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Kalshi Raises More Than $1B in New Funding Round 

By
Paul Ugbede Godwin
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0

Kalshi has raised more than $1 billion in a new funding round at a $22 billion valuation. This roughly doubles its previous valuation of around $11 billion from its December 2025 Series E round where it also raised $1 billion, led by Paradigm with participation from Sequoia, a16z, ARK Invest, and others.

The new round is led by Coatue Management.
It’s described as an ongoing or recently closed financing, with the $22B valuation reflecting strong investor enthusiasm for prediction markets. Kalshi’s annualized revenue run rate has reportedly surged to about $1.5 billion up significantly from earlier figures around $600-700M, which helps justify the aggressive valuation multiple roughly 14-15x revenue.

This comes amid booming interest in prediction platforms, though the sector faces ongoing regulatory scrutiny from the CFTC on certain contracts. At $22B, Kalshi’s valuation now exceeds the market caps of some established players in related spaces like sports betting.

This rapid growth trajectory—multiple massive rounds in quick succession—highlights how prediction markets have exploded in popularity, especially post-2024 election cycles and with broader event-based trading adoption.

Polymarket, the decentralized prediction market platform built on blockchain, has experienced explosive growth since its breakout during the 2024 U.S. election cycle. As of March 20, 2026, it remains a leader in the global prediction markets sector, though it faces intense competition from regulated U.S. rival Kalshi.

Trading Volume Surge: 2023: ~$73 million total. 2024: ~$9 billion (driven heavily by election-related bets, e.g., over $3.3 billion on Trump vs. Harris). 2025: Continued momentum with monthly highs like ~$3 billion in October and cumulative YTD figures exceeding $7-10 billion in later months. The platform pivoted strongly into sports, crypto, and other events. 2026 (early): Record-setting performance, including February’s all-time high monthly volume >$7 billion (7.5x YoY increase) and a single-day peak of $425 million on Feb 28 (surpassing 2024 election day highs).

Recent weekly volumes hit new ATHs around $2.1 billion+. Cumulative notional volume has reached tens of billions, with Polymarket often commanding 50%+ market share in the sector; combined with Kalshi ~79-85% of total prediction market activity. Recent U.S. operations via its 2025 acquisition of CFTC-regulated QCEX have seen >$761 million cumulative notional volume and over 5 million transactions.

Daily active wallets/users surged dramatically.
Over 1.3 million traders reported in late 2025, with ongoing expansion into mainstream via partnerships; MLB as official predictions partner, Dow Jones, DraftKings, NHL, and integrations like Golden Globes coverage.

Shift from crypto/politics focus to broader categories like sports, finance, culture, and AI/tech events. Total raised: ~$2.2-2.3 billion across multiple rounds; investors include Founders Fund, Polychain, General Catalyst, Vitalik Buterin, and a major $2 billion strategic investment from Intercontinental Exchange and NYSE owner in October 2025.

Valuation progression: Early 2025: ~$1.2 billion (unicorn status). October 2025: $9 billion post-ICE deal (Series D/strategic).
Late 2025/early 2026: Secondary/implied valuations climbed to ~$11-11.6 billion.
Mid-March 2026 reports: In early talks for new funding at ~$20 billion potentially doubling from late 2025 levels, amid sector-wide boom where Kalshi and Polymarket both eye similar marks.

This reflects massive investor enthusiasm for prediction markets as an information and forecasting layer, despite regulatory hurdles and competition. Fees have scaled with volume; estimates suggest potential annual revenue in hundreds of millions. Recent fee implementation and growth trends show weekly revenue climbing steadily.

Polymarket’s growth has been fueled by: Mainstream adoption beyond crypto natives.
Event-driven spikes. Regulatory progress (U.S. relaunch and phased rollout post-QCEX acquisition). Prediction markets overall quadrupled volume from 2024 to 2025 ~$64 billion in 2025, with continued momentum into 2026.

However, it’s now neck-and-neck with Kalshi which recently raised $1B+ at $22B valuation. Polymarket leads in global/decentralized volume and crypto integration, while Kalshi dominates regulated U.S. fiat access and certain liquidity metrics. Polymarket has transformed from a niche crypto tool into a major player pricing real-world probabilities, with valuations and volumes suggesting it could become even more central to information markets.

