The curtain is falling on the era of ‘crypto’ price speculation. Perhaps now speculation’s infinitely more talented understudy (utility) will finally be allowed its moment in the spotlight.
The current U.S. administration has given the crypto industry all it ever wanted and then some but token prices keep falling. All the regulatory advances appear already baked-in and crypto boosters have no answer for what might restore token prices’ upward trajectory.
Many early blockchain backers promoted its capacity to free oneself from legacy financial systems, but the exact opposite has occurred. Tradfi institutions are adopting blockchain’s utility-based functionality, like cost-effective stablecoin payments, tokenization of real-world assets and more.
As a supporter of a network focused on functionality (BSV) that Satoshi would recognize as far more reflective of his original plan than BTC, I see this moment as a welcome mat being rolled out for platforms that offer real solutions, not false hopes of instant riches.
The ‘number go up’ crowd complains that AI has stolen the tech narrative, but utilitarians see ways for these two technologies to act in concert, including empowering agentic AI by granting access to payment networks that never sleep. And a blockchain that can continually scale to meet future needs can provide similarly unlimited data-handling abilities.
From the start, blockchain tech was intended to be used, not limited to a means of producing tokens to be hoarded under your mattress. The community lost sight of this intention, but sanity (and focus) are returning. The time to build is now.
What aspect of blockchain utility are you most excited about?
Everyone’s talking about the SpaceX IPO and fair play to Elon for setting new records. But I’ve never felt the urge to go public because I value true independence when making business decisions.
The right decisions for your business can often be costly in the short term, something investors that CEOs will never meet hate because the quarterly reports look bad and the share price falls. But short-term decisions can lead to more dramatic devaluations down the road, a case of pennywise and pound-foolish. Like the Rush song says, I will choose free will.
Just for fun!
🏓 TERAPONG: every bounce is literally money, baby — an instant, zero confirmation 1sat transaction.
Smack the ball off a paddle, wall, or shield and ka-ching — a real ~1-sat #BSV tx hits mainnet. Watch your sats go brrr, then blow 'em on powerups: big paddle, multi-ball, slow-mo, shield. 🕹️⚡ Built on @Replit.
(Built in wallet for non-BSV players to play. @Andrew_Blumson@KevinBlumson@raymmar@MannyBernabe)
https://t.co/iEFEKWTgfO
I am working toward releasing an open-source AI agent project by the end of this year.
The aim is not to build another monolithic system competing in the usual “OpenAI versus Claude versus Gemini” framing. That model assumes that intelligence should be concentrated into a small number of enormous general-purpose systems operated from large data centres. I think there is another path.
The project will explore an open environment of many specialized agents: individually trained, task-specific, economically accountable systems that can be created, improved, and owned by different people. Rather than one model attempting to do everything, the architecture is based on distributed knowledge, specialization, reputation, and competition.
In this model, agents may be trained for narrow domains: law, medicine, engineering, accounting, software, research, education, logistics, personal assistance, verification, translation, and many others. Individuals or small teams could train agents around their own knowledge and experience. Those agents could then be paid for useful work, develop reputations, and compete on accuracy, reliability, cost, and trustworthiness.
The goal is to create a market for specialized intelligence rather than a single centralized intelligence provider.
This is about open-source AI as an ecosystem: many agents, many owners, many areas of expertise, and many routes to improvement. It is not intended to replace human knowledge with one giant black box. It is intended to let human knowledge be embedded, trained, tested, traded, and extended through specialized systems.
The project will invite developers, researchers, domain experts, and individuals who want to build agents around real expertise. The important question is not which large company owns the most powerful model. The important question is how millions of smaller, specialized systems can cooperate, compete, verify one another, and create value in an open environment.
That is the direction I intend to pursue.