As AI agents evolve from experimental chatbots into always-on autonomous systems, industry leaders say GPU access and cloud pricing are becoming the biggest barriers to scale.
AI agents are entering a new phase. The next wave is no longer centered on chatbots that wait for prompts, but on autonomous agents that operate continuously in the background — posting content, managing workflows, monitoring data, trading assets, and executing tasks with minimal human input.
That shift, while unlocking new creative and commercial possibilities, is also exposing a growing infrastructure constraint: sustained access to affordable GPU compute.
According to Gaurav Sharma, CEO of io.net, one of the world’s largest decentralized GPU networks, the rise of agent-based platforms such as OpenClaw signals where the industry is heading — and where it may run into limits.
“AI agent platforms like OpenClaw point to where the industry is heading next: not chatbots that wait for prompts, but autonomous systems that run constantly, post content, manage workflows, trade, monitor data, and operate in the background on their own. That unlocks huge creative and commercial potential for startups and individual builders, but it also creates a growing infrastructure problem. Every always-on agent needs sustained GPU compute, and right now that power is overwhelmingly controlled by a small group of companies – Amazon, Microsoft and Google. As agents move from experiments into real products, the biggest constraint is cost. Running persistent AI systems is expensive, and today’s cloud pricing models make always-on AI inaccessible for most startups and developers at scale.”
From prompts to persistence
The distinction Sharma draws reflects a broader industry transition. Early generative AI products were episodic — users interacted, models responded, compute spun down. Autonomous agents invert that model. They are designed to stay active, react to events, and operate continuously.
This persistence fundamentally changes infrastructure requirements. Instead of burst usage, developers now need long-running GPU workloads that resemble enterprise services more than experimental tools.
For startups, this creates a mismatch between ambition and affordability.
Cloud concentration becomes a constraint for AI agents
Today, most high-performance GPU capacity is concentrated among a handful of hyperscalers: Amazon, Microsoft, and Google. While these platforms offer reliability and scale, they also set pricing structures optimized for large enterprises rather than small teams running persistent AI systems.
For always-on agents, costs compound quickly:
- GPUs cannot be easily idled
- Memory and storage remain allocated
- Networking and monitoring run continuously
As Sharma notes, this makes always-on AI economically inaccessible for many startups, even when the underlying technology is ready.
Why decentralized compute is resurfacing
The infrastructure challenge is reviving interest in alternative compute models, including decentralized GPU networks that aggregate unused or underutilized hardware across regions.
Platforms like io.net position themselves as a complementary layer to hyperscalers, offering:
- Lower-cost GPU access
- Flexible provisioning
- Reduced vendor lock-in
For agent-based ecosystems such as OpenClaw, which integrate multiple compute providers, this model can widen participation by lowering entry barriers for builders.
Implications for the AI startup ecosystem
The tension between innovation and infrastructure has direct consequences:
- Fewer experiments make it to production
- Startups optimize for short-lived demos instead of durable products
- AI development becomes increasingly centralized
If persistent agents become the dominant interface for AI agents— as many industry observers expect — compute accessibility will shape who can compete.
A defining question for the next phase of AI
The success of autonomous AI agents will depend not just on model capability, but on whether the underlying infrastructure can support continuous operation at sustainable costs.
As Sharma’s remarks suggest, the next bottleneck in AI agents may not be algorithms, but economics. How the industry addresses GPU concentration and pricing could determine whether the agentic future is broadly accessible — or limited to a few well-capitalized players.
For now, the rise of always-on agents is forcing a long-overdue conversation about who controls compute, how it is priced, and who gets to build the next generation of AI-powered systems.