US Producer Price Index in February 2026 Came-in Hotter than Expected 

By
Paul Ugbede Godwin
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0

The US Producer Price Index (PPI) for February 2026 came in hotter than expected, signaling renewed inflationary pressures at the wholesale level.

According to the Bureau of Labor Statistics (BLS) release on March 18, 2026: The PPI for final demand rose 0.7% month-over-month significantly above economists’ consensus forecast of 0.3% and up from 0.5% in January.
On a year-over-year basis, the headline PPI accelerated to 3.4%, the fastest annual increase in a year since February 2025 and above expectations of around 2.9% matching January’s reading.

Core measures excluding more volatile components also surprised to the upside: Core PPI increased 0.5% MoM above the expected 0.3% and 3.9% YoY above forecasts of 3.7%, the highest in over a year. The BLS-preferred core (ex-food, energy, and trade services) rose 0.5% MoM and 3.5% YoY.

The monthly gains were broad-based: Final demand services rose 0.5% accounting for more than half the overall increase, driven by areas like traveler accommodations +5.7%, securities brokerage and investment services, and others. Final demand goods jumped 1.1%; the largest since mid-2023, led by food +2.4%, including sharp vegetable spikes, energy +2.3%, and other goods.

This hotter print complicates the Federal Reserve’s outlook, especially amid factors like Middle East tensions potentially boosting oil prices, tariff effects, and supply chain issues. It contributed to market reactions, including higher Treasury yields and a firmer US dollar initially, while reducing expectations for near-term rate cuts.

Year-over-year (YoY, unadjusted): 2.4%, unchanged from January and in line with economist expectations. Core CPI excluding food and energy: +0.2% MoM and 2.5% YoY steady from January. Major drivers: Shelter (+0.2% MoM, largest contributor), food (+0.4% MoM), energy (+0.6% MoM), with offsets from declines in used cars, communication, and others.

Food YoY: +3.1%; Energy YoY: +0.5%; Shelter YoY: +3.0%. This print was broadly as expected and indicated stable, moderate consumer-level inflation still above the Fed’s 2% target but not accelerating.

Significant gap; PPI core signals upstream pressure. Broad-based: Goods +1.1% (food +2.4%, energy +2.3%), Services +0.5%. Shelter, food and energy moderate; some declines offsetting. PPI shows sharper wholesale goods and services spikes. Higher yields, firmer dollar initially; reduced rate cut odds. PPI’s heat added more Fed caution amid external risks (e.g., oil tensions).

Producer prices often foreshadow consumer trends (as businesses pass on costs), so February’s hot PPI suggests potential upward pressure on future CPI readings—especially if factors like energy volatility from Middle East tensions or tariffs persist. Analysts noted possible March CPI upside from gasoline spikes, potentially pushing headline toward ~3.3% temporarily.

CPI’s stability supports gradual disinflation toward 2%, but the PPI surprise complicates it, feeding into the Fed’s preferred PCE gauge which typically runs cooler than CPI. This contributed to market repricing of near-term rate cuts lower after PPI. PPI captures wholesale/producer level including trade services, while CPI measures retail/consumer experience. The gap highlights building cost pressures not yet fully hitting households.

The PPI’s strength contrasts with the cooler February CPI, highlighting a widening upstream-downstream gap. PPI often leads CPI. The broad-based PPI surge; goods +1.1%, services +0.5%, core measures at multi-year highs signals building pressures that could lift future CPI/PCE readings—especially if energy volatility persists or tariffs continue filtering through.

Economists revised February PCE estimates higher, with core potentially sticky. This feeds the Fed’s preferred gauge, adding upside risk to disinflation progress toward 2%. PPI captures producer/wholesale levels including trade services, while CPI reflects retail and consumer experience. The current gap suggests costs are accumulating in supply chains but not yet fully hitting households—potentially temporary if one-off, but worrisome if persistent.

The next CPI release (March 2026 data) is April 10, 2026. Watch for any spillover from PPI’s strength, particularly in goods and energy components. This data feeds into the Fed’s preferred PCE inflation gauge, with February PCE estimates now incorporating some upward pressure.

DoorDash turns Gig Workers into AI data Engines with New “Tasks” app

By
Samuel Nwite
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0

DoorDash is quietly redrawing the boundaries of the gig economy, launching a standalone “Tasks” app that converts its delivery workforce into a scalable pipeline for artificial intelligence training data—an asset increasingly viewed as more strategic than compute power itself.

