Original Title: Two Kites Dancing In A Hurricane

Original Author: 0xsmac

Original translation: SpecialistXBT

Editor's Note: This article offers a sharp and insightful examination of the current boom in prediction markets. The author boldly argues that today's prediction markets are falling into a "local optimum" trap similar to that of BlackBerry and Yahoo in their decline. While the dominant binary options model has generated significant traffic in the short term, it is constrained by structural issues such as poor liquidity and inefficient capital utilization. The article proposes a transition of prediction markets toward a "perpetual futures contract" model, offering constructive and in-depth thinking on how to realize a true "market of everything."

Why do companies find themselves pursuing the wrong goals? Can we fix market forecasts before it's too late?

"Success is like a strong liquor, intoxicating. It is not easy to handle the fame and flattery that come with it. It can corrupt your mind, making you believe that everyone around you is in awe of you, that everyone desires you, and that everyone's thoughts revolve around you." — Ajith Kumar

"The roar of the crowd has always been the most beautiful music." — Vin Scully

Early success is intoxicating. Especially when everyone tells you that you won't succeed, the feeling becomes even stronger. Screw the haters, you're right and they're wrong!

But early success can hide a unique danger: you might be winning the wrong rewards. Although we often joke about "playing stupid games and winning stupid prizes," in reality, the games we participate in are often evolving in real time. Therefore, the very factors that helped you win in the first stage might become obstacles to winning greater rewards once the game enters its mature phase.

One manifestation of such an outcome is that a company stumbles into a "local optimum" without realizing it. The feeling of winning is so sweet that it not only causes you to lose your sense of direction, but also blocks self-awareness, preventing you from recognizing the true situation you're in.

In many cases, this may just be an illusion, a false appearance supported by external factors (for example, economic prosperity leading to an abundance of disposable income for consumers). Alternatively, the product or service you have built may indeed function well, but only within a specific scope or under certain conditions, and it cannot be scaled to a broader market.

The core conflict here is that in order to pursue the ultimate prize (i.e., the global optimum), you need to come down from your current peak. This requires great humility. It means making difficult decisions: abandoning a core feature, completely rearchitecting the tech stack, or personally dismantling a pattern you once believed to be effective. What makes all of this even more challenging is...

Most of the time, you have to make this decision precisely when people (mainly investors and the media) are all telling you "how great you are." Many who previously claimed you were wrong are now scrambling to validate your success. This is an extremely dangerous situation because it breeds complacency exactly when you need to make bold and radical changes.

This is precisely the situation prediction markets find themselves in today. In their current form, they will never achieve mass-market adoption. I won't waste ink here debating whether they have already reached this status (after all, there is a huge gap between knowing something exists and actually having a demand to use it). You may disagree with this premise and are now preparing to close this page or read the rest with resentment. That is your right. But I will reiterate why this model is broken today and explain what I believe these platforms should look like.

I don't want to sound too much like someone from the tech world, and I won't reiterate the "Innovation Dilemma," but classic examples of this phenomenon are certainly Kodak and Blockbuster. These companies (and many others) achieved tremendous success, which created an inertia that resisted change. We all know how their stories ended, but simply shrugging our shoulders and saying "we should have done better" is not constructive. So, exactly what led to these outcomes? Do we see similar signs in today's prediction markets?

Sometimes, the obstacles lie at the technical level. Startups often build their products in a specific, subjective way that may work well in the initial stages (and achieving this as a startup is already overcoming significant challenges!). However, this approach quickly becomes a rigid architectural constraint limiting future development. Continuing to scale after an initial surge or making adjustments to the product design often means challenging core components that seem to be working effectively. Naturally, people tend to address these issues through incremental fixes, but this quickly leads to a product that becomes a patchwork of solutions. Moreover, this only delays the inevitable confrontation with a harsh truth: what is truly needed is a complete rebuild or a fundamental rethinking of the product.

This situation has occurred before when early social networks hit performance ceilings. Friendster, a pioneer in the 2002 social networking scene, enabled millions of users to connect with "friends of friends" online. However, trouble arose when a specific feature (viewing friends within a "three-degree connection" radius) caused the platform to crash under the computational load of exponentially increasing connections.

The team refused to scale back on this feature and instead focused on new ideas and flashy partnerships, even as existing users threatened to migrate to MySpace. Friendster reached a local peak in popularity but could not move beyond it due to flaws in its core architecture, which the team refused to acknowledge, deconstruct, and fix. (By the way, MySpace later also fell into its own kind of "local optimum" trap: it was built around a unique user experience—highly customizable user profiles—and focused on music and pop culture communities. The platform was primarily ad-driven and eventually became overly dependent on its ad portal model, while Facebook emerged at that time with a cleaner, faster, and "real-identity" based network. Facebook attracted some of MySpace's early users, but undoubtedly drew in the next much larger wave of social media users.)

