News & Updates
The latest news and updates from companies in the WLTH portfolio.
Tens of thousands of Nvidia GPUs sit in Meta's Prometheus supercluster, waiting for a failure
And other fun facts about how Zuckercorp aims to keep a million GPUs running long enough to train a model More than 11 percent of the GPUs at Meta's Prometheus supercluster in New Albany, Ohio, are either dead or sitting idle waiting to step in when the next drops out, Zuckercorp revealed this week. The massive pool of redundant GPUs is just one of several measures the hyperscaler has taken to keep its AI training runs on track. Prometheus is among Meta's largest compute deployments to date. Powered by Nvidia's Grace Blackwell and (soon) Vera Rubin rack systems, Meta says the supercluster will eventually scale to a gigawatt of capacity spanning about a million accelerators. But as anyone who has worked on a large scale compute cluster understands, whether for LLM training or traditional HPC workloads, more chips inevitably means more problems. In an eight-GPU node, the chance of having a GPU fail is relatively low. In a 100,000-GPU cluster, failures are unavoidable. Meta is building something that's 10x larger and requires distributed training runs across multiple datacenters. Keeping the entire thing humming long enough to reach the next checkpoint becomes a major headache. In a blog post published this week, Meta offered a unique look at the work it's done to keep failures from derailing the development of models like Muse Spark or its most recent release Muse Wonder. Idle GPUs are the cost of continuity When Meta began the first phase of its Prometheus deployment in New Albany, the company says, it was experiencing roughly 1,000 interruptions per day for every 8,000 GPUs. Interruptions don't mean GPU failure. They simply mean something crashed, failed, or timed out, forcing the training run to stop and roll back to the last known good checkpoint. Meta claims in the year since it's been able to cut down the number of interruptions by 99 percent, to about 11 per day per 8,000 GPUs. One of the ways it's done this is by keeping some of Prometheus' GPUs in reserve. The cluster is currently powered by a combination of GB200 and GB300 systems, each with 72 GPUs per NVLink domain. However, only 64 of them are ever in use at any given moment. The other eight are essentially hot spares. Meta tells us that while these spare GPUs may not be used in the larger training run, they are still available to run opportunistic side jobs, which can be preempted in the case of a failure. Meta did this because a single failed GPU would take out an entire rack and interrupt the training run. Now, when a GPU fails, one of the spares can slot in and take its place. While this saves time -- Meta doesn't have to drop the entire rack from the cluster or wait for someone to swap the faulty component to start training again -- a failed GPU would still result in an interruption, albeit a shorter one. Parallel resiliency To mitigate this, Meta took advantage of the fact that large scale training jobs usually use data parallelism, where multiple copies of the model are resident within the cluster. "When a fault can be isolated to a data-parallel model replica, we reduce the impact of a hardware failure by allowing a single faulty replica to be taken offline, while the remaining cluster repeats that step and continues productive training," Meta explained. "The compromised slice simply restarts on available spare hosts and resynchronizes, converting a potential total system halt into a minor, temporary reduction in aggregate throughput." In other words, Meta's training runs only get interrupted if the fault exceeds the data parallel domain. When this happens, the company has implemented a comprehensive checkpointing scheme, which you can think of a bit like auto-save or a revision history. That way if something does fail, Meta doesn't have to start over from scratch. "Our distributed checkpointing architecture asynchronously saves the model state -- including gradients and optimizer data -- to multi-tier storage," the company wrote. NVLink growing pains Meta says one of the most difficult challenges was making the NVLink fabrics used to connect 72 GPUs behave like one great big accelerator. Earlier H100 and H200-based systems featured just eight GPUs per NVLink domain, which meant the links were far shorter and less numerous. But this functionality was expanded nine-fold with Nvidia's Blackwell generation. "With over 600,000 NVLink connections, even marginal signal degradation triggers frequent NVLink recovery cycles," the company wrote. To overcome this, Meta had to implement a continuous telemetry system and conduct L11 diagnostic burns to isolate weak links before the systems were put into production. Power was another challenge. Ramping up and down tens of thousands of GPUs for a training run can introduce massive spikes in power consumption called transients, which can overwhelm power supply equipment. According to Meta, by focusing on the stability and reliability of its cluster, it was able to achieve more than 85 percent "goodput" -- a measure of useful throughput -- for pretraining and more than 90 percent for reinforcement learning on its latest model Muse Wonder. The company's ambitions don't stop here. As it works its way toward a million GPUs, Meta hopes to get its interruption rate down to just two per day for every 8,000 GPUs, or about 100,000 GPU-hours mean time between interrupts. ® Update: Following publication, Meta reached out to clarify that spare compute is used to run opportunistic jobs that cane be preempted in case of a failure. We have updated our story and the headline to reflect this.
