Big Tech's AI Earnings: The Q3 2025 Reality Check

October 2025 will be remembered as the moment Wall Street stopped applauding Big Tech's AI ambitions and started demanding results. As Microsoft, Google, Meta, and Amazon reported their third-quarter earnings, a pattern emerged that sent shockwaves through financial markets: record revenues powered by artificial intelligence, but accompanied by astronomical spending that raised fundamental questions about profitability, sustainability, and whether the industry is building the future or inflating a bubble.

The numbers tell a story of unprecedented scale. These tech giants are collectively spending over $400 billion annually on AI infrastructure—data centers sprawling across continents, custom-designed chips by the millions, and armies of engineers pushing the boundaries of machine learning. Google's cloud revenue surged 35% to cross $15 billion. Microsoft's Azure grew 40% as enterprises rushed to adopt AI tools. Amazon's AWS accelerated to 20% growth, its fastest pace in years. Yet despite these impressive gains, investor reactions ranged from skeptical to hostile. Meta's stock plummeted 13%, erasing nearly $200 billion in market value in a single day. Microsoft shares fell 4% despite beating expectations. The message was clear: showing AI potential is no longer enough—companies must prove AI profitability.

Wall Street scrutinizes Big Tech's Q3 2025 earnings amid massive AI investments

This earnings season marks a critical inflection point in the AI revolution. After years of "trust us, AI will transform everything" narratives, investors are adopting a more discerning approach reminiscent of the late-1990s dotcom era—distinguishing between companies delivering measurable returns and those burning capital on promises. The Bank of England and International Monetary Fund both issued stark warnings in October 2025, stating that AI valuations have reached "dot-com peak levels," increasing risks of sharp corrections. With MIT research showing that only 5% of AI projects deliver measurable gains, and with circular financing deals raising questions about artificial demand, the tech sector faces its most significant credibility test since the 2000 bubble burst.

This article examines what Big Tech's Q3 2025 earnings reveal about AI's economic reality: who's winning and losing, what's driving the unprecedented spending, why investors are growing cautious, and what this transformation means for markets, jobs, and the future of technology itself.

The Earnings Scoreboard: Winners, Losers, and the AI Factor

Breaking down the Q3 2025 results reveals stark differences in how markets rewarded companies based on their ability to translate AI investments into tangible business outcomes.

Alphabet (Google): The Gold Standard for AI Monetization

Alphabet emerged as the clear winner, delivering results that silenced skeptics and demonstrated how AI can drive both growth and profitability. The company reported $102.35 billion in quarterly revenue—the first time any tech company crossed $100 billion in a single quarter—representing 16% year-over-year growth and exceeding analyst estimates by over $2 billion. Net income surged 33% to $34.98 billion, with adjusted earnings per share of $3.10 crushing expectations of $2.29.

What impressed investors wasn't just the scale but the efficiency. Google Cloud, the segment most directly exposed to AI demand, grew 35% to $15.15 billion, outpacing estimates and demonstrating clear enterprise adoption of AI services. The cloud backlog reached $155 billion, up 46% quarter-over-quarter, indicating sustained future demand. Meanwhile, Google's flagship Search business showed that AI enhancements can drive incremental value rather than cannibalize existing revenue—AI Overviews and Gemini integration actually increased query volumes, particularly among younger users, alleviating fears that generative AI would undermine Google's core business.

CEO Sundar Pichai highlighted that Gemini, Google's flagship AI model, achieved 650 million monthly active users with queries tripling quarter-over-quarter, processing 7 billion tokens per minute. More importantly, Alphabet demonstrated AI monetization across multiple revenue streams: advertising grew through AI-powered targeting improvements, cloud revenue accelerated from enterprise AI adoption, and even YouTube saw 15% ad revenue growth boosted by AI-enhanced content recommendations and creator tools.

