By James Pierog, Founder and CEO of Glimpse
For much of modern finance, information advantage belonged to those with access. The fastest terminals, the best research departments, the closest relationships and the deepest pools of proprietary data shaped who saw the market clearly and who did not. The evolution of finance has, in many ways, been the story of narrowing that gap.
But the next evolution of market intelligence may not be another closed system available only to those who can afford it. It will be an open forecasting layer: public, visual and constantly updating, where market expectations can be seen by anyone rather than locked inside proprietary infrastructure.
Access is no longer the only issue. Investors are not short of information, they are overwhelmed by it. The irony is that finance now has too much commentary in public and too many ‘high-quality’ signals locked away in expensive private systems.
Every asset now sits inside a cloud of commentary: think about analysts producing forecasts, self-proclaimed influencers producing narratives, economists producing models and traders producing charts. AI tools produce summaries of all of them but the central question remains stubbornly difficult: what does the market actually believe is likely to happen next?
That question should not only be available to institutions with expensive terminals or privileged data access. If markets can generate useful probability signals, those signals should be visible, legible and usable to a much wider audience.
This is the question prediction markets are beginning to answer. The phrase prediction markets still carries baggage. Many people associate them with political betting, sports-adjacent speculation or headline-driven binary contracts. That perception is understandable. Platforms such as Kalshi and Polymarket have popularised the category through event markets, particularly around politics, culture and current affairs.
But it would be a mistake to assume that this is the full potential of the model. The next phase of the category should be less about asking people to speculate on isolated headlines and more about building continuous forecasting markets that produce useful public data.
Finance does not need more entertainment prediction markets. It needs open forecasting infrastructure: markets designed not merely to attract trades, but to produce signals that others can read, use and interrogate.
A market asking whether one discrete event will happen is not the same as a market designed to forecast a range of financial outcomes over time. The former often resolves into spectacle and the latter can become a form of information.
When designed well, prediction markets do something elegant: they force private beliefs into public probabilities. That public quality matters because the most valuable output of a well-designed forecasting market is not only the trade itself, but the data it produces. Once visible, that data becomes a shared reference point for traders, analysts, investors and ordinary market observers.
A participant who thinks an outcome is mispriced can trade. If others agree, the price moves. And if new information appears, the market updates. Over time, the price becomes more than just a number. It becomes a condensed expression of distributed knowledge: the kind of signal that could sit at the centre of an open forecasting terminal.
You might think this is some romantic idea but it’s actually central to how markets work already. Think about it: equity prices aggregate expectations about future cash flows. Yield curves aggregate expectations about interest rates, growth and inflation. Options markets aggregate expectations about volatility. Prediction markets apply the same logic to questions that traditional markets often leave scattered across reports, speeches and social media.
A terminal, at its best, helps users see what the market is saying. Prediction markets can extend that function beyond historical prices and analyst feeds into live probabilities about future outcomes.
The lesson for finance is that forecasting improves when it is made explicit, probabilistic and accountable.
Most institutional forecasting still fails at least one of those tests. Corporate forecasts are buried in spreadsheets and research forecasts are often revised without memory. Market commentary is abundant but rarely scored, and even when forecasts are directionally useful, they are frequently expressed in language that avoids precision: constructive, cautious, risk-on, soft landing, higher for longer.
Markets on the other hand are far less forgiving: a price is a probability with consequences. This does not mean prediction markets will replace analysts, economists or portfolio managers any time soon. But I see them becoming another layer in the financial information stack: an open layer of probability data sitting alongside credit spreads, implied volatility, futures curves and research.
Just as investors look at credit spreads, implied volatility or futures curves, they may increasingly look at market-generated probabilities for specific events, policy paths or asset-price ranges.
This could change how investors think about uncertainty. Instead of treating forecasts as static claims, they can treat them as living distributions. Instead of asking what one expert thinks, they can ask how a financially incentivised crowd is pricing the range of possibilities. Instead of waiting for formal revisions, they can watch expectations update in real time.
The opportunity is particularly relevant for assets where narrative and volatility dominate. Bitcoin is an obvious example. It is global, liquid, emotionally charged and subject to constant prediction. Everyone has a view, but few of those views are measured. A forecasting market can help turn that noise into a signal by asking participants to put capital behind their beliefs.
If that signal is public, the value extends beyond the people trading. It becomes something others can watch, cite and interpret; a live view of market expectations rather than another private research product.
This is why we are building Glimpse around Bitcoin forecasting first. Not because Bitcoin is the only market that matters, but because it is one of the clearest examples of an asset where opinion is abundant, accountable forecasting is scarce, and public probability data could become genuinely useful.
The next stage of prediction markets will not be defined by who creates the most markets or attracts the most headlines. It will be defined by who produces the signal that serious people trust. That requires regulatory clarity, careful design and markets that are built for information aggregation.
The winning model will look less like a casino and more like public market infrastructure: accessible, transparent and designed to make uncertainty easier to see.
The question is not whether prediction markets are perfect because they are not. The question is whether finance can afford to ignore a new mechanism for aggregating belief, measuring conviction and turning uncertainty into public, real-time probability data.
If the first era of financial information was defined by closed terminals, the next will be defined by open forecasting markets.