Structured outputs for Anthropic Claude models on Amazon Bedrock
Amazon Bedrock now supports structured outputs for all Anthropic Claude models, on both the bedrock-runtime and bedrock-mantle endpoints, where the model is available. Whether you call Claude through Boto3 and the bedrock-runtime APIs or through the Messages API and the bedrock-mantle endpoint, you can now receive schema-compliant JSON responses that conform to a defined structure. You no longer need custom parsing, retry logic, or post-processing. Practical applications With structured outputs for Claude on Amazon Bedrock, you can automate reliable, schema-compliant tasks across industries. Examples include extracting every required field from financial earnings reports and compliance documents, parsing clinical notes into Electronic Health Records (EHR), and enriching product catalogs with consistent, validated results. You can also analyze contracts for key terms, parties, and obligations, or power intelligent support ticket routing where intents, sentiments, and entities always match your application's data model. What changes with structured outputs on Amazon Bedrock Structured outputs use constrained decoding to enforce your JSON schema during token generation, not after the fact. The model doesn't produce tokens that would violate your schema. This changes output formatting from probabilistic to deterministic. Two complementary mechanisms are now available on both Amazon Bedrock endpoints: What this means for you Each response is a valid JSON, so you no longer encounter JSON.parse() errors or parsing exceptions. Responses are also type safe: field types are enforced, and required fields are always present. Because outputs always conform to your schema, you don't need retries to handle schema violations, making your integration more reliable. Structured outputs also constrains responses to only the fields your schema defines, which reduces output token count and can lower inference costs for high-volume workloads. These properties make structured outputs production ready, so you can deploy at scale without additional schema-validation code. Supported models and availability Structured outputs works with batch inference, so you can process large volumes of requests asynchronously while still receiving schema-compliant outputs. On the bedrock-runtime endpoint, it also works with cross-Region inference, so you can use it across AWS Regions without additional setup. If you need responses in real time, you can stream structured responses as typed events. How it works When you make a request with a JSON schema, here's what happens behind the scenes: * Schema validation - Amazon Bedrock validates your JSON schema against the supported JSON Schema Draft 2020-12 subset. * Grammar compilation - For new schemas, Amazon Bedrock compiles a grammar artifact (the first request might take slightly longer). * Caching - Compiled grammars are cached for 24 hours, making subsequent requests faster. * Constrained generation - The model generates tokens that produce valid JSON matching your schema. Performance considerations For performance, the first request with a new schema might take longer because Amazon Bedrock compiles a grammar artifact before generation begins. After that initial compilation, subsequent requests using the same schema run with minimal overhead because the compiled grammar is cached. This cache is scoped to your account and lasts for 24 hours from first access. Changing the structure of your JSON schema invalidates the cache and triggers a fresh compilation, while changing only the name or description fields leaves the cached grammar intact. Getting started: Structured outputs with the InvokeModel API (bedrock-runtime) The InvokeModel API takes the Anthropic-native request body, so the schema goes in output_config.format as a JSON object rather than a JSON string. The following example shows how to extract the structured lead data from a customer email using boto3 with an Anthropic Claude model: Python JSON The response conforms to your schema. No additional validation is required. For additional API usage examples, including the Messages API, see the accompanying sample notebook. Strict tool use for agentic workflows Amazon Bedrock Mantle is designed for agentic applications with server-side tools, background inference, and stateful conversations. With strict tool use on the bedrock-mantle endpoint, you can now guarantee that tool parameters match your input schema exactly. The following example shows how to define a tool with strict schema enforcement: Setting strict: true constrains the model's tool input to match your schema exactly. The location field is always a string, and the unit field is always either celsius or fahrenheit. The model can't add fields outside your schema, so the tool input contains only what your schema specifies. This is especially valuable for agentic use cases on Amazon Bedrock Mantle, where models orchestrate multi-step workflows with server-side tools like web search and code interpreter. Top use cases for Claude structured outputs Structured outputs reduce error-handling complexity the most in three common patterns: * Data extraction: Extract fields from invoices, contracts, email, and transcripts into JSON that always matches your schema, ready to load into databases or customer relationship management (CRM) systems. * Reliable agent tool calls: Validate tool arguments against their schemas at generation time, so multi-step agent workflows run without failures from malformed input. * Classification at scale: Classify tickets, leads, or content by intent, priority, and sentiment for routing, moderation, and evaluation. Claude Haiku 5.5 reliably handles quick and repetitive workloads such as summaries, compactions, database queries, and classification requests. The following example shows how to classify and route incoming data using the Invoke API and Haiku 5.5: Requirements and best practices