At its core, the initiative is viewed as part of a broader structural shift in how AI systems are developed. While much of the first wave of generative AI relied on scraping vast amounts of internet text, the next phase—particularly robotics, autonomous systems, and “agentic” AI—requires grounded, real-world data. DoorDash’s network of millions of couriers offers precisely that: human-labeled, context-rich inputs generated in uncontrolled, everyday environments.

The Tasks app operationalizes this advantage. Couriers are paid to complete structured assignments—filming routine activities, capturing physical environments, or recording speech—which are then used to train models that need to interpret the physical world with high accuracy. The company says the data will support both its internal systems and those of external partners across industries, positioning DoorDash as a data infrastructure provider rather than just a logistics platform.

This evolution mirrors moves by rivals such as Uber, which has begun testing similar micro-task programs. The convergence points to a broader recalibration in the gig economy: platforms are no longer just intermediaries for labor and demand, but are becoming critical suppliers in the AI value chain.

What distinguishes DoorDash’s approach is its ability to integrate data collection directly into existing workflows. Tasks are embedded within the Dasher app, alongside delivery jobs, allowing the company to gather hyper-local, real-time data at minimal additional cost. This creates a feedback loop—data collected from the field can immediately improve route optimization, mapping accuracy, and customer experience, while also feeding longer-term AI development.

Analysts say this dual-use model could materially improve margins over time. Delivery remains a low-margin business, heavily exposed to fuel costs, labor incentives, and competition. Data, by contrast, scales with far higher profitability. If DoorDash can successfully package and sell AI training datasets—or embed them into higher-value enterprise services—it could open a new revenue stream less sensitive to the cyclical pressures of consumer spending.

It comes at a time when demand for high-quality training data is surging as companies like OpenAI and Google push toward more autonomous systems capable of acting, not just responding. These systems require “ground truth” data—accurate representations of real-world conditions—to function reliably. Synthetic data can fill gaps, but it often lacks the unpredictability and nuance of human environments.

DoorDash’s network effectively becomes a distributed sensor layer, capturing edge cases that are critical for AI performance. For example, variations in lighting, object placement, human behavior, or language accents—factors that are difficult to simulate—can be systematically recorded and fed into training pipelines.

There is also a geopolitical and competitive dimension. As governments tighten restrictions on cross-border data flows and companies guard proprietary datasets, access to unique, internally generated data is becoming a key differentiator. DoorDash’s model allows it to build such a dataset organically, without relying on third-party sources that may be restricted or commoditized.

However, the strategy introduces new tensions around labor and data ownership. Couriers are effectively producing high-value digital assets, yet are compensated on a per-task basis with no ongoing claim to the downstream value created. As AI models trained on this data generate revenue, questions around fair compensation, data rights, and transparency are likely to intensify—echoing earlier debates over how social media platforms monetized user-generated content.

There are also privacy and regulatory considerations. Tasks involving video, audio, or location data raise potential concerns about consent, data storage, and usage, particularly as the app expands into new markets with stricter data protection regimes.

Still, for DoorDash, the upside is clear. By leveraging an existing workforce to solve one of AI’s most expensive bottlenecks—data acquisition—the company is effectively lowering the barrier to entry for itself and its partners in the AI ecosystem.

The rollout remains limited to select U.S. markets, but the model is inherently global. With operations spanning multiple countries, DoorDash could eventually replicate the system internationally, creating one of the largest human-in-the-loop data networks in the world.

From Entertainment to Ecosystems: When Online Casinos Start Feeling Like Places

By
TI Partners
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0

You can tell something has changed the moment you log in and hesitate.

Not because you’re unsure what to play—but because there’s already something happening. A chat scrolling. A tournament ticking down. Someone celebrating a win you didn’t see but somehow feel part of. It’s subtle, but it shifts the mood entirely.

Online casinos used to feel like vending machines. Insert money, pull lever, hope for magic. Now they feel more like late-night cafés. A bit noisy. A bit alive. You don’t always know who’s there, but you’re aware you’re not alone.

And that changes everything.

Loyalty Isn’t Points Anymore—It’s Identity

The old loyalty system was painfully dull. Spend money, earn points, exchange for something mildly useful. It had all the charm of a supermarket receipt.

