A variant on Moore’s law is the observation that the financial capital required to launch a new business decreases exponentially as technology gets cheaper.

Pre-internet, and using a notional geometric scale for the purposes of illustration, you might have needed $5m to found and build a new venture. The World Wide Web probably reduced that to $500k, while cloud computing brought it down to $50k. With the expansion of SaaS and API solutions, that cost might have been $5k to get going. Now, vibe coding and $500 of AI prompts can probably launch a new website, build a back end database, implement an e-commerce solution and deploy agentic AI bots to go and find your first customers.

This is a great outcome if measured by a lower barrier to market entry. It also enables founders to “fail fast, fail cheap”, and incentivises innovation by financially de-risking the process.

But even though the cost of AI tools is extraordinarily cheap in terms of the computing and processing power they deliver, there is a huge cost to our rapid adoption of AI that needs to be accounted for.

First, we are seeing corporate lay-offs among tech firms and parts of the service industry that no longer need as many human bodies and minds to operate at scale. So there is a human, economic and societal cost of increased un(der)employment.

Second, traditional skills and expertise are being hugely reduced in perceived value – why pay a graphic artist to design an image when I can use dall-e for free?

Third, as more and more creative tasks are being outsourced or delegated to AI (“create a short story about an F1 race in the style of Ernest Hemingway”) we risk losing our own innate creativity (that comes with experimentation, curiosity, play and reflection). This in turn devalues the creative process itself (thanks to cheaper, AI-enabled production).

Fourth, AI (and the Large Language Models on which it is trained) has no great respect for intellectual property. It doesn’t recognise boundaries between copyright material, content that is subject to creative commons, content that is in the public domain, and content which is publicly available. Again, if copyright owners and original content creators are not recognised or compensated for their work, why would anyone aspire to creating anything original?

Finally, there is the cost of resources (energy, water, rare earth metals) needed to maintain huge AI processing plants and data centres. (But at least this demand is accelerating the development of renewable energy.)

A few years ago, I posted a blog about the importance of the human factor, in the face of technological progress brought by automation and AI. I still remain cautiously optimistic that AI will bring huge benefits, despite the rampant growth of AI in the three years since I wrote that piece. But we are currently in an awkward and comfortable transition phase. If more jobs are lost to AI, and if human-led output is increasingly devalued, perhaps we will need to revisit the debate about Universal Basic Income and other policies to facilitate this transition.

Next week: Music, music everywhere…. and none of it very memorable