The market responded enthusiastically—Alphabet's stock surged 5-7% in after-hours trading, adding tens of billions in market value. Analysts from Piper Sandler and Wolfe Research raised price targets to $285-290, citing a "AI bull case" materializing. While Alphabet did raise its 2025 capital expenditure forecast to $91-93 billion (the third increase this year) and warned of "significant" further increases in 2026, investors viewed this spending as productive rather than speculative given the clear revenue acceleration it's producing.

Microsoft: Growth Meets Capacity Constraints

Microsoft delivered solid results that would have thrilled investors in any other context but left them wanting more given the AI hype. Revenue reached $77.67 billion, beating estimates by nearly $2 billion and growing 18.4% year-over-year. Earnings per share of $3.72 exceeded expectations, and operating margins expanded to 48.9% from 46.6%, demonstrating operational efficiency despite massive AI investments.

Azure and other cloud services grew 37-40%, with AI contributing significantly—in prior quarters, AI added 16 percentage points to Azure's growth. Microsoft Copilot, the company's AI productivity tool, surpassed 150 million monthly active users, driving higher average revenue per user and demonstrating genuine enterprise adoption. Commercial bookings surged 112%, including massive commitments from OpenAI and other AI-focused customers.

Yet the stock declined 3-4% in extended trading. Why? CEO Satya Nadella acknowledged ongoing capacity constraints that have limited Azure's ability to fully meet AI demand, with management warning these constraints could persist through fiscal year 2026. Capital expenditures jumped to $34.9 billion in Q3, up from $24.2 billion the previous quarter—a 74% year-over-year increase—with plans to increase AI capacity by over 80% this year and double data center footprint over the next two years.

The concern isn't that Microsoft is losing the AI race—it's that the company must spend enormously just to maintain competitive position, with no clear timeline for when capacity will match demand or when AI investments will generate returns commensurate with their costs. CFO Amy Hood indicated capital expenditures would remain elevated and potentially increase further in fiscal 2026, raising questions about margin sustainability.

Guidance for the subsequent quarter projected 14-16% growth, solid but not spectacular given the AI investment scale. While Microsoft remains a leader with strong positions in enterprise AI through Azure, OpenAI partnership, and Copilot, the market is questioning whether the company can achieve sufficient returns to justify the capital intensity.

Meta: The Spending Spooks Wall Street

Meta's Q3 results illustrated how even strong operational performance can be overshadowed by spending concerns. Revenue reached $51.24 billion, up 26% year-over-year—the highest growth rate since Q1 2024—and matched analyst estimates. Advertising revenue, Meta's core business, totaled $50.08 billion, up 21%, with AI-powered recommendation systems driving a 5% increase in Facebook usage time and 6% on Instagram.

AI integration showed tangible benefits: Reels (short-form video) achieved a $50 billion annual run rate, AI-driven ad tools reduced cost-per-lead by 14% for Advantage Plus campaigns, and new Instagram ranking models lifted conversions by 2%. Daily active users across Meta's family of apps reached 3.54 billion, up 8% year-over-year, with AI features like the Vibes feed boosting engagement.

Tech giants are investing hundreds of billions in AI infrastructure, raising questions about returns

Yet Meta's stock plummeted 9-13.5%, erasing approximately $200 billion in market value. The culprit: a one-time $15.93 billion non-cash tax charge under the "One Big Beautiful Bill Act" that reduced reported earnings per share to $1.05 (versus adjusted EPS of $7.25 excluding the charge), combined with raised capital expenditure forecasts to $70-72 billion for 2025 and promises of even higher spending in 2026.

CEO Mark Zuckerberg defended the strategy, stating that a "significantly larger investment" in AI compute is "very likely to be profitable" given potential returns from improved ad systems and new AI products like Ray-Ban Meta smart glasses, which sold out rapidly. CFO Susan Li noted that AI talent hiring, including roles in monetization and infrastructure, was a key expense driver, with headcount rising 8% year-over-year to 78,450 employees despite recent layoffs of about 600 workers in AI divisions.

