Pan

Voice is becoming the control layer for agents. ChatGPT on desktop can now listen, speak, coordinate work and direct multiple agents across your computer. That means AI is moving from “answering questions” to operating workflows. As agents take action, they will need https://t.co/mnp8YmPIKH — PAN (@Pan_Ecosystem) Jul 24, 2026

Open models are not the end of the AI stack. They are the beginning of massive agent deployment. As more companies and developers build on open models, agents will need to call external compute, run private tasks, verify outputs and pay for execution. https://t.co/CYPvOFW9kP — PAN (@Pan_Ecosystem) Jul 26, 2026

Agent autonomy is rising. Without verification, one rogue action can cascade. Production agents need intent checks and execution proofs. Safety is an infrastructure layer. — PAN (@Pan_Ecosystem) Jul 28, 2026

In preparation for what’s next, Pan new website is now live. We’ve updated the design, product pages, and onboarding flow. What do you think we should launch next? https://t.co/r8mRaALhL9 — PAN (@Pan_Ecosystem) Jul 29, 2026

Training loves centralized clusters. Agents need continuous, low-latency compute. Idle GPUs will join the supply side. PAN is building the coordination layer for verifiable agent compute. https://t.co/lspsYwKjLu — PAN (@Pan_Ecosystem) Jul 30, 2026

Every tool call and inference step can be a transaction. Agents will request, verify and pay for execution. PAN is building financial rails for the agent economy. https://t.co/IrgYnmDzIM — PAN (@Pan_Ecosystem) Aug 1, 2026

One question keeps showing up for us at PAN: if agents are going to work all day, where does that work actually run? Not the model. Not the demo. The execution behind it. https://t.co/8tr8Jp4WOU — PAN (@Pan_Ecosystem) Aug 4, 2026

Once agents start paying for tools and compute, settlement stops being optional. Someone has to prove what was requested, what ran, and who paid. That’s the part we’re building around. https://t.co/cGrVzXVv2s — PAN (@Pan_Ecosystem) Aug 6, 2026

I don’t think the next AI compute wave will look like the last one. Training was big clusters and big headlines. Agents feel different. Smaller jobs. Running constantly. Happening in more places. Messier, but probably much bigger than people expect. https://t.co/5NJBwZaBZV — PAN (@Pan_Ecosystem) Aug 7, 2026

We’re not trying to make smarter agents. We’re trying to make agent execution something you can actually verify and settle. Intent, compute, payment, result. PAN sits in that loop. — PAN (@Pan_Ecosystem) Aug 9, 2026

Official Announcement Thrilled to partner with @XAgent_official the zero-code platform empowering anyone to Speak to Build custom AI Agents. As the Demand Layer for AI Compute PAN will explore seamless compute access agent-to-agent payments and scalable infrastructure for the https://t.co/mYgOtdDTjz — PAN (@Pan_Ecosystem) Aug 10, 2026

Training compute is easy to recognize. Big clusters. Big budgets. Big announcements. Agent compute will be harder to see. Small jobs. Always running. Spread across tools, apps, and users. Paid for one action at a time. That market may be much larger than it looks. — PAN (@Pan_Ecosystem) Aug 11, 2026

Training compute is easy to spot. Big clusters. Big budgets. Big announcements. Agent compute will be harder to see. Small jobs. Always running. Spread across tools, apps, and users. Paid for one action at a time. The biggest compute market may be the one nobody sees. https://t.co/rI6uYqRSgb — PAN (@Pan_Ecosystem) Aug 13, 2026

Agents need receipts. Not screenshots. Not vague logs. A real one should show: intent → compute → verification → payment → result. PAN is building that execution record. https://t.co/i2DVVPjjDD — PAN (@Pan_Ecosystem) Aug 14, 2026

Open models do not just create more builders. They create more agent attempts. More experiments. More tool calls. More failed runs. More inference demand. More need to verify what actually happened. The open-source wave expands the execution market, not just the model market. https://t.co/Sg0KVkPqk7 — PAN (@Pan_Ecosystem) Aug 16, 2026

Agent compute is not one big bill. It is hundreds of small executions that become visible only after they add up. https://t.co/tRbmilJYPy — PAN (@Pan_Ecosystem) Aug 20, 2026

Idle GPUs aren’t compute. If developers can’t discover them, verify them, run jobs to completion, and pay for actual usage, they’re just hardware. — PAN (@Pan_Ecosystem) Aug 23, 2026

Cheap GPU time isn’t cheap when the node dies halfway through a job. Price the completed run, not the advertised hour. — PAN (@Pan_Ecosystem) Aug 25, 2026

Listing distributed GPUs is easy. Making different cards, drivers, and networks behave like one reliable product is the hard part. — PAN (@Pan_Ecosystem) Aug 26, 2026

Users don’t buy GPUs. They buy reliable runtime: job starts, job finishes, and usage is metered correctly. https://t.co/jjRbhEd3kc — PAN (@Pan_Ecosystem) Sep 1, 2026

Q3 or Q4? — PAN (@Pan_Ecosystem) Sep 4, 2026

Cheap GPU hours are not cheap compute. A medical or agent workload dies on the run that never finishes. Hourly price is a listing. Completed-job cost is the bill. Buy runtime that starts, checkpoints, and settles. Not idle silicon. — PAN (@Pan_Ecosystem) Sep 9, 2026

Community testing is live. A few are already in. More soon. https://t.co/BACymzvRRE — PAN (@Pan_Ecosystem) Sep 20, 2026