Now? It’s closer to a role-playing game.

You climb tiers. You unlock things that sound almost dramatic—“VIP status,” “exclusive tables,” “private tournaments.” The platform begins to treat you differently, and more importantly, you begin to feel different.

There’s a quiet psychological trick at play. You’re no longer just a player. You’re someone with a rank. A history. A kind of digital reputation, even if it’s only visible through badges and perks.

And once you have that, walking away feels… slightly wasteful. Like abandoning progress in a game you didn’t realize you were playing.

The Chat Box: Chaos, Comfort, and Unexpected Humanity

Spend five minutes in a live dealer room and watch the chat. It’s a strange mix.

Someone complains about losing five hands in a row. Someone else drops a joke that barely lands. Another player types “good luck everyone” like a ritual before the spin. It’s messy, repetitive, sometimes absurd—but also weirdly comforting.

Because it feels real.

You start recognizing patterns. Not full personalities, not quite friendships, but fragments. A username you saw yesterday. A tone you remember. The digital equivalent of nodding at the same stranger on your morning commute.

And suddenly, you’re not just playing against odds. You’re sharing a moment.

It’s easy to underestimate how powerful that is.

Tournaments: The Moment the Room Wakes Up

Then come the tournaments, and everything speeds up.

The quiet, individual rhythm of play gets replaced by something more collective. There’s a clock. A leaderboard. A sense that everyone is pushing at the same time, even if you never speak to each other.

It’s not just about money anymore. It’s about position.

You glance at the rankings more often than you’d like to admit. You calculate. You chase. You stay a little longer than planned because you’re “almost there.” And even when you’re not, it still feels like you’re part of an event rather than just passing time.

There’s a kind of electricity in that shared urgency. You don’t get it when you’re alone.

Small Social Features, Big Subtle Impact

What’s interesting is how none of this feels forced.

There’s no loud announcement saying, “Welcome to the community.” It just… builds itself around you.

A few things quietly doing their job:

Public win notifications that make big moments visible

Referral bonuses that turn friends into participants

Group challenges that nudge players toward shared goals

Tiny interactions that accumulate into familiarity

Individually, they’re nothing special. Together, they create an atmosphere.

You start to feel like you’re returning to a place, not just opening a website.

Somewhere in the Middle, TonyBet Gets It Right

Around the middle of this shift, platforms like TonyBet seem to understand the assignment without making a big show of it.

The tonybet casino experience does not yell out to the community, it just has one created about you. You can see it in the way tournaments are designed, and the interface pushes the interaction without imposing it, and the perks of loyalty are more like rewards than ones that are arbitrary.

A restraint there is of a sort. It does not have anything that is too engineered, yet it is obvious. And that is likely the reason that it works. You are not instructed to study it–you just get to doing it.

Almost by accident.

When a Platform Begins to Seem Like Home

Here is where it becomes interesting.

Since familiarity is a mighty thing. When you become aware of a space, its beat, its inhabitants, its minor peculiarities, you start to cling to it, which you never thought possible.

You log in not because you feel like playing but because you are curious:

Who’s online right now?

Is it still the same tournament going on?

Did anyone hit something big?

They are minor questions, though they make you go back.

And gradually, the platform ceases to be fungable. It is made your place, though you may well never have made a conscious choice of that.

The Slightly Uncomfortable Truth

Something a little disturbing here is the smoothness of this.

This is due to the fact that there is a thin boundary between entertainment and immersion. When a platform is animated, abandoning it is not similar. No longer closing a tab, it is leaving a space in which in some little sense you have begun to be acknowledged.

That’s not necessarily bad. It is able to make it more enjoyable, less mechanical.

But it’s worth noticing.

The more human something is the less one can remember that it is designed.

So What Are We Actually Doing Here?

Maybe the better question isn’t about gambling anymore.

Maybe it’s about presence.

You log in for a game, sure. But you stay for something harder to define. A mix of noise, interaction, progress, and familiarity that makes the experience feel… inhabited.

Not quite a community in the traditional sense. Not quite a game either.

Something in between.

And once you’ve felt that shift—even briefly—it’s hard to go back to the old version, where it was just you, a screen, and silence.