Prediction markets could create finance’s first open forecasting terminal
By James Pierog, Founder and CEO of Glimpse
For much of modern finance, information advantage belonged to those with access. The fastest terminals, the best research departments, the closest relationships and the deepest pools of proprietary data shaped who saw the market clearly and who did not. The evolution of finance has, in many ways, been the story of narrowing that gap.
But the next evolution of market intelligence may not be another closed system available only to those who can afford it. It will be an open forecasting layer: public, visual and constantly updating, where market expectations can be seen by anyone rather than locked inside proprietary infrastructure.
Access is no longer the only issue. Investors are not short of information, they are overwhelmed by it. The irony is that finance now has too much commentary in public and too many ‘high-quality’ signals locked away in expensive private systems.
Every asset now sits inside a cloud of commentary: think about analysts producing forecasts, self-proclaimed influencers producing narratives, economists producing models and traders producing charts. AI tools produce summaries of all of them but the central question remains stubbornly difficult: what does the market actually believe is likely to happen next?
That question should not only be available to institutions with expensive terminals or privileged data access. If markets can generate useful probability signals, those signals should be visible, legible and usable to a much wider audience.
This is the question prediction markets are beginning to answer. The phrase prediction markets still carries baggage. Many people associate them with political betting, sports-adjacent speculation or headline-driven binary contracts. That perception is understandable. Platforms such as Kalshi and Polymarket have popularised the category through event markets, particularly around politics, culture and current affairs.
But it would be a mistake to assume that this is the full potential of the model. The next phase of the category should be less about asking people to speculate on isolated headlines and more about building continuous forecasting markets that produce useful public data.
Finance does not need more entertainment prediction markets. It needs open forecasting infrastructure: markets designed not merely to attract trades, but to produce signals that others can read, use and interrogate.
A market asking whether one discrete event will happen is not the same as a market designed to forecast a range of financial outcomes over time. The former often resolves into spectacle and the latter can become a form of information.
When designed well, prediction markets do something elegant: they force private beliefs into public probabilities. That public quality matters because the most valuable output of a well-designed forecasting market is not only the trade itself, but the data it produces. Once visible, that data becomes a shared reference point for traders, analysts, investors and ordinary market observers.
A participant who thinks an outcome is mispriced can trade. If others agree, the price moves. And if new information appears, the market updates. Over time, the price becomes more than just a number. It becomes a condensed expression of distributed knowledge: the kind of signal that could sit at the centre of an open forecasting terminal.
You might think this is some romantic idea but it’s actually central to how markets work already. Think about it: equity prices aggregate expectations about future cash flows. Yield curves aggregate expectations about interest rates, growth and inflation. Options markets aggregate expectations about volatility. Prediction markets apply the same logic to questions that traditional markets often leave scattered across reports, speeches and social media.
A terminal, at its best, helps users see what the market is saying. Prediction markets can extend that function beyond historical prices and analyst feeds into live probabilities about future outcomes.
The lesson for finance is that forecasting improves when it is made explicit, probabilistic and accountable.
Most institutional forecasting still fails at least one of those tests. Corporate forecasts are buried in spreadsheets and research forecasts are often revised without memory. Market commentary is abundant but rarely scored, and even when forecasts are directionally useful, they are frequently expressed in language that avoids precision: constructive, cautious, risk-on, soft landing, higher for longer.
Markets on the other hand are far less forgiving: a price is a probability with consequences. This does not mean prediction markets will replace analysts, economists or portfolio managers any time soon. But I see them becoming another layer in the financial information stack: an open layer of probability data sitting alongside credit spreads, implied volatility, futures curves and research.
Just as investors look at credit spreads, implied volatility or futures curves, they may increasingly look at market-generated probabilities for specific events, policy paths or asset-price ranges.
This could change how investors think about uncertainty. Instead of treating forecasts as static claims, they can treat them as living distributions. Instead of asking what one expert thinks, they can ask how a financially incentivised crowd is pricing the range of possibilities. Instead of waiting for formal revisions, they can watch expectations update in real time.
The opportunity is particularly relevant for assets where narrative and volatility dominate. Bitcoin is an obvious example. It is global, liquid, emotionally charged and subject to constant prediction. Everyone has a view, but few of those views are measured. A forecasting market can help turn that noise into a signal by asking participants to put capital behind their beliefs.
If that signal is public, the value extends beyond the people trading. It becomes something others can watch, cite and interpret; a live view of market expectations rather than another private research product.
This is why we are building Glimpse around Bitcoin forecasting first. Not because Bitcoin is the only market that matters, but because it is one of the clearest examples of an asset where opinion is abundant, accountable forecasting is scarce, and public probability data could become genuinely useful.
The next stage of prediction markets will not be defined by who creates the most markets or attracts the most headlines. It will be defined by who produces the signal that serious people trust. That requires regulatory clarity, careful design and markets that are built for information aggregation.
The winning model will look less like a casino and more like public market infrastructure: accessible, transparent and designed to make uncertainty easier to see.
The question is not whether prediction markets are perfect because they are not. The question is whether finance can afford to ignore a new mechanism for aggregating belief, measuring conviction and turning uncertainty into public, real-time probability data.
If the first era of financial information was defined by closed terminals, the next will be defined by open forecasting markets.