Microsoft Build 2026 In-Depth Analysis: In-House Chip Maia 200, MAI Model and Azure’s Cost Counterattack, and Ackman’s Long-Term Logic for a Large Position in Microsoft

Over the past six months, the software sector has undergone a violent re-rating known by the market as the "SaaSpocalypse." The core concern is singular: whether generative AI will bypass and replace traditional software companies that rely on subscription models, thereby structurally destroying their business models. According to financial media estimates, global software stocks lost approximately $2 trillion in market capitalization during this period alone. However, the recently concluded earnings season, along with the Microsoft Build 2026 conference in San Francisco in early June, is pushing the narrative in the opposite direction. This article explores three questions: whether AI is disrupting software or injecting new growth momentum; why Microsoft became the most misunderstood case this year despite software's resilience; and whether Microsoft's performance at Build 2026 is sufficient to address market concerns over its massive capital expenditures.

1. Earnings Debunk "Software is Dead": The Software Sector Rekindled by AI

The first to shake the "Software is Dead" narrative was a batch of the latest quarterly earnings reports. Team collaboration software vendor Atlassian (TEAM) is the most representative example of a reversal. Its third quarter of fiscal year 2026 (ended March 31) total revenue reached $1.787 billion, up 32% year-over-year; cloud business revenue was $1.132 billion, up approximately 29%; and non-GAAP earnings per share recorded $1.75, significantly higher than market expectations. Atlassian's co-founder and CEO directly addressed the market's pessimism in a letter to shareholders, stating, "Some believe software is dead, but I am convinced AI is a tailwind for Atlassian," and emphasized in a CNBC interview that the concerns weighing on software stocks are not reflected in the company's actual numbers.

This growth is not an isolated case but a common signal across multiple software sub-sectors. Data cloud vendor Snowflake (SNOW) saw its product revenue reach $1.33 billion in the first quarter of fiscal year 2027, up 34% year-over-year, with a growth rate that has continued to accelerate from previous quarters, setting a record for the highest single-quarter revenue increment in the company's history; shares surged over 30% in a single day after the report, and the company simultaneously announced a five-year cloud agreement with AWS worth approximately $6 billion. Digital workflow platform ServiceNow (NOW) reported subscription revenue of $36.71 billion in the first quarter of 2026, up 22% year-over-year, surpassing the upper limit of its performance guidance and raising its full-year outlook; the number of "million-dollar contract value customers" for its generative AI suite, Now Assist, increased by more than 130% year-over-year, with management stating that AI's growth exceeded the company's own expectations. Infrastructure observability vendor Datadog (DDOG) reported first-quarter revenue of $1.006 billion, up 32% year-over-year, marking the company's first-ever quarter with revenue exceeding $1 billion, and growth is still accelerating. Even Twilio (TWLO), a cloud communications platform positioned as underlying communication infrastructure, saw first-quarter revenue grow 20% year-over-year, its fastest growth rate since 2022.

After Atlassian raised its financial guidance, market sentiment recovered significantly, leading to a collective rebound in the stock prices of peers like Salesforce and ServiceNow, as pessimistic expectations for the entire software sector were recalibrated. In other words, the impact of AI on the software industry, at least at the current stage, is closer to a "demand amplifier" than a "demand replacer." This assessment provides an industry-level context for understanding Microsoft's situation: as the software sector as a whole warms up, Microsoft, as the leader in AI software, should have been the strongest performer in this recovery; however, the opposite was true.

2. Microsoft's Paradox: Misread Earnings and Capital Expenditure Panic

Since the beginning of 2026, Microsoft's stock performance has noticeably lagged behind other tech leaders. The stock price fell by about 23% at one point in the first quarter, marking its worst single-quarter performance since the 2008 financial crisis; at the end of that quarter, the stock hit a local low of approximately $356. A popular misinterpretation in the market is attributing the stock's weakness directly to poor performance, but this inference is inconsistent with the financial data.

According to Microsoft's fiscal year 2026 third-quarter earnings report, the company's revenue, profit, and cloud growth all exceeded Wall Street's consensus expectations. Total revenue for the quarter was $82.89 billion, up 18% year-over-year; operating profit was $38.4 billion, up 20%; and GAAP diluted earnings per share were $4.27, higher than the $4.06 expected by the market. Intelligent Cloud revenue was $34.7 billion, up 30% year-over-year, with the core Azure cloud service growing 40%, further accelerating from 31% in the same period last year. At the application layer, the annualized revenue run rate of Microsoft's AI business has exceeded $37 billion, a year-over-year increase of 123%; Remaining Performance Obligation (RPO), a measure of contracts on hand, reached $627 billion, up 99% year-over-year, nearly doubling; paid seats for Microsoft 365 Copilot exceeded 20 million, with the net addition rate of seats growing 250% year-over-year, the fastest since the product's launch. The only relatively weak part of the entire earnings report was the More Personal Computing segment, which recorded a year-over-year decline of about 1%.