Xiaomi Unveils Hunter Alpha as Early Test Build of MiMo-V2-Pro, Confirming Chinese Origin After Viral Speculation Linked It to DeepSeek V4

By
Samuel Nwite
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0

Xiaomi Corp. confirmed on Wednesday that the mysterious AI model Hunter Alpha — which appeared anonymously on the OpenRouter platform on March 11 and quickly sparked widespread speculation it was a stealth test of DeepSeek’s anticipated next-generation system — is in fact an early internal test build of Xiaomi’s flagship MiMo-V2-Pro model.

The announcement came from Xiaomi’s AI model team MiMo, led by former DeepSeek researcher Luo Fuli. In an X post on Thursday, Luo described the rapid shift from chat-based to agentic AI paradigms as a “quiet ambush” that caught even her team off guard.

“People ask why we move so fast. I saw it firsthand building DeepSeek R1,” Luo said.

MiMo-V2-Pro is positioned as the “brain” for advanced AI agents capable of executing complex, multi-step tasks with minimal human prompting and supervision — a significant leap beyond traditional chatbots. The model will partner with five major agent frameworks, including the viral open-source OpenClaw, offering developers worldwide a week of free access upon launch.

Xiaomi’s Hong Kong-listed shares surged as much as 5.8% on Thursday, reflecting investor enthusiasm for the company’s aggressive push into frontier AI capabilities.

Hunter Alpha’s Viral Emergence and Speculation

Hunter Alpha surfaced without attribution on OpenRouter, a popular AI gateway aggregating dozens of models, and was initially described by the platform as a “stealth model.” Reuters reported that during its testing last week, the chatbot identified itself as “a Chinese AI model primarily trained in Chinese,” with knowledge extending to May 2025 — the same cutoff reported for DeepSeek’s models.

When pressed on its creator, it replied: “I only know my name, my parameter scale and my context window length.”

The profile advertised a 1-trillion-parameter scale and a context window of up to one million tokens — specifications that fueled speculation it was an early preview of DeepSeek-V4, which Chinese media had rumored could launch as soon as April. The combination of massive scale, long context, strong reasoning, and free access drove rapid adoption: Hunter Alpha surpassed one trillion tokens in total usage and topped OpenRouter leaderboards, according to MiMo.

Independent AI benchmark tester Umur Ozkul noted the speculation linking it to DeepSeek was understandable given the timing and advertised capabilities.

Nabil Haouam, an engineer building AI agent systems, highlighted the standout combination, saying: “Hunter Alpha’s 1-million-token context paired with reasoning capability and free access. Most frontier models with that context window come with real cost at scale.”

Stealth Launches as Industry Practice

Anonymous or “stealth” model releases on platforms like OpenRouter are increasingly common, allowing developers to gather unbiased feedback before official announcements. A similar pattern occurred in February when Pony Alpha appeared anonymously before Zhipu AI confirmed it as part of its GLM-5 system five days later.

Hunter Alpha’s profile included a standard notice that all prompts and completions are logged by the provider and may be used to improve the model — a routine disclosure in the industry.

Xiaomi’s confirmation positions MiMo-V2-Pro as a direct competitor to frontier agentic systems from OpenAI, Anthropic, Google, and DeepSeek. The company has aggressively expanded its AI efforts in recent years, integrating generative capabilities into smartphones, IoT devices, and its growing ecosystem of apps and services.

The announcement aligns with China’s broader push to lead in agentic AI — systems that reason, plan, and act autonomously — amid intense domestic competition and U.S. export restrictions on advanced chips. OpenClaw’s viral adoption in China earlier this year demonstrated strong local demand for accessible, messaging-integrated agents, further accelerating investment in the space.

Xiaomi’s stock surge is seen as a reflection of investor optimism that MiMo-V2-Pro — backed by the company’s hardware ecosystem and global distribution reach — could carve out a significant position in the fast-evolving agentic AI market. The free developer access period and framework partnerships are seen as an indication of an aggressive go-to-market strategy aimed at rapid ecosystem adoption.

However, the revelation ends weeks of speculation while underscoring the speed at which Chinese AI labs are closing the gap with Western frontrunners. As agentic systems move from experimental to mainstream, the race for compute, data, talent, and developer mindshare is intensifying — with Xiaomi’s entry adding another formidable contender to an already crowded field.

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