Analysts weren't convinced. Oppenheimer downgraded Meta to "perform" from "outperform," drawing parallels to the company's metaverse investments that consumed tens of billions with limited returns. Deutsche Bank slashed free cash flow estimates by 30-40% for 2026-2027, emphasizing risks of delayed monetization. The concern centers on Meta's pattern of aggressive spending on long-term bets—Reality Labs has lost over $50 billion since 2020—without clear proof these investments generate proportional returns.

For Q4 2025, Meta guided to revenue of $56-59 billion, with AI expected to sustain ad growth but offset by lower Reality Labs revenue due to no new VR headset releases. The company indicated that expense growth and capital expenditures would outpace revenue in 2026, prioritizing long-term AI positioning over near-term profitability—a strategy that might pay off eventually but requires significant investor faith.

Amazon: AWS Acceleration and AI Infrastructure

Amazon's Q3 results exceeded expectations across most metrics, with revenue of $180.2 billion (up 12% year-over-year) and earnings per share of $1.95, beating estimates by approximately 25%. The star performer was Amazon Web Services (AWS), which generated $33 billion in revenue, growing 20.2% year-over-year—the fastest growth rate since 2022 and surpassing analyst expectations.

AWS's acceleration was directly attributable to AI demand. CEO Andy Jassy described AI as the "single largest driver of new cloud workloads," with unannounced deals in October exceeding the entire Q3 total, indicating momentum. Amazon added 3.8 gigawatts of computing capacity over the past 12 months, with plans to add another gigawatt in Q4 and double capacity by 2027. The company's custom AI chips, particularly Trainium 2, are "fully subscribed" and growing 150% since Q2.

A major milestone was the opening of Project Rainier, an $11 billion AI data center equipped with 500,000 Trainium chips, supporting partnerships like the one with Anthropic (an AI company in which Amazon invested $9.5 billion, generating a pre-tax gain that boosted net income). AWS maintained market leadership with a $132 billion annualized run rate, larger than Google Cloud and Microsoft Azure individually, though growing slightly slower than Azure's 40%.

Beyond AWS, Amazon's core e-commerce segments showed steady growth: North American sales rose 11% to $106.3 billion, international sales increased 14% to $40.9 billion, and advertising revenue grew 23-24% to $17.7 billion, boosted by AI-driven ad tools. The Rufus shopping assistant reached 250 million users, increasing purchase completion rates by 60%, demonstrating AI's potential to enhance customer experience while driving revenue.

However, the results weren't without concerns. Amazon reported $4.3 billion in special charges, including a $2.5 billion FTC settlement over Prime subscription practices and $1.8 billion in severance costs from layoffs totaling 14,000 net job cuts. Operating income of $17.4 billion would have been $21.7 billion without these charges. Looking ahead, Amazon raised full-year 2025 capital expenditures to $125 billion, up from $118 billion, with expectations for further increases in 2026 driven by AI and infrastructure needs.

The stock initially surged 13-14% in after-hours trading but later dipped 3% to $226.99, possibly reflecting profit-taking or concerns about high capital intensity. Overall, Amazon demonstrated clearer AI monetization than Meta or Microsoft but still faces questions about the sustainability of investment levels and whether capacity expansions will generate sufficient returns.

The $400 Billion Question: Why Are They Spending So Much?

The scale of AI spending is staggering and accelerating. Collectively, the major cloud providers—Amazon, Microsoft, and Google—are on pace to spend approximately $350 billion on capital expenditures in 2025, with Meta adding another $70-72 billion. These investments primarily fund:

• Data Centers: Massive facilities housing hundreds of thousands of servers, requiring land acquisition, construction, power infrastructure, and cooling systems
• Custom AI Chips: Companies like Google (TPUs), Amazon (Trainium, Inferentia), and Microsoft (partnerships with Nvidia and AMD) are designing specialized processors optimized for AI workloads
• Networking Infrastructure: High-speed connections between data centers to support distributed AI training and inference
• Energy: AI workloads consume vastly more power than traditional computing; data centers now represent 2-3% of global electricity consumption
• Talent: Top AI researchers command million-dollar compensation packages; companies are hiring thousands of engineers, data scientists, and infrastructure specialists

What drives this spending beyond technological capability? Several forces converge:

Competitive Dynamics: Once Amazon, Microsoft, or Google announces capacity expansion, competitors must match or risk losing market share. Cloud customers evaluate providers partly on availability and performance—if one provider can't meet AI workload demands due to capacity constraints (as Microsoft currently faces), enterprises will shift to competitors. This creates a "spend or fall behind" dynamic where companies invest even if returns are uncertain.

