Prediction markets are evolving quickly. As builders shift from long-duration forecasts toward live sports, short-term price movements, and continuous information flows, the underlying infrastructure begins to feel misaligned. These formats behave more like live trading systems, which raises a new question: do prediction markets now need an execution environment designed specifically for real-time behavior.
Real-time guarantees matter because delays change how users experience a market and how they manage risk. A stale price can distort a trader’s position, weaken trust in displayed probabilities, or create financial losses during fast-moving events. Competing with centralized systems also means matching the immediacy users already expect.
As prediction markets move into the mainstream, they must offer scale, real-time responsiveness, and decentralized transparency at the same time. The industry has solved parts of this, but not yet all three together.
The move toward real-time markets
Early prediction markets succeeded despite slow updates and multi-second or multi-minute lag between new information and reflected market state. That rhythm worked for long-running elections, macro events, and outcomes with slow information cycles. The new generation of markets moves differently.
Live sports markets, for example, need to update as the game unfolds. A swing in momentum, a single play, or a sudden injury can shift the probability in seconds. Short-duration financial markets behave in a similar way. They demand updates that feel constant rather than periodic. Even experimental AI-driven trading agents expect the market to react at human or subhuman speeds.
These formats stretch traditional chains to the edge. They assume the system can ingest new information, update state, and reflect it on the user interface almost instantly. Many current networks cannot meet those expectations consistently.
What builders are experiencing today
Across interviews, builders describe the same underlying pattern. Their ideas are not blocked. Their infrastructure is.
The most acute pain shows up in live sports prediction markets. Teams report that indexing layers often fall several minutes behind during high-intensity moments of a game. A user may see a stale position or outdated price, forcing them to rely on block explorers or off-chain data to understand what is happening. Some builders need interface updates in the range of a few hundred milliseconds and oracle-to-market transitions within one or two seconds. Without that, the product breaks.
Projects building in-play sports markets, including ScorePlay, operate in environments where even small indexing delays can make UIs unreliable during critical moments.
Even outside sports, teams working on short-term markets emphasize similar needs. Their markets move quickly enough that global-state chains can become congested or unpredictable at precisely the moments when accuracy matters most. Builders end up creating custom infrastructure, running private servers, or stitching together hybrid systems to simulate the speed they need. These workarounds show ingenuity, but they also highlight what the underlying chain cannot provide.
In all of these conversations, the issue is not token swaps or exchange mechanics. It is the behavior of prediction markets themselves, where price represents probability and must track information in near real time.
Existing solutions reveal a structural limit
The approaches used today by successful projects demonstrate both the creativity and the constraints of current systems.
Polymarket’s hybrid model has helped it scale dramatically. Public dashboards show the platform processing hundreds of millions in monthly volume, and Fortune recently reported that the company reached a 9 billion dollar valuation following investment from the New York Stock Exchange’s parent company.
Other networks have improved their consensus mechanisms and reduced finality times to several seconds. Polygon’s recent upgrade brought finality down to around five seconds, but real-world stalls lasting 10 to 15 minutes were still observed as recently as late 2025.
Hyperliquid is developing HIP-4 because its existing architecture cannot support binary prediction markets without distortion. The project notes its current one percent tick size ****for price movements, which creates a slow and chunky adjustment process after resolution. In practice, this can leave a long window where informed traders can exploit the gap before the market converges.
These methods are not wrong. They represent rational designs under the constraints of existing chains. But taken together, they suggest that prediction markets are beginning to outgrow infrastructures built for general-purpose transactions and token trading.
The real constraint is not throughput
The broader blockchain industry often frames progress in terms of throughput. Higher TPS is seen as the primary solution to scalability. Prediction markets, however, tend to break not at the level of volume but at the level of responsiveness.
Real-time prediction markets need latency guarantees, strong isolation between unrelated markets, reliable syncing between oracle updates and market state, and execution that behaves predictably during bursts of activity. A market that updates every second cares far more about consistency than about the raw number of transactions a chain can push.
This distinction has been highlighted by analysts like Messari and Delphi Digital, who describe prediction markets as continuous information engines rather than static bet-settlement systems.
This implies a fundamentally different execution environment than the one designed for high-throughput token activity.
The idea of a prediction market OS
A prediction market OS would address these needs directly. Rather than treating prediction markets as one application on top of a global state machine, it would give them an execution model shaped around their actual behavior.
One approach to this is the microchain model, where each market or each user operates within isolated state. With this structure, a surge of activity in one live market does not slow down others. Oracle updates can feed directly into the specific market that needs them without waiting for a global chain to advance or an indexing layer to catch up. Finality becomes predictable, not best-effort.
The aim is not simply to make transactions faster. It is to remove block timing as the governing factor in how quickly a prediction market can incorporate new information.
This is infrastructure designed for probability, not for generic trading.
What deeper real-time capability unlocks
When markets reliably update in real time, entirely new formats become viable. In-play sports markets can adjust continuously rather than in coarse steps. Short-duration markets can resolve cleanly without long, exploitable convergence windows. AI agents can participate meaningfully because the system reacts on a timescale compatible with their strategies. And as more markets operate in this way, prediction markets start to resemble live information networks rather than static binary contracts.
This does not eliminate the traditional challenges of liquidity or UX. But it expands what prediction markets can become once those challenges are addressed.
A direction emerging from real constraints
Prediction markets are starting to expose where general-purpose chains fall short. As builders push into formats that require responsiveness measured in seconds rather than minutes, the limitations of global-state architectures become clearer. Their workarounds are inventive, but they also point to what an ideal system might look like.
A prediction market OS is one possible response. It reflects the fact that markets built around information behave differently than markets built around tokens. Whether the ecosystem moves in this direction will depend on how quickly these real-time use cases grow and how much pressure they put on current infrastructure. For now, it is a direction emerging from lived experience and one that deserves close attention.
The OS described here is purpose-built for prediction market logic and resolution, not for token swaps or traditional order books.
About Linera
Linera is the real time blockchain for prediction markets. It introduces microchains, a new architecture that brings predictable low latency and linear scaling to applications. Linera enables developers to build secure onchain applications without limits.
Website: linera.io
X: @linera_io
Discord: discord.gg/linera
GitHub: github.com/linera-io
LinkedIn: linkedin.com/company/linera-io
