Original Title: Two Kites Dancing In A Hurricane

Original Author: 0xsmac

Original Compiler: SpecialistXBT

Editor's Note: This article examines the prosperous facade of prediction markets with a sharp perspective. The author points out that today's prediction markets are falling into the "local optimum" trap reminiscent of Blackberry and Yahoo. Although the binary options model adopted by mainstream prediction markets has gained tremendous traffic in the short term, it is plagued by structural issues of liquidity scarcity and inefficient capital. The article proposes the evolution of prediction markets towards a "perpetual contract" model, providing constructive deep thinking for achieving a true "market of everything."

Why do companies find themselves chasing the wrong goals? Can we fix prediction markets before it's too late?

"Success is like a strong drink, intoxicating. It is not easy to navigate the fame and flattery that come with it. It can corrode your mind, making you start to 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 cheers of the crowd have always been the most wonderful music." ------ Vin Scully

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

But early success carries a unique danger: you may have won the wrong rewards. While we often joke that "play stupid games, win stupid prizes," in reality, the games we participate in are often evolving in real-time. Therefore, the factors that led you to win in the first phase may very well become stumbling blocks to winning larger prizes as the game matures.

One manifestation of this outcome is that companies unknowingly fall into a "local optimum." The feeling of winning is so good that it not only makes you lose your direction but also blinds you to self-awareness, preventing you from seeing the true situation you are in.

In many cases, this may just be a mirage, an illusion supported by external factors (such as economic prosperity leading to an overflow of disposable income in consumers' hands). Or, the product or service you built may indeed work well, but only within a specific scope or under certain conditions, failing to scale to a broader market.

The core conflict here is that to chase the true ultimate prize (i.e., the global optimum), you need to come down from the current peak. This requires immense humility. It means making tough decisions: abandoning a core feature, completely restructuring the tech stack, or personally overturning the model you once thought was effective. What makes all this even more challenging is…

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

This is exactly the predicament that prediction markets find themselves in today. In their current form, they will never achieve widespread market adoption. I do not want to waste ink debating whether they have achieved this status (after all, there is a vast chasm between knowing something exists and having a real demand to use it). Perhaps you disagree with this premise and are now preparing to close the page or read the rest with resentment. That is your right. But I will reiterate why this model is broken today and what I believe these platforms should look like.

I do not want to sound too much like a tech industry person; I will not reiterate the "innovator's dilemma," but the classic cases in this regard are Kodak and Blockbuster. These companies (and many others) achieved great success, which created an inertia against change. We all know how the story ends, but simply throwing up our hands and saying "we need to do better" is not constructive. So, what specifically led to these outcomes? Do we see these signs in today's prediction markets?

Sometimes, the obstacle lies at the technical level. Startups often build products in a specific subjective way that may work in the initial phase (the fact that a startup can do this is already overcoming significant challenges!), but soon it becomes a future architectural shackle. Wanting to continue expanding after an initial explosion or adjusting product design means threatening some seemingly effective core components. People naturally tend to solve problems through incremental fixes, but this quickly leads to the product becoming a patchwork. Moreover, this only delays the acceptance of the harsh truth: what is truly needed is a complete rebuild or reimagining of the product.

Early social networks experienced this when they hit performance ceilings. Friendster was a pioneer of social networks in 2002, connecting millions of users online with "friends of friends." But when a specific feature (viewing friends within "three degrees of separation") caused the platform to crash under the load of calculating exponential connections, trouble arose.

The team refused to scale back 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 of popularity but could not surpass it because its core architecture was flawed, and the team refused to acknowledge, dismantle, and fix it. (By the way, MySpace later fell into its own "local optimum" trap: it was built on a unique user experience of highly customizable user homepages and focused on music/pop culture communities. The platform was primarily ad-driven and ultimately became overly reliant on its advertising portal model, while Facebook emerged with a cleaner, faster, and "real" identity-based network. Facebook attracted some early MySpace users but undoubtedly appealed more to the next large wave of social media users.)

The persistence of such behavior is not surprising. We are all human. Achieving some superficial success, especially as a startup with a high failure rate, naturally leads to self-inflation. Founders and investors begin to believe in the performance they boast about and double down on the formula that brought them to today, even as warning signals flash brighter and brighter. People easily ignore new information and even refuse to face the reality that the current environment is different from the past. The human brain is fascinating; with enough motivation, we can rationalize many things.

Stagnation of "Research In Motion"

Before the iPhone was released, Research In Motion (RIM) and its Blackberry phones were the kings of smartphones, holding over 40% of the U.S. smartphone market. It was built on a specific vision of smartphones: a better PDA (personal digital assistant) optimized for enterprise users, specifically for email, battery life, and that beloved physical keyboard. However…

The world changes, fast as 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 could not adapt.

It is well known that its leadership team initially dismissed the iPhone.

"It’s not secure. The battery drains too quickly, and it has a terrible digital keyboard." ------ Larry Conlee (RIM COO)

They quickly became defensive.

RIM arrogantly believed that this new phone would never attract its enterprise customer base, which was not unfounded. But this completely missed the groundbreaking shift of smartphones evolving from "email machines" to "universal devices for everyone." The company suffered from severe "technical debt" and "platform debt," common symptoms of 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 too late.