First-Mover Advantages: The AI infrastructure being built today will determine market position for years. Companies that establish data center footprints, secure energy allocations, lock in semiconductor supply, and build operational expertise create barriers competitors struggle to overcome. AWS's current leadership stems from its years-long head start; catching up requires enormous investment.

Demand Signals: All companies report that AI demand exceeds current capacity. Microsoft explicitly cites capacity constraints limiting Azure growth. Amazon notes that October AI deals alone exceeded Q3 totals. Google's cloud backlog grew 46% quarter-over-quarter. These signals indicate that more capacity translates to more revenue—at least in the near term.

Partnership Commitments: Many investments tie to specific partnerships and commitments. OpenAI's agreements with Microsoft involve guaranteed compute capacity. Anthropic's partnership with Amazon includes infrastructure commitments. These deals obligate spending regardless of broader market conditions.

Strategic Positioning: Beyond immediate cloud revenue, companies view AI infrastructure as foundational for future products and services. Amazon uses AWS capacity internally for Alexa, robotics, and logistics optimization. Google leverages its infrastructure for Search, YouTube, and advertising. Microsoft powers Copilot and enterprise tools. The spending serves multiple strategic purposes beyond cloud service revenue.

Corporate leadership faces difficult decisions balancing AI investments against profitability demands

Shareholder and Market Pressure: Paradoxically, companies face pressure both to invest heavily in AI (to capture opportunities and avoid disruption) and to demonstrate returns (to justify valuations). During 2023-2024, markets rewarded AI investment announcements with stock price increases. Now, as evidenced by Q3 reactions, the pendulum is swinging toward demanding profitability. But reversing course risks being perceived as falling behind technologically.

Circular Deals and Financial Engineering: Some arrangements raise concerns about artificial demand. Nvidia investing $100 billion in OpenAI, which then purchases Nvidia chips with that capital, creates a circular flow that inflates apparent demand without necessarily reflecting genuine market needs. Meta's $27 billion in debt financing for data centers similarly raises questions about whether spending reflects actual customer demand or speculative positioning.

The Bubble Question: Are We Repeating History?

The specter of the dot-com bubble looms large over Q3 2025 earnings discussions. The parallels are striking and troubling:

Valuation Metrics: The S&P 500's cyclically adjusted price-to-earnings (CAPE) ratio reached levels last seen at the 2000 dot-com peak. AI-focused companies trade at valuations assuming decades of growth, with price-to-earnings ratios exceeding historical averages by wide margins. The "Magnificent 7" tech stocks (Microsoft, Apple, Google, Amazon, Meta, Nvidia, Tesla) now account for over one-third of the S&P 500's total value, creating enormous concentration risk.

Investment vs. Returns Gap: The dot-com era saw massive capital deployed into internet infrastructure and startups, with most investors ultimately losing money despite the internet's ultimate transformative impact. Today, an MIT study of over 300 AI projects found that only 5% delivered measurable gains, with most stalling due to poor integration and scalability issues. If 95% of AI projects fail to generate returns, the aggregate spending levels become difficult to justify.

Hype Cycles: During the dot-com era, companies adding ".com" to their names saw stock prices jump. Today, companies mentioning "AI" in earnings calls or press releases experience similar effects. The fundamental question is whether current AI capabilities justify current valuations or whether markets are pricing in speculative future potential that may not materialize on expected timelines.