To use structured outputs effectively on Amazon Bedrock, follow these guidelines: * Set additionalProperties: false on all objects: This is required for structured outputs to work. Without it, your schema won't be accepted. * Use descriptive field names and descriptions: Models use property names and descriptions to understand what data to extract. Clear names like customer_email outperform generic names like field1. * Use enum for constrained values: When a field has a limited set of valid values, use enum to constrain options. * Start basic, then add complexity: Begin with the minimum required fields and add complexity incrementally. Basic schemas compile faster, are easier to debug, and keep your request payload lean because the schema counts toward the Converse API payload size. * Reuse schemas to benefit from caching: Structure your application to reuse schemas across requests. The 24-hour grammar cache improves performance for repeated queries. * Check the stop reason in every response: Two scenarios can produce non-conforming responses: refusals (when the model declines for safety reasons) and token limits (when max_tokens is reached before completing). Handle both cases in your code. * Test with realistic data before deployment: Validate your schemas against production-representative inputs. Edge cases in real data often reveal schema design issues. For additional best practices and schema design recommendations, see Structured outputs in the Amazon Bedrock User Guide. Supported JSON Schema features * All basic types: object, array, string, integer, number, boolean, null. * enum (strings, numbers, booleans, or nulls only). * const, anyOf, allOf (with limitations). * $ref, $def, and definitions (internal references only). * String formats: date-time, time, date, duration, email, hostname, uri, ipv4, ipv6, uuid. * Array minItems (only values 0 and 1). Not supported These keywords are accepted by the API but are not enforced during generation. A request that includes them returns a successful response, and the model can still return values that violate them. Validate these constraints in your application rather than relying on the schema to enforce them. * Recursive schemas. * External $ref references. * Numerical constraints (minimum, maximum, multipleOf). * String constraints (minLength, maxLength). * additionalProperties set to anything other than false. Conclusion Structured outputs for Anthropic Claude models on Amazon Bedrock provides the reliability that production AI applications require. It is available now on both the bedrock-runtime and bedrock-mantle endpoints. By combining JSON Schema output format and strict tool use, you can build data pipelines that need no additional validation. You can also create reliable agentic workflows and production AI applications without custom parsing or retry logic. Structured outputs for Claude on Amazon Bedrock is now generally available. To learn more, see the Amazon Bedrock documentation and explore our sample notebook. What workflows could validated, schema-compliant JSON improve in your organization? Share your feedback and questions in the comments.
How Vercel is rethinking go-to-market with AI
Vercel is pioneering an AI-powered go-to-market approach, drastically reducing human sales and support teams by integrating AI agents for qualification and resolution. This post also explores a contrarian view on traditional one-on-one meetings, advocating for real-time, asynchronous communication over scheduled discussions. AI agents can reduce a 10-person sales team to one person and an AI agent Vercel is demonstrating a paradigm shift by integrating AI agents into go-to-market functions, allowing a 10-person sales team to scale down to one person supported by an AI agent. AI handles inbound qualification and 91% of support resolutions, freeing human teams to focus on complex prospecting and identifying product problems. This creates a leaner BDR, support, and RevOps structure, improving the headcount-to-revenue ratio. We get into why she walked away from one of the most valuable private companies in the world, what it's like to take a 10-person sales team down to one person and an AI agent, and the brutally honest feedback that reshaped how she leads. Scheduled one-on-ones can hinder real-time problem solving The speaker adopts an 'anti-one-on-one' stance, particularly for leadership teams and new managers. This perspective suggests that urgent issues are often resolved immediately through platforms like Slack, diminishing the impact of scheduled one-on-ones for critical discussions. It challenges the conventional belief that regular, scheduled meetings are always the most effective management tool. And we have like a leadership team and you know, new managers and I'm I've I would say I'm also the like anti- one-on-one. The founder-executive relationship is a professional marriage The relationship between an executive and the founder is paramount, outweighing other factors for company success. It is likened to a 'professional marriage,' requiring significant time investment, open communication, and the capacity for productive disagreement. This strong bond is essential for navigating challenges and ensuring strategic alignment at the highest levels of the company. my general advice maybe is wrong but my advice my opinion is the relationship with the founder is maybe 10 times more important than every else everything else on the list. Operators must prioritize predictable output over minor issues An operator's job is to build systems that consistently produce predictable output, specifically revenue. This requires disciplined prioritization, where operators must actively decide if a minor issue is a critical priority (P0) contributing to the system's next strategic evolution. They must learn to overlook minor imperfections, like fixing commas in a document, to focus on higher-impact initiatives. I think so because like I'm my job is to be an operator.
