Almost every input a company buys has a visible market. Compute has clearing prices across a handful of clouds. Capital has rates. Even attention and risk trade on exchanges with real-time signals. But human skill, the input companies say matters most, has none of that. There's no ticker for a given capability, no live read on whether it's scarce this quarter, no transparent benchmark a worker can check to know their own worth. That opacity is starting to look less like a permanent condition and more like a gap waiting to be closed, and closing it would reset how workforce value gets set.
It's a promising concept for Wael Sabra, the founder and CEO of SiiRA. The global talent platform was built to help organizations source and hire vetted specialists from anywhere in the world under the premise that talent should be as accessible as cloud infrastructure. This puts Sabra in the business of watching where specific skills exist, how they move, and what they're actually worth as demand shifts.
"There's a market for everything, but there is no true market where skill sets are priced and traded," he points out. In Sabra's view, it's an absence that the next phase of AI-driven hiring is likely to address.
The missing exchange for human capability
Sabra frames the problem by pointing at what talent lacks relative to every other input a company sources. The comparison he starts from is infrastructure. "There's an AWS and a Google Cloud and a Microsoft for hardware, but there isn't a Google or Microsoft or an AWS for talent," he says. SiiRA set out to fill the gap with a platform that lets a company reach talent anywhere the same way it reaches compute.
The deeper issue underneath is measurement. Talent, in Sabra's telling, is one of the least digitized and least transparent markets a company operates in, which means most organizations lack any real visibility into where specialized skills sit or what they should cost. It's the kind of blind spot that persists only because no one has built the instrument to see through it yet.
When demand spikes, old benchmarks break
The clearest evidence that traditional pricing fails is what happens when demand for a specific capability accelerates faster than supply. Sabra's example is concrete and current. "Right now one of the areas that's experiencing growth is Rust development, and a lot of that is because of hardware or embedded systems," he says, describing a language whose speed on inference hardware has driven a sudden surge in demand.
The supply hasn't caught up, and that dislocation exposes how crude standard compensation benchmarks are. "Maybe a few years ago there were hundreds of those developers around the world, and now all of a sudden the demand is in the thousands or the tens of thousands," Sabra explains. The pricing consequence is immediate. A backend engineer can't be slotted into a standard band once a rare skill enters the picture. "If they know Rust, immediately their pay is going to be a lot more." The old proxies of geography and title simply miss it.
What he envisions isn't just measurement, but exchange. "As AI and humans get more intertwined in how they're working together, I really see the development of a global market of skill sets becoming something that is not just for pricing, but also for trading," Sabra says. He references prediction markets like Polymarket and Kalshi, which make it easy for individuals to trade on future events.
The mechanism he describes is transparency that cuts both ways: if companies are placing more value on a given capability, that signal should be open to everyone who holds the skill. "If there are a lot of companies paying more for Salesforce development, for example, that pay should be transparent to everybody who has that skill set, so that they know how competitive they are in the market."
Prices that move in real time
The feature that makes it a market rather than a directory is that the numbers move. In Sabra's model, compensation for a capability wouldn't sit as a static band, but fluctuate as conditions change the same way any traded price does. He describes pay that adjusts "as more supply comes in within that skill set," so a shortage that commands a premium today compresses as more workers acquire the capability and the balance shifts.
That dynamic reframes what both sides of the market are actually trading on. For a worker, it turns a skill into a position whose value they can track and act on, learning a scarce capability when the signal says it's richly priced. For a company, it replaces guesswork with a live read on what a team actually costs to build. Sabra is candid that the shape of this is far from settled, but he's clear that the direction of travel points toward talent being measured, priced, and eventually traded with a precision the market has never had.
Why now's the time for the shift
What makes this more than a thought experiment, in Sabra's framing, is that the same forces raising the value of specific skills are also generating the data to price them. As companies hunt for narrow capabilities that AI can't yet supply on its own, the demand signals sharpen and platforms that sit across global talent flows can begin to read them. The Rust example is early proof: a capability that barely registered a few years ago became a priced premium almost overnight, and someone was positioned to observe it happen.
His larger point is that talent has simply lagged every other market in becoming legible, but that lag is closing. "I see the digitization of talent globally as a huge gap, an area that hasn't been measured or looked at before the way futures markets are," Sabra says. The companies and workers who benefit will be the ones who treat skills as the unit of value that gets priced, rather than clinging to geography and tenure as stand-ins for worth that no longer track what the market will actually pay.