One perspective is that companies in such situations (the greater the initial success, the harder the evolution, which is one reason why Zuckerberg is considered the "GOAT") should operate with an almost split personality: one team focused on leveraging current success, while another team is dedicated to disrupting it. Apple may be a prime example of this, allowing the iPhone to cannibalize the iPod market and then letting the iPad eat into the Mac market. But if this were easy, everyone would be doing it.

Yahoo

This may be a presidential mountain-level "missed opportunity." Once upon a time, Yahoo was the homepage for millions. It was the gateway to the internet (even arguably the original "universal app")—news, email, finance, games, everything you could want. It viewed search as just one of many features, to the extent that Yahoo did not even use its own search technology in the early 2000s (it outsourced search to third-party engines and even briefly used Google).

It is now well known that its leadership team missed multiple opportunities to deepen search capabilities, most famously the chance to acquire Google for $5 billion in 2002. In hindsight, it is obvious, but Yahoo failed to understand what Google knew: search is the foundation of the digital experience. Whoever owns search will own internet traffic and, consequently, advertising revenue. Yahoo overly relied on its brand strength and display advertising while catastrophically underestimating the monumental shift towards a "search-centric" navigation method and later the personalized content streams of social networks.

Remember this guy?

Please forgive me for using a cliché, but in a bubble market, "a rising tide lifts all boats." The cryptocurrency space has experienced this deeply (see Opensea and many other examples). It is difficult to determine whether your startup has real traction or is merely riding an unsustainable wave of momentum. What complicates matters further is that these periods often coincide with a surge in venture capital and speculative consumer behavior, obscuring potential fundamental issues. WeWork's laughable rapid rise and fall illustrate this well: easily accessible capital led to massive expansion, masking a completely broken business model.

Strip away all the branding and lofty rhetoric, and WeWork's core business model is very simple:

Long-term lease office space → Spend money on renovations → Short-term sublease at a premium.

If you are not familiar with this story, you might think, well, this sounds a lot like a short-term landlord. That is precisely its essence. A real estate arbitrage deal disguised as a software platform.

But WeWork was not necessarily interested in building a lasting business; they were optimizing for something entirely different: explosive growth and valuation narratives. This worked for a short time because Adam Neumann was charismatic enough to sell the vision. Investors bought into it and fueled a specific type of growth that was completely detached from reality (in WeWork's case, this meant opening as many offices in as many cities as possible without regard for profitability, i.e., "lightning expansion," locking in large long-term leases, and dismissing the notion that unit economics were crucial, believing "we can grow our way out of losses"). Many outsiders (analysts) saw through its essence: it was a real estate company with an inverted risk profile, unstable customers, and a business model inherently built with structural losses.

Most of the above is a retrospective analysis of failed companies. In a sense, this belongs to "hindsight bias." But it reflects three different insights into failure: companies fail due to an inability to progress technically, a failure to recognize and respond to competition, or an inability to adjust their business model.

I believe we are now witnessing the same scene playing out in prediction markets.

The Promise of Prediction Markets

The theoretical prospects of prediction markets are enticing:

Leveraging the wisdom of the crowd = Better information = Turning speculation into collective insight = An infinite market

But today's leading platforms have hit a local peak. They have discovered a model that can generate some traction and trading volume, but this design fails to achieve the true vision of "everything being predictable and liquid."

On the surface, both show signs of success; no one doubts that. Kalshi reports that the industry's annualized trading volume will reach about $30 billion this year (which will be discussed in detail later regarding how much of this is organic growth). The industry is experiencing a new surge of interest in 2024-25, especially as the narrative of on-chain finance and the gamification of trading further penetrate cultural zeitgeist. The excessive marketing of Polymarket and Kalshi may also be related to this (in some cases, aggressive promotion does indeed work).

But if we peel back the onion and dig deeper, we find some warning signs indicating that growth and PMF may not be as they appear. The elephant in the room is liquidity.

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

Kalshi and Polymarket struggle with this point, except for a few very high-profile markets.

Huge trading volumes are concentrated around major events (U.S. elections, highly anticipated Federal Reserve decisions), but most markets exhibit extremely wide bid-ask spreads and almost no activity. In many cases, market makers are even reluctant to execute trades (one of Kalshi's founders recently admitted that their internal market makers are not even profitable).

This indicates that these platforms have yet to crack the puzzle of expanding market breadth and depth. They are stagnating at a level: performing decently in dozens of hot markets, but the long-tail vision of a "market of everything" has not been realized.

To cover up these issues, both companies resort to incentives and unsustainable behaviors (sound familiar?), which are typical signs of hitting a local optimum and natural growth being insufficient (by the way, in this specific market dynamic, I have a feeling that most people think these two are the only major competitors.

I do not think this is necessarily important at this stage, but if these two teams believe this, then if one is perceived as "leading" in this assumed "two-horse race," it poses a survival threat to the other. This is a particularly unstable position, in my view, based on a false assumption).

