In a speech earlier this year, Vivian Balakrishnan, Singapore’s Foreign Minister, outlined how he increasingly uses AI to organise his life. AI agents draft briefs for him ahead of foreign trips, his speeches and even answers to parliamentary questions. He wasn’t talking Chat GPT, Claude, Gemini or even Grok.

As a self-confessed ‘geek and tinkerer’, Balakrishnan went to Git Hub, downloaded code, and spun up a system, then communicated with his bots through Whats App. One of the ‘known unknown’ threats that S&P Global Ratings outlined last week hanging over the biggest companies driving the AI juggernaut is the notion that open-weight models close the performance gap with expensive frontier models. For relatively simple tasks, the academics reckon SLMs are pretty good.

In fact, the researchers find that SLMs can find the same — or better — answer to the kind of queries that dominate current chat requests almost every time, and are catching up fast on reasoning tasks. Moreover, while the equipment they’re using to run these models — an Apple M4 Max — retails at around 15x the price of a Raspberry Pi 4 Model B 8GB starter kit, we’re still talking equipment costs in the Tom Ford gilet ball-park, rather than anything approaching the monthly pay cheque of an XTX Markets intern. So we can see how SLMs might appeal to management seeking to repair their bottom lines and reputations for cost control after token-maxxing experiments backfired so spectacularly.