Prediction Markets vs FedWatch: Can Polymarket Predict the Fed Better in 2026?
The Federal Reserve's interest rate outlook has shifted dramatically in just a few weeks. After the Fed raised its benchmark rate to 3.75%-4.00% in September 2026, traders initially expected another increase in October. However, softer inflation readings and disappointing employment data quickly changed those expectations. By October 7, the CME FedWatch Tool indicated just a 17.2% probability of another rate hike, while prediction markets such as Polymarket and Kalshi were also leaning toward a pause. This rapid reversal raises an important question: can prediction markets anticipate Federal Reserve decisions more effectively than traditional interest rate futures? As investors increasingly monitor both Polymarket and CME FedWatch, understanding their differences has become essential for interpreting monetary policy expectations, financial market volatility, and the potential direction of Bitcoin and other cryptocurrencies. Why Have Fed Rate Predictions Changed So Quickly? The September Rate Hike Changed Market Expectations On September 16, 2026, the Federal Open Market Committee (FOMC) unanimously increased the federal funds target range by 25 basis points to 3.75%-4.00%. The decision marked the Fed's first rate increase since 2023 and reflected continued concerns about inflation remaining above the central bank's 2% target. Higher energy costs, resilient economic activity, and potential inflationary pressures from investment spending all contributed to the debate surrounding monetary policy. The decision initially reinforced expectations that the Fed might continue tightening monetary conditions. Investors evaluated whether September marked the beginning of another tightening cycle or a limited adjustment intended to prevent inflation from becoming more persistent. This distinction matters because a sustained hiking cycle could create significantly different conditions for equities, Treasury bonds, and the broader Cryptocurrency market than a single rate increase followed by a pause. However, the Fed's September decision did not guarantee another increase in October. The committee emphasized that future actions would depend on economic developments, while policymakers remained divided over how aggressively monetary policy should respond to inflation. As new economic reports arrived, financial markets began reassessing both the timing and necessity of another hike. Softer Inflation and Employment Data Triggered a Reversal The first major shift came on September 30, when the latest personal consumption expenditures (PCE) inflation data indicated softer price pressures than economists had anticipated. Kalshi reported that its October 25-basis-point rate-hike probability had fallen from 65% on September 25 to just 33% on September 30. At the same time, its probability of an unchanged rate increased to 65%. The reversal demonstrated how quickly traders adjusted their expectations following new macroeconomic information. The September employment report, released on October 2, added further pressure to the case for an immediate rate increase. U.S. nonfarm payrolls increased by only 29,000 jobs, compared with economists' expectations of approximately 90,000, while the unemployment rate reached 4.2%. Although the report did not necessarily signal a recession, weaker hiring reduced the urgency for additional tightening. Markets reacted immediately. MarketWatch reported that Polymarket's estimated probability of an October rate increase declined from approximately 25% before the employment report to 18% afterward. Meanwhile, CME futures pricing indicated a strong preference for keeping interest rates unchanged. The figures were observed at different points and should not be interpreted as perfectly synchronized quotes, but they show how both prediction markets and traditional futures markets incorporated the same economic surprise. Fed Minutes Reveal a More Complicated Policy Outlook The September FOMC meeting minutes, released on October 7, added another layer of uncertainty. Although policymakers unanimously supported the September increase, they disagreed about the underlying inflation risks. Some viewed higher interest rates as protection against potential energy and supply shocks, while others considered additional tightening necessary to contain broader demand-driven inflation. Most officials still regarded another increase before the end of 2026 as potentially appropriate, although the timing remained uncertain. Market expectations increasingly favored a pause in October and the possibility of another increase in December. This distinction between near-term and year-end policy expectations is central to understanding why October rate-hike probabilities declined even as Fed officials maintained a relatively restrictive outlook. How Does CME FedWatch Calculate Interest Rate Probabilities? The CME FedWatch Tool is one of the most widely followed indicators of market expectations surrounding Federal Reserve decisions. Rather than surveying economists or asking investors to vote on possible policy outcomes, it derives probabilities from prices in 30-Day Federal Funds futures contracts traded on CME Group's exchanges. These futures reflect expectations about the average effective federal funds rate during specific calendar months. The FedWatch methodology translates futures-implied rates into possible FOMC target-rate outcomes using assumptions about the