Since performance does not constitute a reason to sell, the market's real concern is Microsoft's capital expenditure (CapEx) pace. Management's guidance for revenue and operating margins for the next quarter was slightly below market expectations; more crucially, the company expects capital expenditures next quarter to exceed $40 billion and guided that by the end of calendar year 2026, annual capital expenditures will reach approximately $190 billion. Such a massive investment stems, on the one hand, from rising costs of key components like memory and, on the other hand, from Microsoft's high-intensity expansion of AI infrastructure. The resulting contradiction is that the market does not disbelieve in Microsoft's AI capabilities but rather fears that an expansion model characterized by "massive upfront capital investment with returns not yet fully realized" will suppress margins and free cash flow in the short term.

Notably, entering the second quarter, as earnings sentiment settled, AI optimism warmed up, and "SaaSpocalypse" fears eased, Microsoft's stock price has recovered from its lows. Before the Build conference, Microsoft and Nvidia jointly launched the RTX Spark chip for AI PCs and released new hardware products like the Surface Laptop Ultra, further restoring market confidence; the stock price once returned to about $460 in early June. However, after the Build keynote on June 2, there was significant profit-taking in the market, with Microsoft falling about 4% to 5% on June 3, returning to around $438, corresponding to a market capitalization of approximately $3.28 trillion and a P/E ratio of about 27x.

The overall stance of sell-side institutions remains bullish. Among dozens of analysts covering Microsoft, the vast majority give "Buy" or "Strong Buy" ratings, with an average 12-month target price of approximately $560 to $570. High-end targets range from $650 from Morgan Stanley and Wells Fargo to $680 from Tigress Financial; however, more cautious voices exist—Stifel lowered its target price to about $415 in February, at the lower end of the current coverage range. Morgan Stanley, in a research report using a "revenue per megawatt" framework, suggested that the market might be underestimating the long-term revenue potential of the Azure AI business converted from capital expenditures—this effectively reframes the "cash-burning" narrative from the liability side to the asset side.

3. The Choice of Smart Money: Ackman's Long-term Logic of "Abandoning Google for Microsoft"

Before analysts collectively raised their ratings, Bill Ackman, founder of the hedge fund Pershing Square, had already built a position at low levels with real money. According to his latest 13F filing, Microsoft was the only new position established by Pershing Square in the first quarter of 2026. Ackman bought approximately 5.65 million shares in one go, quickly making Microsoft the fund's fourth-largest holding, accounting for 15.3% of the total portfolio; the market value of this position at the end of the first quarter was approximately $2.09 billion, and with the stock price rebounding, it had appreciated to about $2.3 billion by mid-May.

Looking at the investment logic, Ackman began buying in February during the period when Microsoft's stock price plunged due to market panic following its earnings release, with a forward P/E ratio of about 21x at the time of purchase. He stated publicly that this valuation was roughly in line with the overall market level but significantly lower than Microsoft's average trading valuation in recent years, representing a rare "discount" window. In stark contrast to buying Microsoft, he slashed his holdings in Alphabet in the same quarter in a near-total liquidation: Class A shares were reduced from about 678,000 to about 32,000, and Class C shares were reduced from over 6.1 million to about 312,000. This extreme rotation of "abandoning Google for Microsoft" is seen as a genuine sample of the institutional capital trend. Ackman likened this investment to his past operations of building heavy positions in Amazon, Meta, and Alphabet when market doubts about "AI competition and spending" were deepest; the implicit judgment is that when the market's skepticism about a company's "spending without returns" reaches a peak, it is often the point where valuation is most attractive.

This is not a short-term trading signal. Microsoft appears not only in Pershing Square's flagship fund but also as a core holding of Pershing Square USA, its closed-end fund listed on the New York Stock Exchange in April this year. Allocating the same investment thesis to two different fund vehicles simultaneously reflects Ackman's high degree of conviction in Microsoft's long-term AI monetization capabilities. It should be emphasized that institutional holding trends only serve as a reference and cannot replace independent judgment; their true value lies in prompting us to examine the underlying asset he is betting on—Microsoft's AI strategic positioning, which is exactly what Build 2026 attempted to answer directly.

4. Build 2026's Cost Counter-Attack: Self-Developed Chips, In-House Models, and Platform Moats

This year's Build lasted about two and a half hours, with more than thirty products released. Stripping away the details from an investment perspective, the entire keynote can be condensed into one sentence: Microsoft wants to "provide a complete computer for AI agents." CEO Satya Nadella deconstructed this architecture into five layers: compute, models, context, tools, and the top layers of runtime and safety governance. The strategic intent is not to create a "more useful Copilot button" but to transform Windows, Azure, GitHub, and Microsoft 365 into the operating system for the AI agent era. For the capital market, the most valuable aspect of this conference is that it directly addresses the aforementioned "cash-burning panic" through three threads: self-developed chips, in-house models, and platform moats.

