 1987, the Nobel prize winning economist Robert Solow remarked: “You can see the computer age everywhere but in the productivity statistics.” At the time, computers had been around for 40 years. Real productivity gains did emerge in the 1990s after the arrival of the internet, but they were short-lived.
Four years after the launch of ChatGPT, the pattern seems to be much the same: lots of hype and excitement but with very little productivity growth. The main economic impacts, as in the 1990s dotcom era, have been a stock market boom and huge investment in infrastructure (fibre optics then, datacentres today), betting on future demand that has yet to emerge.
Still, the announcement of large-scale job losses at NAB, Qantas, Canva and Atlassian, some of them explicitly blamed on AI, raises the question of whether the job apocalypse is finally here. The evidence suggests otherwise.
NAB has specifically denied that AI played a role in its decision, and there is no reason to disbelieve this. The NAB sackings seem to be business as usual for the bank which, since…
The artificial intelligence company is pursuing public market capital to satisfy massive infrastructure demands, including data centers, electricity, and specialized processors. Operating as a public benefit corporation valued well above $1 trillion, the firm aims to deploy systems capable of solving major global problems. However, leadership balances this ambitious vision against catastrophic hazards that could destabilize global security and modern economic systems.
Modern automated programming tools rely on models trained by scraping decades of freely shared software from platforms like Stack Overflow and the GNU Project without proper attribution. Converting these outputs into closed, proprietary products essentially functions as license laundering while contradicting United States legal precedents on human authorship. Establishing standard open-access practices protects the collaborative software ecosystem that made automated coding possible in the first place.
In-depth profiling of an H100 GPU revealed that unexpected stalls were caused by excessive data transfers back to host memory rather than processing limits. Investigators used the Tensward analysis library through NVIDIA's CUPTI interface to discover that prompt caching defaults were blocking client requests. Resolving these memory transfer bottlenecks dramatically improved system responsiveness and allowed the hardware to operate closer to its true compute potential.