AI Beyond the Digital Economy

AI pioneers have a common dream, which they refer to as Artificial General Intelligence (AGI): that nebulous AI model which is more advanced than any human. It's all many tech firms seem to talk about. Yet, no one can agree what AGI is, let alone how it will impact consumers and businesses. Thankfully, we're starting to see a real debate about the impact of AI on employment and the wider economy. This debate is being driven by Silicon Valley circles, and rather ignores the fact there is a real economy which is only partially linked with the digital economy. A recent article in the Economist examined the long-term impacts of AGI. It was a timely article on an important topic. Yet, like many contributions on AGI, it hand waves away the differences between the digital economy and the real economy. That's a mistake, which has very real consequences on how disruptive AI will be in practice.

The Impact of R&D

At one extreme, the likes of Sam Altman are talking as if AI will abolish the law of supply and demand. It won't and it can't. Supply and demand is driven by the scarcity of real world goods. AI potentially provides businesses with limitless access to digital labour, but it doesn’t help with the cost of raw materials needed to produce those real world goods. Nor does it reduce the cost of energy. That's a major concern at the moment because high natural gas prices are currently a major drag on economic growth. This problem is compounded by the rush to build data centres to run ever more complex large language models. Connections to the energy grid are severely limiting the pace of the data centre rollout. In the same vein, high-end servers require liquid cooling. That drives demand for scarce water supplies, which is also a real concern in some regions.

AI boomers believe that AI can invent new technologies to overcome current resource constraints. In some fields that will be true. In other fields it won't happen. Either way, tech firms are rather ignoring the timescale needed to develop, test and commercialise new technology. Software firms are used to creating and launching new products in near real time. That doesn't work in manufacturing, where supply chains need to be setup and factories constructed. In the physical world, it takes years to bring a new technology to market, and mass-market adoption is a lot slower too. The much vaunted AI discoveries to solve climate change or the energy crisis probably won't meaningfully impact the economy this decade. AI will speed up R&D, but not enough to accelerate growth any time soon.

The Impact on Jobs

AI doomers believe that AI will cause catastrophic job losses that will tank the consumer economy, killing any prospect of economic growth. Tech firms have stopped hiring in recent years, but that's a trend which predates the AI boom. Technology is a mature industry, and prior to the AI bubble, big tech firms were under pressure to cut R&D. That led to hiring freezes and job cuts. Those constraints still persist. Entry level tech firms are increasingly hard to find in the west, because such roles have been offshored for cost reasons. AI has led to a resurgence in technology R&D, but mostly in the form of new data centres rather than new development jobs. The data centre bubble is a temporary phenomenon that will deflate in time, with knock-on effects for the wider economy. Large scale tech hiring isn't coming back though, but more due to the end of the SaaS boom, rather than because of AI.

The rise of SaaS software and cloud computing transformed the technology, sparking a decade-long wave of digital transformation that peaked during the pandemic. Corporate business processes were digitised and shifted online. The rise of digital marketing is emblematic of this. Over the past 10 years, all marketing has become digital. Yet, that transformation process has come to a close. As a result, the pace of net new technology adoption has slowed. For many software firms, AI is an opportunity to recapture the boom times. It allows digital processes to expand outside of pre-designed back office processes.

In time, the fusion of AI and robotics may unlock a truly transformational AI revolution. Automomous robots would be the most disruptive use for the technology, but are still a long way off. In the interim, the technology is being gradually optimised to supplement entry level white collar roles. That makes it massively beneficial to big tech firms, but of limited use to anyone else. Eventually, we may see it expand into the real world. Although, as we've seen with self-driving cars, truly intelligent AI is easy to predict but hard to master. The timeline for a truly autonomous AI is far from certain.

Salesforce Expands Agentforce Capabilities

Following a series of security breaches, Salesforce are under pressure from investors. Shareholders want to see Agentforce delivering value to customers, given increased competition from OpenAI and others. The Winter 26 release vastly expands the SDR and Lead Generation capabilities of Agentforce, introducing account research agents and autonomous meeting booking. It also introduces a wave of content creation capabilities to Marketing Cloud.

Webinar: Driving Demand Operations at Scale 

Scalability will be critical to the success of your demand operations as you plan for 2026 and beyond. If you missed CRMT Digital's latest MOps Unlocked webinar then catch the recording, where they were joined by leading marketers Wilhelm Frank from Lenovo and Mark Wheeler from Storyblok to uncover insights for demand operations success.

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A Marketing Operations Roadmap is an essential strategic tool that aligns marketing priorities with your overall business goals. Any roadmap begins with understanding where your marketing organisation is right now, and then defining where you want to get to. This free Marketing Operations Roadmap Matrix from CRMT Digital allows you to do exactly that.