History is full of belief-based money: shells, beads, pelts. They all went to zero when consensus faded.
Gold and businesses retain value because of what they do.
Bitcoin does nothing outside its network. Its floor is zero.
Not if. When.
Another argument: “It’s scarce and costly to mine, so it must have value.”
But cost ≠ value. You can burn energy or money to produce anything. Without demand grounded in utility, scarcity doesn’t set a floor.
Gold’s scarcity is valuable because it’s paired with utility. Bitcoin’s is not.
But technology never stands still. Stablecoins and other rails already move value faster, cheaper, and with less volatility.
If tech is the anchor, Bitcoin has already been leapfrogged.
No intrinsic value = no value.
The most common claim: “Bitcoin is digital gold.”
But gold’s value doesn’t come from belief. It comes from utility: conductivity, durability, jewelry, aerospace, medicine.
Even if no one used gold as money, those uses remain.
If belief in Bitcoin fades, nothing remains.
Hospitality doesn’t need more tools. It needs outcomes.
At Jurny, we believe operations should run themselves ,so hosts and hoteliers can focus on what really matters: the guest experience.
👉 Ready to experience it for yourself? Book a demo today at https://t.co/vLOGHwh5lY
When you do that, AI stops being a demo and starts moving P&L.
And yes, there’s still massive room for improvement. As model capabilities scale, results grow exponentially.
Until we completely restructured our organization around AI, progress was limited.
What changed?
✔ Mapped every department’s workflows — to identify automation-ready steps
✔ Rewrote SOPs — to remove ambiguity and standardize processes for AI
✔ Structured knowledge — so agents have clean, retrievable context
✔ Defined actions — so agents know what they’re allowed to execute
Most pilots fail because companies treat AI like another app, not a company-wide redesign.
This isn’t a feature. It’s an operating model shift.
We saw it first-hand.
That’s the difference between “AI doesn’t work” and “AI just handled 80% of my support volume.”
The equivalent of years of human training — unlocked instantly with the right infrastructure. 🤯
After restructuring the knowledge base, refining prompts, building proper flows, and layering the right agentic design…
That same model now resolves nearly 80% of tickets fully autonomously.
Across hundreds of real-world cases
Poor prompting, poor context, poor data = poor results.
I’ve been guilty of the same mistakes.
Here’s a real example from our helpdesk system at @jurnyinc:
⚙️ Same exact LLM
📚 Same access to the knowledge base
🏗️ Different infrastructure
“AI doesn’t work for me.”
“I’ve tried it, it’s not that good.”
“It’s not ready yet.”
I hear this all the time. But the issue isn’t the model.
It’s how you use it. 🧵