It is not surprising that such behaviors persist. We are all human. Achieving some form of apparent success, especially as a startup where failure rates are extremely high, naturally leads to an inflated sense of self. Founders and investors begin to believe in the exaggerated achievements they have promoted, doubling down on the formula that brought them this far, even as warning signs grow increasingly evident. People often ignore new information and may even refuse to acknowledge that the current environment is different from the past. The human brain is fascinatingly capable of rationalizing many things, given sufficient motivation.

Stagnant "Research In Motion"

Before the iPhone was introduced, Research In Motion (RIM) BlackBerrys were the kings of the smartphone world, capturing more than 40% of the U.S. smartphone market. They were built around a specific concept of what a smartphone should be: a better PDA (Personal Digital Assistant) optimized for business users, with a focus on email, battery life, and that addictive physical keyboard. However...

The world changes rapidly, like lightning.

One point that may be underestimated today is that BlackBerry excelled at serving its customers. Because of this, when the world around them changed dramatically, RIM was unable to keep up with those changes.

As is well known, its leadership team was initially dismissive of the iPhone.

"It's not safe. The battery drains very quickly, and there's also a poor numeric keypad." — Larry Conlee (RIM's Chief Operating Officer)

Then they quickly became defensive.

RIM's arrogant belief that this new phone would never attract its enterprise customer base was not entirely unfounded. However, it completely missed the epoch-making shift in which smartphones evolved from mere "email devices" into "universal devices accessible to everyone." The company suffered from severe "technical debt" and "platform debt," a common symptom among companies that achieved early success. Their operating system and infrastructure were optimized for secure messaging and battery efficiency. By the time they accepted reality, it was already too late.

There is a perspective that suggests companies in such situations—where the greater the initial success, the harder it is to evolve—should operate with a mindset almost akin to schizophrenia: one team focused on leveraging current success, and another team dedicated to disrupting it. Apple may be the prime example of this approach, as they allowed the iPhone to gradually erode the iPod's market, and then the iPad to encroach on the Mac's territory. But if it were easy, everyone would have followed suit by now.

Yahoo!

This might be a "missed opportunity" on the scale of Mount Rushmore. Once upon a time, Yahoo was the homepage for millions of people on the internet. It was the gateway to the internet (and could even be considered the original "all-in-one app")—offering news, email, finance, games, and more. It treated search as just one of many features, to the extent that Yahoo didn't even use its own search technology in the early 2000s (it outsourced search to third-party engines and even used Google for a period of time).

It is now well known that its leadership team gave up multiple opportunities to enhance its search capabilities, most notably the opportunity in 2002 to acquire Google for $5 billion. In hindsight, this seems obvious, but Yahoo failed to understand what Google knew: search is the foundation of the digital experience. Whoever controls search will control internet traffic, and thus advertising revenue. Yahoo relied too heavily on its brand strength and display advertising, and catastrophically underestimated the massive shift toward "search-centric" navigation and later, the rise of social networks with personalized content streams.

Do you remember this guy?

Please forgive me for using a cliché, but in a bubble market, "when the tide rises, all boats float." The crypto space has experienced this firsthand (see OpenSea and many other examples). It's difficult to determine whether your startup is gaining real traction or simply riding a wave of unsustainable momentum. Matters become even more ambiguous when these periods overlap with surges in venture capital and speculative consumer behavior, which obscure underlying fundamental issues. WeWork's absurdly rapid rise and fall is a perfect example: easily accessible capital fueled massive expansion, masking a completely broken business model.

Peel away all the branding packaging and fancy wording, WeWork's core business model is very simple:

Long-term lease of office space → Pay for renovation → Sublet at a premium on a short-term basis.

If you're unfamiliar with this story, you might think, well, this sounds a lot like a short-term landlord. That's exactly what it is. A real estate arbitrage scheme disguised as a software platform.

But WeWork was not necessarily interested in building a sustainable business; instead, they optimized for something entirely different: explosive growth and a valuation narrative. This approach worked for a while because Adam Neumann was a charismatic individual who could effectively sell a vision. Investors bought into it wholeheartedly, fueling a specific type of growth that was completely detached from reality (in WeWork's case, this meant opening as many offices as possible in as many cities as possible without regard for profitability—so-called "blitzscaling"—locking in large long-term leases, and dismissing the importance of unit economics with the belief that "we can grow our way out of losses"). Many outsiders (analysts) saw through the illusion: it was a real estate company with an inverted risk profile, unstable customers, and a business model that inherently built in structural losses.