size of rate changes and the relationship between the target rate and the effective federal funds rate. CME generally models policy changes in increments of 25 basis points and constructs a probability tree across upcoming meetings. For example, suppose futures prices imply an expected policy-rate increase equivalent to five basis points, and the simplified model allows only a 25-basis-point hike or no change. Under those assumptions, a five-basis-point expected increase corresponds to approximately a 20% probability of a hike. Actual FedWatch calculations are more sophisticated, particularly when multiple meetings or outcomes are involved. Crucially, these probabilities are model-derived estimates rather than direct observations of individual traders' beliefs. How Do Polymarket and Kalshi Predict Fed Decisions? Polymarket Turns Policy Outcomes Into Tradable Contracts Unlike FedWatch, Polymarket allows participants to trade contracts linked to the occurrence of specific events. Federal Reserve markets typically offer outcomes such as a 25-basis-point increase, an unchanged rate, or a rate reduction at a particular meeting. Traders buy and sell contracts based on their own estimates of what the Fed will ultimately announce. A contract trading at $0.80 can be interpreted as having a market-implied probability of approximately 80%, before accounting for trading costs, bid-ask spreads, liquidity, and other market frictions. If the specified event occurs, a winning contract generally settles for $1; otherwise, it settles for zero. As participants respond to economic data and monetary policy announcements, prices adjust continuously through trading activity. The important distinction is that Polymarket directly prices an explicitly defined event outcome. Its probabilities are not extracted from an underlying interest rate futures contract. This makes the platform intuitive for users who want a straightforward answer to questions such as whether the Fed will raise rates at its next meeting, although the contract's resolution rules and available liquidity must still be examined carefully. Kalshi Offers Another Event-Based Market Kalshi operates a similar event-driven model, allowing participants to trade contracts tied to economic indicators, policy decisions, and other measurable outcomes. Its Federal Reserve markets provide an additional reference point for investors comparing expectations across prediction markets and traditional financial derivatives. Kalshi's September 30 market update is particularly revealing. After softer inflation data, the probability of a 25-basis-point October hike dropped to 33%, while maintaining rates became the leading outcome at 65%. These prices reflected actual market activity and evolving expectations rather than an official Federal Reserve forecast. Although Polymarket and Kalshi share event-based structures, their respective trading conditions, contract specifications, participant bases, and liquidity can differ. Consequently, the two platforms may display slightly different probabilities for apparently identical monetary policy outcomes. Investors should therefore examine what each market actually measures before interpreting those differences as evidence of superior forecasting ability. Prediction Markets vs FedWatch: What Are the Main Differences? Although prediction markets and CME FedWatch both provide probabilities for future Federal Reserve decisions, they rely on fundamentally different pricing mechanisms. Platforms such as Polymarket and Kalshi allow traders to buy and sell contracts tied to specific policy outcomes, including interest rate hikes, cuts, or pauses. Their probabilities are derived directly from market prices, reflecting how participants collectively assess the likelihood of an event. In contrast, CME FedWatch calculates probabilities using 30-Day Federal Funds futures prices and a mathematical framework that estimates potential changes in the federal funds target rate. This distinction means prediction markets directly price individual events, while FedWatch translates broader interest rate expectations into probabilities. Another important difference involves market participants, liquidity, and trading objectives. Prediction markets attract retail traders, professional speculators, and participants seeking exposure to specific economic outcomes. Their straightforward contract structures make Federal Reserve expectations relatively accessible, although prices can be influenced by limited liquidity, bid-ask spreads, and concentrated trading activity. CME FedWatch, meanwhile, draws information from the established federal funds futures market, where institutional investors, banks, and professional interest rate traders participate for hedging and speculation. While this market benefits from established financial infrastructure, its implied probabilities still depend on modeling assumptions and may be influenced by risk premia. These structural differences help explain why Polymarket, Kalshi, and CME FedWatch occasionally display different probabilities for the same Federal Reserve meeting. For example, a prediction market might assign a 20% probability to a 25-basis-point rate hike, while FedWatch indicates a 17% probability of an increase. Such differences do not necessarily mean one forecast is more accurate. They may reflect differences in contract definitions, observation times, liquidity, or probability calculations. Ultimately, prediction markets offer a direct way