“All that glitters is not gold”, so the saying goes. All that has benefits isn’t a good investment either, and that’s been a recurring problem in assessing technology in general and network tech in particular. Many new technologies were capable of doing something well, even better, but not enough so to justify an investment that often involved ripping out un-depreciated assets and taking new risks. Operators tell me that’s where things really are with AI RAN. Of a dozen who have done real work on the topic, ten say that they are only about halfway to a business case with it. Can they get the rest of the way? Here are their issues and the potential remedies they suggest, with my own views mixed in.
The biggest problem that network operators cite is that AI RAN is too much of a moving target in both cost and benefits. “You talk about building AI into the RAN,” one technologist said, “as though there was one sort of AI and one model of RAN, and neither is true.” Another: “How much energy can you save if you’re relying on a technology that needs new power plants to run?” There is no real definition of just what’s needed to run an AI RAN model, which means both its capital cost and opex is impossible to predict accurately. The benefits AI RAN targets are only real under certain conditions of deployment, both in terms of density of cells and customers, and in terms of age of existing infrastructure. It’s also uncertain whether an advance in wireless standards to 6G could justify a shift to AI from ASICs, given that telcos don’t want 6G to require a forklift because 5G hasn’t returned on the investment in it.
How do you fix this? “The AI RAN camp needs to put its technology on a diet,” says one operator expert. “We aren’t going to run an AGI [artificial general intelligence] model at a cell site.” Most of the dozen operators told me that they believe AI RAN should perhaps be called “ML RAN” because they think the real needs of AIops could be realized with machine learning and a modest hosting requirement. That would lower both the cost of the gear and the power and cooling requirements. But, they say, all the impetus behind AI RAN is being created by chip giants like Nvidia, who don’t want a watered down model.
That gives rise to the second problem, which is AI RAN has become a theoretical on-ramp for edge computing. If you can’t lower costs with superchip AI RAN, sell AI services as edge services, the theory goes. “Who do we sell it to?” one operator asks. “Why would we win against hyperscalers who are already selling AI?” Not at the network edge, perhaps, but every cloud provider does offer an on-premises middleware tool that’s clearly a camel’s nose for any edge service tent that becomes feasible. “So because we can’t make an AIops business case for AI RAN because it’s too suppositional, we should add some other value thing in that’s even more iffy.”
Edge services at the network edge rather than in the metro, according to operators, it just too big a step. If you want extreme latency control, host on premises next to the processes under control, which is and has been the established practice. The only way to break out of that would be to lower the cost of your edge service radically, which can’t be done profitably unless you can achieve a mighty economy of scale. At a cell site? Get real. Even metro hosting could be challenging, but that’s where you have to start with edge computing. Instead of moving expensive assets out to the cell edge, you lower latency between customer and metro, something that was a goal for 5G and is expected to be one for 6G.
The third question raised comes out of this evolutionary approach. Given that AI technology is changing rapidly, and that many of its value propositions are under pressure, how do you justify a long-term investment in it? Telcos typically depreciate over a longer cycle than enterprises, and if AI is something that has to be eased into, how long could it take before we have a convincing AI answer? What’s the risk of investing in it without such an answer, especially if the final justification emerges only at the end of a long and as-yet-undefined application evolution to something like real-time, augmented reality, robots, and so forth?
This issue can be seen as a consequence of the other two. Time, they say, heals all wounds, and while that’s nonsense at one level, it is true that many if not all the other issues could be resolved by the evolution of network-dependent applications, particularly those elusive real-world-real-time applications I’ve blogged about in the past. Given that, you could argue that AI RAN benefits are inevitable, so why not get started? The problem is that the realization of the benefits and the evolution of the best-available technology could render current investment obsolete. How then can you justify getting started?
The only possible answer to this one is to work hard, right now, to frame the technical requirements for those future applications, and assess AI/ML directions to align the technology with the needs. That could redirect early investment in AI RAN along lines more likely to create an optimum return on investment for network operators. The problem with this lies in the inherent opportunism of the vendors in the AI space who would almost surely dominate that hard work. We can see today that Nvidia’s focus is on validating its own market position, which tends to create an emphasis on things like large-scale robotics, which would require considerable work and time to integrate into enterprise operations and people’s lives. What these require is not the question that early evolutionary steps would need to answer. It’s not the final, most exciting destination, but the first steps on the route, that really matter.
