TLDR Meta revealed a roadmap of four in-house AI chips under its MTIA program The first chip, MTIA 300, is already live powering ranking and recommendation systemsTLDR Meta revealed a roadmap of four in-house AI chips under its MTIA program The first chip, MTIA 300, is already live powering ranking and recommendation systems

Meta Stock: Company Reveals Custom AI Chip Plans as Data Center Expansion Accelerates

2026/03/11 22:17
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TLDR

  • Meta revealed a roadmap of four in-house AI chips under its MTIA program
  • The first chip, MTIA 300, is already live powering ranking and recommendation systems
  • The remaining three chips will roll out through 2027, with the final two focused on AI inference
  • Meta plans six-month release intervals to keep pace with rapid data center expansion
  • Capital spending is projected at $115–$135 billion in 2026, with Broadcom and TSMC involved in production

Meta unveiled its roadmap of four new in-house AI chips on Wednesday, as the company pushes to expand its infrastructure at pace with surging AI demand.

The chips are part of Meta’s Meta Training and Inference Accelerator (MTIA) program. The first chip, the MTIA 300, is already deployed and powers Meta’s ranking and recommendation systems across its platforms.


META Stock Card
Meta Platforms, Inc., META

The remaining three chips — the MTIA 400, 450, and 500 — will be released across the rest of 2026 and into 2027. The final two are designed specifically for inference workloads.

Inference is the process by which an AI model responds to user queries — the part users actually experience. It’s a different, and increasingly critical, workload compared to training large models from scratch.

Meta has had some wins with inference chips before. Training chips, though, have been a tougher nut to crack. The company has long aimed to build a generative AI training chip but hasn’t fully cracked it yet.

Starting with the MTIA 400, Meta has designed an entire server system around the chip — roughly the size of several server racks — and includes liquid cooling. That’s a step up from just designing a processor in isolation.

Why Meta Is Building Its Own Chips

Custom chips let Meta optimize for its own workloads rather than relying entirely on general-purpose processors. The payoff? Lower energy use and better cost efficiency at scale.

That said, Meta isn’t going fully DIY. The company contracts Broadcom (AVGO) to help design certain elements, and uses Taiwan Semiconductor Manufacturing Co (TSMC) to fabricate the final processors.

In February, Meta also signed large deals with Nvidia (NVDA) and AMD (AMD) to purchase tens of billions of dollars worth of chips — so off-the-shelf hardware remains part of the mix.

Meta’s Spending Plans

Meta said in January that it expects capital expenditure of between $115 billion and $135 billion in 2026. That’s a substantial commitment to infrastructure and underlines why in-house chip design matters — at that spending level, even marginal efficiency gains translate to real money.

The six-month cadence for new chip releases reflects both the pace of Meta’s build-out and the urgency it sees around AI infrastructure. Song confirmed the rollout schedule is tied directly to how fast the company is expanding its data center footprint.

The MTIA 450 and 500 — the final two chips in this current roadmap — are slated for 2027 and are squarely aimed at inference, the workload Meta says is seeing the most rapid growth right now.

Meta stock (META) was up 0.17% on Wednesday as the announcement was made.

The post Meta Stock: Company Reveals Custom AI Chip Plans as Data Center Expansion Accelerates appeared first on CoinCentral.

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Author: G3ronimo Compiled by: TechFlow HyperLiquid has grown into a mature crypto-native exchange, with the majority of its net fees programmatically distributed directly to token holders through an "Assistance Fund" (AF). This design makes $HYPE one of the few tokens capable of being valued based on cash flow. To date, most valuations of HyperLiquid have relied on traditional multiples, comparing it to established financial platforms like Coinbase and Robinhood, using EBITDA or revenue multiples as a reference. Unlike traditional corporate stocks, where management typically retains and reinvests earnings at their discretion, HyperLiquid systematically returns 93% of transaction fees directly to token holders through a support fund. This model creates predictable and quantifiable cash flows, making it well-suited for detailed discounted cash flow (DCF) analysis rather than static multiple comparisons. Our methodology begins by determining $HYPE's cost of capital. We then invert the current market price to determine the market-implied future earnings. Finally, we apply growth projections to these earnings streams and compare the resulting intrinsic value to today's market price, revealing the valuation gap between current pricing and fundamental value. Why choose discounted cash flow (DCF) over a multiple? While other valuation methods compare HyperLiquid to Coinbase and Robinhood via EBITDA multiples, these methods have the following limitations: The difference between the corporate and token structures: Coinbase and Robinhood are corporate stocks, whose capital allocation is guided by the board of directors, and profits are retained and reinvested by management; while HyperLiquid systematically returns 93% of trading fees directly to token holders through a relief fund. Direct Cash Flow: HyperLiquid's design generates predictable cash flows that are well-suited to DCF models, rather than static multiples. Growth and risk characteristics: DCFs are able to explicitly model different growth scenarios and risk adjustments, whereas multiples may not adequately capture growth and risk dynamics. Determining an appropriate discount rate To determine our cost of equity, we start with reference data from the public market and adjust for cryptocurrency-specific risks: Cost of equity (r) ≈ Risk-free rate + β × Market risk premium + Crypto/illiquidity premium Beta Analysis Based on regression analysis with the S&P 500: Robinhood (HOOD): Beta of 2.5, implied cost of equity of 15.6%; Coinbase (COIN): Beta of 2.0, implied cost of equity of 13.6%; HyperLiquid (HYPE): Beta is 1.38 and the implied cost of equity is 10.5%. At first glance, $HYPE appears to have a lower beta, and therefore a lower cost of equity than Robinhood and Coinbase. However, the R² value reveals an important limitation: HOOD: The S&P 500 explains 50% of its returns; COIN: The S&P 500 explains 34% of its return; HYPE: The S&P 500 only explains 5% of its returns. $HYPE’s low R² suggests that traditional stock market factors are insufficient to explain its price fluctuations, and crypto-native risk factors need to be considered. risk assessment Despite $HYPE’s lower beta, we still adjust its discount rate from 10.5% to 13% (which is more conservative compared to COIN’s 13.6% and HOOD’s 15.6%) for the following reasons: Lower governance risk: Direct programmatic distribution of 93% of fees reduces concerns about corporate governance. In contrast, COIN and HOOD do not return any earnings to shareholders, and their capital allocation is determined by management. Higher Market Risk: $HYPE is a crypto-native asset and is subject to additional regulatory and technological uncertainties. Liquidity considerations: Token markets are generally less liquid than established stock markets. Get the Market Implied Price (MIP) Using our 13% discount rate, we can reverse engineer the market’s implied earnings expectations at the current $HYPE token price of approximately $54: Current market expectations: 2025: Total revenue of $700 million 2026: Total revenue of $1.4 billion Terminal growth: 3% annual growth thereafter These assumptions yield an intrinsic value of approximately $54, which is consistent with current market prices. This suggests that the market is pricing in modest growth based on current fee levels. At this point we need to ask a question: Does the market-implied price (MIP) reflect future cash flows? Alternative growth scenarios @Keisan_Crypto presents an attractive 2-year and 5-year bull market scenario. 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