to interpret expectations about specific policy outcomes, while CME FedWatch provides a futures-based perspective on monetary policy. Comparing both can help investors develop a more comprehensive understanding of interest rate uncertainty. Why Can the Same Fed Decision Have Different Probabilities? Imagine that Polymarket prices a 25-basis-point October hike at 20%, while FedWatch indicates 17%. The three-percentage-point difference does not automatically mean one market is wrong. It might reflect different trading times, liquidity conditions, contract definitions, investor demand, or calculation methods. In addition, a market contract covering only an exact 25-basis-point increase is not necessarily comparable to FedWatch's probability of any rate hike. The latter can include multiple target-rate outcomes above the current range. Similarly, a contract predicting no change at the October meeting does not necessarily imply that the Fed will refrain from increasing rates later in the year. The most meaningful comparison therefore requires matching the same meeting date, monetary policy outcome, and observation time. Without those controls, apparent forecasting disagreements may simply result from comparing different financial instruments. Are Prediction Markets Faster Than Futures Markets? Prediction markets are sometimes described as more responsive because traders can express a view on a particular event without trading an underlying interest rate derivative. However, direct event pricing does not automatically translate into faster information processing. Federal funds futures are closely monitored by professional market participants, and both types of markets can respond rapidly to scheduled economic releases. The distinction becomes particularly important during unexpected inflation or employment announcements. A smaller event market may experience sharp probability changes when a few participants trade, while a highly liquid futures market might reflect a broader set of expectations. Conversely, an active prediction market could incorporate event-specific information that is less visible in broader interest rate instruments. Determining which market reacts faster requires synchronized historical data, preferably collected at intraday intervals. It is not enough to compare a Polymarket quote from the morning with a FedWatch estimate published several hours later. What Could Change the October Fed Decision? Inflation Data Remains the Biggest Near-Term Catalyst The next major test is the September Consumer Price Index report, scheduled for October 14, followed by the Producer Price Index on October 15. Both releases arrive before the October 27-28 FOMC meeting and could significantly influence expectations. Stronger-than-anticipated price growth may revive the case for further tightening, particularly if energy costs continue feeding into broader consumer prices. Conversely, another round of softer inflation figures could reinforce expectations that the Fed can keep rates unchanged while evaluating the impact of September's increase. Investors should pay attention not only to headline inflation but also to underlying price measures and the distinction between temporary supply-driven pressures and persistent demand-driven inflation. Employment Weakness and Energy Prices Create Competing Risks The Fed also faces uncertainty surrounding employment. September's weaker payroll growth suggested that labor market conditions were cooling, potentially reducing the urgency for additional rate increases. Nevertheless, a single monthly report cannot establish a sustained deterioration, especially when employment estimates are frequently revised. Meanwhile, geopolitical tensions and higher energy costs continue to create upside inflation risks. This combination makes monetary policy particularly difficult to anticipate: softer hiring favors caution, while persistent price increases support continued tightening. Prediction markets and FedWatch may respond differently as participants evaluate which risk will have the greater influence on policymakers. October and December Expectations Must Be Separated Another important factor is the distinction between the timing and the direction of monetary policy. A high probability of unchanged rates in October does not necessarily imply that the Fed has completed its tightening cycle. The September meeting minutes indicated that most policymakers still considered another increase during 2026 potentially appropriate, while financial markets increasingly focused on December as a possible date. The December 8-9 FOMC meeting will also include updated economic projections, giving investors additional information about the Fed's expected rate path. For this reason, traders should compare October and December probabilities separately instead of using a single meeting's forecast to characterize the entire policy outlook. What Do Fed Rate Predictions Mean for Bitcoin and Crypto? Federal Reserve expectations influence the broader Crypto market through several financial channels. Interest rates affect borrowing costs, Treasury yields, dollar demand, and investors' willingness to hold volatile assets. Because Bitcoin does not generate contractual interest income, changes in the attractiveness of interest-bearing investments can also affect its relative appeal. On October 7, Bitcoin fell to approximately $83,682 before trading near $84,303 in a Barron's market update. The report highlighted pressure from rising