Most of the above are retrospective analyses of failed companies. In a way, this is being a "hindsight expert." However, it reflects three distinct insights into failure: companies fail because they are unable to advance technologically, unable to identify and respond to competition, or unable to adapt their business models.

I believe we are now witnessing the same scenario unfolding in prediction markets.

The Commitment to Market Forecasting

The theoretical prospects of predicting the market are tempting:

Harnessing the wisdom of the crowd = better information = transforming speculation into collective insight = an infinite market

However, today's leading platforms have reached a local peak. They have discovered a model that can generate a certain level of traction and trading volume, but this design cannot fully realize the true vision of "everything being predictable with ample liquidity."

At first glance, both have shown signs of success, and no one doubts this. Kalshi reported that the industry's annualized trading volume this year will reach approximately $30 billion (we will later delve into how much of this is organic growth). The industry is experiencing a new wave of interest in 2024-25, especially as on-chain finance narratives combine with gamified trading to further embed themselves into the cultural zeitgeist. Over-aggressive marketing efforts by Polymarket and Kalshi may also be related (in some cases, aggressive marketing does work).

But if we peel back the layers of the onion and dig deeper, we'll find some warning signs that suggest growth and PMF (Product-Market Fit) may not be as they appear on the surface. The elephant in the room is liquidity.

For these markets to function, they require deep liquidity, meaning a large number of people willing to bet on one side of the market, so that prices are meaningful and reveal true price discovery.

Kalshi and Polymarket are both struggling with this, except for a few highly publicized markets.

Significant trading volume tends to concentrate around major events (such as the U.S. presidential election and highly anticipated Federal Reserve decisions). However, most markets exhibit extremely wide bid-ask spreads and very low liquidity. In many cases, market makers are not even willing to trade (a recent admission from one of Kalshi's founders revealed that their internal market makers were not even profitable).

This indicates that these platforms have yet to solve the challenge of expanding both the breadth and depth of their markets. They remain at a plateau: performing adequately in dozens of popular markets, but the vision of a long-tail "market of everything" has not been realized.

To cover up these issues, both companies resorted to incentives and unsustainable behaviors (sound familiar?), which are typical signs of getting stuck in local optima and insufficient organic growth. (By the way, here's a small aside: in this particular market dynamic, I have a feeling that most people consider these two to be the only two major players competing.)

I don't think this is necessarily important at this stage, but if both teams believe this, then being perceived as "ahead" in this hypothetical "two-horse race" could pose a survival threat to the other company. This is an especially unstable position, which, in my view, is based on a wrong assumption.

Polymarket has launched a liquidity incentive program, attempting to narrow the bid-ask spread (theoretically, if you place orders near the current price, you will receive rewards). This helps make the order book appear tighter and indeed provides a better experience for traders by reducing slippage to some extent. However, this is still a form of subsidy. Similarly, Kalshi has introduced a trading volume incentive program, which effectively offers cash rebates to users based on their trading volume. They are essentially spending money to get people to use their product.

Right now, I can sense some of you shouting, "Uber also subsidized for a long time!!!" Yes, incentives themselves aren't inherently bad. But that doesn't mean they are good! (I also find it amusing how people always like to point out exceptions to the rule, while ignoring the pile of corpses.) Especially considering the dynamics of current prediction markets, this will soon become a hamster wheel that can't be stopped before it's too late.

Another important fact we need to understand is that a significant portion of trading volume consists of fake or artificial trades. I think it's not worthwhile to spend time debating the exact percentage, but it is clear that such fake trades make the market appear more liquid than it actually is. In reality, only a small number of participants are frequently engaging in these trades to generate profits or create an illusion of market activity. This implies that the actual natural demand is weaker than it appears on the surface.

"Last Traded Price"

In a healthy, well-functioning market, you should be able to place bets at odds close to the current market price without causing significant price fluctuations. However, this is not the case on these platforms today. Even moderately sized orders can significantly affect the odds, clearly indicating insufficient trading volume. These markets often reflect only the actions of the most recent traders, which is precisely the core of the liquidity problem I mentioned earlier. This current situation suggests that although a small group of core users sustains some market activity, these markets as a whole are neither reliable nor liquid.

But why exactly is this the case?

The market structure of pure binary trading cannot compete with perpetual contracts. It is a cumbersome approach that leads to fragmented liquidity, and even when teams attempt to work around this issue, the results are at best awkward. In many of these markets, you also encounter a strange structure where there is an "Other" option representing unknown factors, but this introduces the problem of splitting emerging competitors from that category into separate, individual markets.