The first thread is self-developed chips and cost efficiency, which directly corresponds to market questions about out-of-control capital expenditures. Microsoft confirmed that its Maia 200 self-developed AI inference chip, released in January 2026, has entered mass production and has begun to support the operation of Microsoft 365 Copilot. Regarding cost, a professional clarification is needed here: the written statement on Microsoft's official blog is that the "performance per dollar" of Maia 200 is approximately 30% higher than the latest generation of hardware in Microsoft's own fleet, without naming specific competitors; Nadella's version in the earnings call was also based on the "latest silicon in the fleet" and mentioned that the chips have been deployed in data centers in Arizona and Iowa; while Guthrie gave a broader version in a promotional video, claiming Maia is "30% cheaper" than any other AI chip on the market. The difference in the two versions lies in the benchmark; this article prefers to follow the written and earnings call versions. The accompanying release of the next-generation Cobalt 200 processor, oriented toward agent workloads, was described in the official Azure blog as achieving a generational performance improvement of up to 50% compared to the previous Cobalt 100, with cloud database workloads increasing by up to 135%, web services by up to 40%, and communication encryption by up to 45%.

Microsoft defines its core internal metric as "how many tokens can be produced per watt and per dollar." According to its disclosures, the latest MAI model trained on the self-developed Maia 200 chip has further improved end-to-end power efficiency by about 1.4 times. Goldman Sachs analysts pointed out in a related research report that self-developed chips not only reduce Microsoft's dependence on Nvidia but also help improve the gross margin of the Azure AI business. It should be clarified that the aforementioned performance and cost data currently come mainly from Microsoft's official channels and lack comprehensive testing from authoritative third-party institutions; therefore, they should be viewed as "official claims" rather than independently verified conclusions.

The second thread is the formation of in-house models, which means Microsoft is "no longer just a distribution channel for OpenAI." This conference released seven MAI in-house models at once, with the most attention on Microsoft's first reasoning model, MAI-Thinking-1. This model has 35 billion active parameters, a 256K context window, adopts a sparse Mixture-of-Experts (MoE) architecture, and emphasizes being trained entirely from scratch without any knowledge distillation, using cleaned and commercially licensed enterprise-grade data. Microsoft claimed in its official blog that in blind tests, independent evaluators preferred this model over Claude Sonnet 4.6, and its performance on the SWE-Bench Pro coding test was at the same level as Claude Opus 4.6. Approaching the capabilities of top-tier large models with a medium-sized, low-token-cost model is Microsoft's core selling point for enterprise customers. Additionally, MAI-Code-1 for lightweight coding has been integrated into Copilot and VS Code, while vision, voice, and transcription models have entered PowerPoint, OneDrive, and GitHub Copilot, respectively; these models are simultaneously listed on third-party platforms such as OpenRouter and Fireworks, no longer locked within Microsoft's own ecosystem. The significance of this shift is that Microsoft, long seen as the "enterprise outlet for OpenAI," is building its own model stack, thereby enhancing its bargaining power and cost control. It should also be noted that the above benchmark results were announced by Microsoft itself and are yet to be replicated by third parties.

The third thread is Microsoft's most proficient strategy—platform and moat: no matter which company's model ultimately wins, Microsoft will earn revenue from it. Currently, the selection of models in Microsoft's Foundry model catalog has exceeded 12,000; in addition to its own MAI models, the latest models from OpenAI and Anthropic (including Claude Opus 4.8) are also included. This multi-model strategy means that regardless of which model enterprise customers choose, Microsoft can stably earn revenue at the platform and cloud infrastructure levels. To lower the barrier for enterprise deployment, Microsoft also launched Agent 365, an agent permission console, and combined it with the networking layer Web IQ and the enterprise-oriented fine-tuning tool Frontier Tuning, packaging AI agents into standard infrastructure that "enterprises dare to deploy and IT departments can control and manage." The commercial logic is that Microsoft does not need to have the world's strongest model in every generation, as long as it firmly holds the entry point for scenarios where "AI agents complete work for enterprises and generate data as a result"—this is highly consistent with the underlying judgment that Ackman is willing to bet on for the long term.