Treasury yields, dollar strength, and geopolitical developments affecting energy prices. These conditions demonstrate why a lower probability of an immediate Fed hike does not automatically translate into a higher Bitcoin price. Can Prediction Markets Help Crypto Traders? Prediction markets may provide useful contextual information by showing how participants interpret economic events. If the probability of another rate hike falls sharply after a weak employment report, crypto investors can compare that change with Bitcoin price movements, Treasury yields, and the dollar. However, the probability itself is not a technical trading signal or a guarantee of future price direction. The relationship is particularly complicated when inflation and growth risks move in opposite directions. A Fed pause caused by improving inflation may support risk appetite, while a pause driven by deteriorating economic activity could coincide with weaker demand for speculative assets. Similarly, a high probability of tighter monetary policy may already be reflected in cryptocurrency prices before the official decision. For readers following the cryptocurrency market and discussions on KuCoin Square, the most useful approach is to treat Fed probabilities as one part of a broader macroeconomic framework. Bitcoin prices, ETF flows, derivatives positioning, dollar liquidity, and market leverage remain important independent variables. Can Prediction Markets Really Beat FedWatch? There is no convincing basis to declare a universal winner from the recent October rate forecasts alone. Both systems showed that market expectations were changing, but they capture different forms of trading activity. Prediction markets offer direct event-based prices, while CME FedWatch derives its estimates from established interest rate futures. Their respective usefulness depends on the event being studied, market depth, observation period, and desired application. An appropriate performance comparison would examine many historical FOMC decisions using synchronized probabilities recorded at fixed intervals before each meeting. Researchers could assess forecast calibration, Brier scores, responsiveness to economic announcements, and the effects of transaction costs. It would also be important to distinguish forecasting a binary rate decision from forecasting the broader expected path of interest rates. Ultimately, prediction markets and FedWatch may be more valuable as complementary indicators than as direct competitors. When their probabilities converge, investors gain insight into a shared market consensus. When they diverge, the gap provides an opportunity to investigate contract design, liquidity, risk premia, and differences in participant expectations. Neither system eliminates uncertainty, and neither should be treated as an official Fed forecast. Conclusion The dramatic reversal in October 2026 rate-hike expectations illustrates how rapidly financial markets can absorb new economic information. Following September's rate increase, softer inflation data and weaker employment growth pushed both prediction markets and futures-based estimates toward an October pause, even as Federal Reserve officials continued discussing the possibility of additional tightening before year-end. The real value of comparing Prediction Markets vs FedWatch lies in understanding how different instruments price the same monetary policy uncertainty. As the October 14 CPI report and October 28 Fed decision approach, Polymarket, Kalshi, and CME FedWatch will remain important tools for monitoring market expectations. For cryptocurrency investors, these probabilities offer useful macroeconomic context, but their signals should be evaluated alongside broader financial conditions rather than interpreted as direct Bitcoin price predictions. FAQs Can anyone trade Fed interest rate predictions on Polymarket? Access depends on the user's jurisdiction, the platform's eligibility requirements, and the specific market. Before participating, users should check whether the relevant contracts are legally accessible and understand their settlement conditions. Does a 90% prediction market probability guarantee that an event will happen? No. It means the market price implies a high estimated likelihood, not certainty. Even well-calibrated forecasts should occasionally experience outcomes assigned a relatively low probability. Does CME FedWatch use artificial intelligence to predict interest rates? CME FedWatch primarily uses a defined methodology based on federal funds futures prices and assumptions about policy-rate changes. It is not an AI-generated forecast or a direct survey of economists. Can prediction market prices be manipulated? Thinly traded contracts may be vulnerable to temporary price distortions from large orders, coordinated activity, or limited liquidity. High trading volume alone does not prove that a market is accurately pricing an event. Are prediction market probabilities and betting odds the same? They both communicate implied likelihoods, but contract settlement, pricing, transaction costs, and regulatory structures can differ. An event contract trading at $0.60 is commonly interpreted as implying approximately a 60% probability before market frictions. Disclaimer: This content is for informational purposes only and does not constitute investment advice. Investments carry risk. Please do your own research (DYOR). ⚠ For information purposes only. Crypto carries risk. Not financial advice!
