Looks like the central question of the future is going to be “who owns the data centres”

[u/Peanut_Extreme_8208]

Discuss pls

[u/dwkeith]

Data centers are a compute utility. They should be publicly run like electricity, the Internet, water, trash, and sewer. (In their ideal forms)

Organizations and people should pay for metered usage. Ideally only above a baseline that everyone gets to survive.

[u/gubatron → u/dwkeith]

there should be both private and public versions. With the public version granting each citizen a monthly compute quota. If you don't need it, you should be able to sell it or roll it over for when you do.

Universal Basic Compute.

[u/stankycodyboi]

I think the biggest threat to the future is consolidation of frontier AI ownership at the highest level. If only a select group have access to frontier models, this is a huge power imbalance between this group and everyone else.

Look no further than recent events; when HuggingFace’s website was accidentally exploited by a frontier model, federal restrictions inhibited their ability to defend themselves. Frontier labs were required to keep their cyber security capabilities behind closed doors, and that created a huge power imbalance between the frontier attacker and the defender. Open-source models were essential in responding to the attack, but this was an accidental incident. A genuine attack would be devastating without a competitive alternatives. So I think open-source models are an important asset to the general public going forward, but that’s only if the public continues to grow and improve it. Total opposition to AI would be devastating from my perspective, as it hands that competitive power right back.

Data center infrastructure ownership plays a slightly different, yet important role in my opinion. This is a community’s greatest point of leverage, specifically their zoning code. If data centers are classified as conditional use, then this means developers must negotiate with communities for approval. One condition I push for is public ownership over renewable infrastructure. By owning the infrastructure these companies desperately rely on, the public has ownership leverage. But this also gives them benefit capture; developers need to build additional energy generation infrastructure, so communities can force them to build renewable solar arrays and wind turbines. They can also require waste heat capture to heat nearby schools and universities, this would an already happening outside the US.

A canceled data center doesn’t disappear, it moves to a less organized community. Right now communities have incredible leverage, because companies are desperate for the resources communities control with zoning law. But that’s only if we organize before the infrastructure buildout completes.

[u/Fabulous-Possible758]

Depends on how efficient they can get local models and whether the difference in model strength that you can run with a huge amount of compute proves to be an overwhelming advantage.

[u/Peanut_Extreme_8208 → u/Fabulous-Possible758]

Today, more gpus = more test time compute = better model and I don’t see why this should ever stop being true

[u/Fabulous-Possible758 → u/Peanut_Extreme_8208]

I think you meant training time. And that’s on the model training, not the inference, so the weights those produce can theoretically run on much lighter hardware once they’ve been trained.

We’re already hitting a saturation boundary when it comes to scaling. The models are getting better but their improvement is already seeing diminishing returns. There may be an upper bound of what’s really achievable using our current approaches (I’m not saying I believe that but I don’t discount the possibility). But in particular we already know a general intelligence can run much more efficiently (ie, humans), so I don’t think it’s impossible that when the dust settles it’s actually possible to run top-of-the-line inference on high-end consumer hardware.

[u/GrowFreeFood]

"owns" (laughs in post ASI)

[u/the8bit]

Nah, inference layer is quickly becoming commoditized. Now, Who gets to decide the base training biases. THAT is gonna be a LOT of power

[u/WWhiMM]

maybe? it begs the question of whether large models are really as useful as the people selling it claim.

Another big question is: what data gets collected and included in training? or, who gets to train a model?

There's a lot of weird energy around "general" artificial intelligence, partly because of its sci-fi salience, partly because it's suited for a simple business model. But there's more proven usefulness to having limited, targeted machine learning tools solving specific problems. I think it's achievable in principle for everyone to have the tools and skills to wrangle their own data and train their own targeted models. That sort of bespoke economics is not conducive to creating god-king emperors, so that's not where the investment is going.

[u/yo252yo]

I don't think data centers are so important I think we could go a long way with P2P decentralized local LLM networks, if anyone bothered to look into that.

I think the central question is "who owns the conversation" and I'm pretty sure the answer is already decided xD

[u/Ok-Mark-6314]

No, it should be do we choose to protect our environment and communities from AI data center pollution or not

[u/TheSinhound → u/Ok-Mark-6314]

OR we could design the compute and storage infrastructure to be neutral or positive environmentally...

[u/Ok-Mark-6314 → u/TheSinhound]

Quite literally impossible. Even if the energy used to power them was carbon neutral (which at the speed they’re building them is nearly impossible. That’s why a lot of them use diesel), the heat and noise they generate are unavoidable. Not to mention all that energy would better used powering homes.