Polymarket launched a liquidity rewards program to try to narrow the spreads (theoretically, if you place an order near the current price, you will be rewarded). This helps make the order book look tighter and indeed provides a better experience for traders by somewhat reducing slippage. But this is still a subsidy. Similarly, Kalshi launched a trading volume incentive program, essentially offering cash back based on users' trading volume. They are paying people to use the product.

Now I can sense that some of you are shouting, "Uber also subsidized for a long time!!!" Yes, incentives themselves are not bad. But that does not mean they are good! (I also find it interesting that people always like to point to exceptions to the rules without looking at the pile of corpses.) Especially considering the current dynamics of prediction markets, this will quickly turn into a hamster wheel that cannot be stopped before it's too late.

Another fact we need to know is that a significant portion of the trading volume is fake trading. I think it is pointless to argue about the exact proportion, but clearly, fake trading makes the market appear more liquid when, in reality, it is just a few participants frequently operating to gain profits or create market hype. This means that natural demand is actually weaker than it appears.

"Last Trader Pricing"

In a healthy, well-functioning market, you should be able to place bets close to the current market odds without significant price fluctuations. But today, this is not the case on these platforms. Even moderately sized orders can significantly impact the odds, clearly indicating insufficient trading volume. These markets often only reflect the movements of the last trader, which is at the core of the liquidity issue I mentioned earlier. This status quo indicates that while a small core of users maintains some market operation, these markets are overall neither reliable nor liquid.

But why is this the case?

The pure binary trading market structure cannot compete with perpetual contracts. It is a cumbersome approach that leads to fragmented liquidity, and even if these teams try to address this issue with workarounds, the results are at best clumsy. In many of these markets, you will 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 basket into their own independent markets.

The binary nature also means you cannot provide real leverage in the way users want, which in turn means you cannot generate valuable trading volume like perpetual contracts. I see people arguing about this on Twitter, but I am still shocked that they cannot recognize: betting $100 on a 1% probability outcome in a prediction market is not the same as opening a $100 position with 100x leverage on a perpetual contract exchange.

The unspoken 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 need to address the jump risk present in binary outcome markets. This is evident to anyone actively using perpetual contract exchanges and prediction markets—yet, unbeknownst to these teams, these are precisely the users you need to attract.

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

If this core limitation is not addressed, you will never be able to introduce the kind of leverage needed to make the product attractive to users (those who can bring real value to your platform). Leverage relies on continuous price fluctuations to safely close positions before losses exceed collateral, thus avoiding sudden fluctuations (e.g., jumping from 45% to 100%) that clear one side of the order book. Without this, you cannot timely margin call or liquidate, and the platform will eventually go bankrupt.

Another core reason these markets do not work under the current structure is that there is no native multi-outcome hedging mechanism. First of all, as it stands, there is no natural way to hedge because these markets resolve to 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 believe the argument that "prediction markets are a new thing, and we are in a high-growth phase" is naive.

Prediction markets will ultimately settle (i.e., they will actually close at resolution), while perpetual futures obviously do not. They are open-ended. A design similar to perpetual contracts can change the market by incentivizing active trading, making its functions more continuous, 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, while oracle prices, although problematic, are at least continuously updated, the oracle issues in prediction markets are also more pronounced.

Behind these design issues lies a capital efficiency problem, but this seems to be well understood at this point. Personally, I believe that "earning stablecoin yields" with the funds already invested does not bring about substantive change. Especially considering that exchanges will provide this yield regardless. So what is the trade-off here? If every transaction is fully prepaid, that is certainly good for eliminating counterparty risk! And it can also attract some users.

But this is disastrous for the broader user base you need, as this model is extremely inefficient from a capital perspective and will only significantly increase participation costs. This is particularly bad when these markets require different types of users to operate at scale, as these choices mean that the experience for each user group will be worse. Market makers need substantial capital to provide liquidity, while retail traders face enormous opportunity costs.

There is certainly more to unpack here, especially around how to attempt to solve some of these fundamental challenges. More complex and dynamic margin systems will be necessary, especially considering factors like "time until the event occurs" (the risk is highest when the event resolution is near and the odds are close to 50/50). Introducing concepts like leverage decay as the resolution approaches is also necessary, and early tiered liquidation levels would help.

Drawing from traditional financial brokerage models to achieve instant collateralization is another step in the right direction. This will free up capital for more efficient use and allow for simultaneous orders across markets, updating the order book after execution. Introducing these mechanisms first in scalar markets and then expanding to binary markets seems to be the most logical order.

The point is, there is a vast amount of design space that has yet to be explored, partly because people believe today's model is the final form. I just do not see enough people willing to confront the existence of these limitations first. Perhaps it is not surprising that those who recognize this are often the very types of users these platforms should want to attract (aka perpetual contract traders).

But what I see is that their criticisms of prediction markets are mostly brushed aside by supporters, who are told to look at the trading volumes and growth numbers of these two platforms (absolutely real and organic numbers, uh-huh). I hope prediction markets can evolve, I hope they can be embraced by the masses, and I personally believe that a market of everything is a good thing. Most of my frustration stems from a widely accepted view that today's version is the best version, but clearly, I do not agree with that perspective.