The binary nature also means you cannot offer real leverage in the way users want, which in turn means you cannot generate valuable trading volume as perpetual contracts do. I've seen people argue about this endlessly on Twitter, but I'm still shocked that they can't recognize the difference between betting $100 on a 1-cent probability outcome in a prediction market and opening a 100x leveraged position of $100 on a perpetual contract exchange.

The hidden secret here is that to solve this fundamental problem, you need to redesign the underlying protocol to allow for generalization and treat dynamic events as first-class citizens. You must create an experience similar to perpetual contracts, which means you must address the jump risk present in binary-outcome markets. This is obvious to anyone actively using perpetual contract exchanges and prediction markets—precisely the users you need to attract, though these teams are often unaware of this.

Addressing jump risk means redesigning the system to ensure that asset prices move continuously, meaning they do not arbitrarily jump, for example, from a 45% probability to 100% (we have seen how frequently and openly these events are manipulated/insider-traded, but that's another topic I don't want to open up right now. Please stop committing crimes.).

Without addressing this core limitation, you will never be able to introduce the kind of leverage that makes your product attractive to users (those who can bring real value to your platform). Leverage relies on continuous price fluctuations to allow for safe liquidation before losses exceed collateral, thus preventing sudden swings (e.g., from 45% to 100% instantly) that could wipe out one side of the order book. Without this, you won't be able to timely issue margin calls or liquidations, and the platform will eventually go bankrupt.

Another core reason these markets don't function well under the current structure is the lack of a native multi-outcome hedging mechanism. First, as it stands, there is no natural way to hedge, because these markets resolve as YES/NO, and the "underlying" is the outcome itself. In contrast, if I go long on a BTC perpetual contract, I can short BTC elsewhere to hedge. This concept does not exist in today's prediction market structure, so if market makers are forced to bear direct event risk, it becomes extremely difficult to provide deep liquidity (or leverage). This again reinforces why I think the argument that "prediction markets are new and we are in a high-growth phase" is naive.

Prediction markets eventually settle (i.e., they actually close at resolution), whereas perpetual futures clearly do not. They are open-ended. A design similar to perpetual contracts could change the dynamics of prediction markets by incentivizing active trading, making the market function more continuously, and thus alleviating some of the common behaviors that make prediction markets less attractive (many participants simply hold until resolution rather than actively trading probabilities). Additionally, since prediction outcomes are one-time discrete results, and while oracle price feeds also have issues, they are at least continuously updated, making the oracle problem in prediction markets even more pronounced.

Behind these design issues lies the question of capital efficiency, but this is already well understood at present. In my personal view, earning stablecoin returns from already deposited funds does not bring about substantial change, especially considering that exchanges will provide such returns regardless. So what is the trade-off being made here? If every transaction is fully pre-funded, this is certainly good for eliminating counterparty risk! And it can also attract a certain segment of users.

However, this is disastrous for the broader user base you need. From a capital perspective, this model is highly inefficient and significantly increases participation costs. This is especially problematic when these markets require different types of users to operate at scale, as these trade-offs mean a worse experience for each user group. Market makers need substantial capital to provide liquidity, while retail traders face significant opportunity costs.

There is certainly more to unpack here, especially regarding how to address some of these fundamental challenges. A more complex and dynamic margining system will be necessary, particularly taking into account factors such as "time until event resolution" (risk is highest when the event resolution is near and odds are close to 50/50). Introducing concepts like leverage decay as resolution approaches, as well as early-tiered liquidation levels, will also be essential.

Adopting the brokerage model from traditional finance to enable instant collateralization represents another step in the right direction. This will free up capital for more efficient utilization, allow simultaneous orders across markets, and update the order book after trades are executed. Introducing these mechanisms first in scalar markets and then expanding to binary markets appears to be the most logical sequence.

The key point is that there's a vast design space yet to be explored, partly because people believe today's models are already the final form. I simply haven't seen enough people willing to first acknowledge the existence of these limitations. Not surprisingly, those who recognize this are often precisely the type of users these platforms should aim to attract (i.e., perpetual contract traders).

But what I see is that most of the criticisms of prediction markets are waved away by their proponents, who tell critics to look at the platforms' trading volumes and growth figures (absolutely real and organic figures, right?) I hope prediction markets can develop further, I hope they gain broader public acceptance, and personally, I believe that the idea of being able to trade on anything is a good thing. Most of my frustration comes from a widely accepted belief that the current version is the best possible one, but obviously, I don't agree with that view.

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