Pulkit @Pulkit05_ - Twitter Profile

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4 months ago

Something interesting is happening with AI right now that most people aren't paying attention to we're moving from "ask and wait" models to agents that just... run in the background, all the time → posting content, managing workflows, monitoring markets, executing trades, basically doing stuff autonomously while you're doing other things. Saw this shift really clearly with platforms like OpenClaw, these aren't demos anymore they're shipping products where agents operate independently with minimal human input and that changes everything from an infrastructure perspective. The thing nobody talks about is that the compute model for this is completely different than what cloud infrastructure is built for. • Chatbots have sporadic usage → you ask, it responds, GPU idles, you pay per request, costs are predictable, it works • always on agents → can't shut down, GPUs running 24/7, memory stays allocated, storage is always active, networking never stops same hardware, but the economics are wildly different when you're running persistent workloads at scale and this is where it gets messy for builders imo. Most high performance GPU capacity right now sits with three cloud providers - AWS, Azure, GCP, their pricing makes sense for enterprise ML training runs, big batch jobs, that kind of stuff but it wasn't designed for a startup running 10 agents continuously, 24/7 for months on end. I keep seeing the same pattern: dev builds a cool agent, tests it locally, works great, checks what it'll cost to run in production 24/7 and then... they realise they can't afford this, not because the tech doesn't work, it works fine but because the infrastructure economics just don't make sense at that scale and this is one of the most core reason where distributed compute models start getting really interesting instead of renting from centralized providers, what if you aggregate idle GPUs globally, like different economic model, no single point of control, turns out there's already thousands of GPUs networked across multiple countries doing exactly this. Makes running persistent agents economically viable for teams that couldn't afford traditional cloud pricing. Here's my DePIN thesis on this: if agents become the primary way we interact with AI (which seems increasingly likely), then who can afford to run them 24/7 basically determines who gets to participate in building this future; > centralized infrastructure creates high barriers > distributed infrastructure lowers them and infrastructure choices shape outcomes more than most people realize. Seeing this up close through @ionet, the shift from episodic AI to persistent agents isn’t just a product upgrade but i believe it’s an infrastructure challenge that needs a totally different approach than what worked in the chatbot era. Worth paying attention to if you're building in this space, the bottleneck isn't going to be model capability, it's going to be who can afford to keep the lights on 24/7.

Pulkit05_'s tweet photo. Something interesting is happening with AI right now that most people aren't paying attention to

we're moving from "ask and wait" models to agents that just... run in the background, all the time → posting content, managing workflows, monitoring markets, executing trades, basically doing stuff autonomously while you're doing other things.

Saw this shift really clearly with platforms like OpenClaw, these aren't demos anymore they're shipping products where agents operate independently with minimal human input and that changes everything from an infrastructure perspective.

The thing nobody talks about is that the compute model for this is completely different than what cloud infrastructure is built for.

• Chatbots have sporadic usage → you ask, it responds, GPU idles, you pay per request, costs are predictable, it works

• always on agents → can't shut down, GPUs running 24/7, memory stays allocated, storage is always active, networking never stops

same hardware, but the economics are wildly different when you're running persistent workloads at scale and this is where it gets messy for builders imo.

Most high performance GPU capacity right now sits with three cloud providers - AWS, Azure, GCP, their pricing makes sense for enterprise ML training runs, big batch jobs, that kind of stuff but it wasn't designed for a startup running 10 agents continuously, 24/7 for months on end.

I keep seeing the same pattern:
dev builds a cool agent, tests it locally, works great, checks what it'll cost to run in production 24/7 and then... they realise they can't afford this, not because the tech doesn't work, it works fine but because the infrastructure economics just don't make sense at that scale

and this is one of the most core reason where distributed compute models start getting really interesting

instead of renting from centralized providers, what if you aggregate idle GPUs globally, like different economic model, no single point of control, turns out there's already thousands of GPUs networked across multiple countries doing exactly this.

Makes running persistent agents economically viable for teams that couldn't afford traditional cloud pricing.

Here's my DePIN thesis on this:

if agents become the primary way we interact with AI (which seems increasingly likely), then who can afford to run them 24/7 basically determines who gets to participate in building this future;

> centralized infrastructure creates high barriers
> distributed infrastructure lowers them

and infrastructure choices shape outcomes more than most people realize.

Seeing this up close through @ionet, the shift from episodic AI to persistent agents isn’t just a product upgrade but i believe it’s an infrastructure challenge that needs a totally different approach than what worked in the chatbot era.

Worth paying attention to if you're building in this space, the bottleneck isn't going to be model capability, it's going to be who can afford to keep the lights on 24/7.

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