The conference also disclosed several developments that are not core investment theses but possess long-term imaginative potential. In terms of hardware, Microsoft launched the Surface Laptop Ultra, which uses Nvidia chips and supports up to 128GB of unified memory, as well as a developer device capable of running models with 120 billion parameters locally; in terms of software, it announced that Copilot will be integrated as a resident agent, Microsoft Scout, this summer; in the scientific frontier, Microsoft released the Majorana 2 quantum chip, which it claims has a thousand-fold improvement in reliability, and launched the scientific research agent Microsoft Discovery, although data related to Majorana 2 remains controversial in academic circles.

5. Four Layers of Fundamental Transmission: From "Cost" to "Gross Margin"

Beyond product details, what is more worthy of long-term investors' attention is how these strategies transmit to Microsoft's future financial fundamentals. This transmission chain can be roughly divided into four layers.

On the cost side, the reason AI computing power suppresses gross margins is rooted in the high premiums previously paid to procure Nvidia GPUs. If, as Microsoft claims, Maia 200, which reduces per-token costs by more than 30%, begins to scale, and the MAI models achieve a 1.4-fold energy efficiency improvement on internal silicon, then as inference demand migrates to proprietary hardware, the unit cost of Azure AI is expected to drop significantly, and gross margins will gradually converge toward the higher levels of traditional CPU cloud businesses. The impact of AI on gross margins may transition from the current "dilution" phase to an inflection point where it "no longer dilutes and even brings gains." This judgment is still predicated on Microsoft's cost data being valid and represents a conditional deduction.

Regarding capital expenditures and cash flow, out of this year's investment of approximately $190 billion, about $25 billion comes from currently high memory component costs. As supply chain prices return to normal, combined with Microsoft shortening the deployment cycle for new GPUs by about 20% and completing some infrastructure ahead of schedule, this high-intensity capital expenditure is expected to peak and decline; market consensus suggests that free cash flow is likely to rebound significantly starting in fiscal year 2028. Management currently maintains guidance that operating margins will expand by about 1 percentage point in fiscal year 2026, with revenue and operating profit continuing to grow at double-digit rates in fiscal year 2027. If these guidances are met, the current high investment is more akin to "upfront investment" rather than a structural cash black hole.

On the revenue side, relying on an ecosystem of over 12,000 models on the Foundry platform, combined with the aforementioned $627 billion in RPO contracts on hand (up 99% year-over-year), Microsoft not only gains multi-model platform revenue but also deepens customer switching costs and lock-in effects, thereby strengthening revenue predictability and pricing power. In terms of the Total Addressable Market (TAM), the emergence of AI agents extends Microsoft's sales units from "human-oriented seats" to "agent-oriented seats," theoretically raising the ceiling for long-term growth.

Connecting these four layers, it can be seen that what Microsoft is making is not a gamble but a long-term layout using self-developed chips, in-house models, and proprietary platforms as leverage to transform "today's costs" into "tomorrow's gross margins." Corresponding to quantifiable tracking indicators, it is worth continuing to observe Azure growth, AI annualized run rate, operating margin trends, and the growth momentum of RPO and Copilot seats.

6. Risks and Verification: Information Gap Between Official Claims and Third-Party Testing

Reassembling the above clues, the logical chain is relatively clear: subscription software, judged by the market as a "sunset industry," has not only not been replaced by AI but has gained new growth momentum, providing a foundation for confidence across the entire industry; and Microsoft's misunderstood earnings report, which triggered a stock price plunge, never had performance itself as the pain point but rather market concerns over its capital expenditure pace; the Build 2026 response is specifically designed for this pain point. Based on the improvement of cost curves, the deepening of moats, and the aligned choices of institutional capital, there is a basis for a relatively positive judgment on Microsoft's long-term fundamentals.

However, rational investors still need to maintain a degree of sobriety. Beyond the possibility that the construction costs of AI agent infrastructure could continue to exceed expectations and the ROI cycle for the enterprise side could be extended, it is even more necessary to face a blind spot of information asymmetry: whether it is the "30% cost reduction" figure for Maia 200 or the benchmark results of the MAI models "surpassing competitors," both currently remain at the stage of "official claims" by Microsoft, and the market has yet to see a comprehensive testing report on this hardware and model stack from an authoritative third-party institution. That is to say, this seemingly brilliant scorecard still lacks the objective verification that Wall Street values most. Once subsequent third-party evaluation results fall short of expectations, Microsoft's short-term valuation could still come under pressure in a volatile market environment.

Therefore, the most direct testing point for whether this fundamental script can be realized is the latest quarterly earnings report to be released in late July. Two indicators carry the most signal: first, whether Azure cloud growth continues, and second, whether the trend of operating margins improves as guided by management—the latter is particularly critical because it directly answers the core question of "whether costs are truly being transformed into gross margins."

Overall, this article tends to believe that Microsoft's long-term fundamentals are in a key transition stage from "capital expenditure suppression" to "efficiency and platform monetization," but whether this transition can be successfully completed still depends on the aforementioned data and third-party verification.