Market Concentration: AI has added approximately $6 trillion to Big Tech's market value since 2022. However, this gain is concentrated in a handful of companies. If sentiment shifts or AI monetization disappoints, losses could cascade through indices heavily weighted toward these firms, impacting retirement accounts, pension funds, and the broader economy.

Warning Voices: Business leaders are sounding alarms. OpenAI's Sam Altman warned about overvaluation in the AI sector. Amazon's Jeff Bezos described the current moment as a "good bubble" with real technological potential but acknowledged significant risks for speculative ventures. JPMorgan CEO Jamie Dimon highlighted "heightened uncertainty" in markets, urging greater caution. The Bank of England and IMF issued formal warnings about AI-driven market risks.

Yet there are also important differences from the dot-com era:

Profitable Core Businesses: Unlike many 1990s internet startups that had no revenue or path to profitability, today's AI investors are established, profitable companies with diversified revenue streams. Google, Microsoft, Amazon, and Meta generated a combined $150+ billion in quarterly revenue in Q3 2025. Even if AI investments underperform, these companies have strong core businesses.

Real Current Value: AI is already delivering measurable value in specific applications: ad targeting improvements, customer service automation, code generation assistance, and operational efficiency gains. The question isn't whether AI works but whether current spending levels match current capabilities or overshoot them.

Infrastructure vs. Speculation: Much of today's spending funds infrastructure—data centers, chips, networks—that has tangible value and alternative uses. During the dot-com crash, many assets proved worthless. Today's data centers can be repurposed, sold, or leased if AI demand disappoints, providing some downside protection.

Longer Investment Horizons: Today's investors, having experienced the dot-com crash and 2008 financial crisis, arguably have more sophisticated risk assessment. The market's Q3 2025 reactions—punishing Meta for unclear returns while rewarding Google for demonstrated monetization—suggest investors are distinguishing between companies based on fundamentals rather than blindly chasing AI hype.

The most likely scenario may be neither "AI revolutionizes everything immediately" nor "AI bubble bursts catastrophically" but rather a middle path: AI delivers transformative value in specific domains over time, but current spending outpaces near-term monetization, leading to a period of disappointing returns and market corrections before eventually validating long-term investment theses. This would mirror other technological transitions like cloud computing, which took years to fully monetize after massive initial investments.

What This Means for Jobs, Markets, and the Future

Big Tech's Q3 2025 earnings have implications extending far beyond quarterly financial results:

Employment Impact: The AI investments driving these earnings are simultaneously eliminating jobs at scale. As detailed in previous analysis, over 77,000 tech workers lost jobs in the first seven months of 2025, with AI explicitly cited as a driver. The same companies reporting record revenues are cutting thousands of positions—Amazon announced 14,000 net job cuts, Meta laid off 600 AI division employees, and Microsoft eliminated over 15,000 positions in 2025.

The paradox is stark: AI investments create wealth for shareholders and opportunities for AI-skilled workers (job postings requiring AI expertise pay 25% more on average) while destroying opportunities for those whose work AI replaces. Entry-level positions have declined 13% in AI-exposed fields since 2022 as companies automate or eliminate traditional career entry points. This dynamic raises profound questions about economic inequality, workforce retraining, and the social sustainability of AI-driven productivity gains that accrue to capital rather than labor.

Market Volatility: The Q3 earnings reactions signal that AI-related stocks will experience increased volatility as markets reassess valuations based on actual performance rather than potential. Companies demonstrating clear AI monetization (like Google) will be rewarded; those with opaque strategies or high spending without proportional returns (like Meta) will be punished. This represents a healthier, more sustainable market dynamic than the "AI hype" phase of 2023-2024 but creates risks for investors concentrated in tech.

The concentration of market value in AI-related stocks means broader indices are vulnerable to sentiment shifts. If AI-driven companies continue underperforming expectations, corrections could impact retirement accounts, pension funds, and economic confidence broadly. Alternatively, companies that successfully navigate the transition from investment to monetization could drive sustained market gains, validating current valuations.

Innovation vs. Consolidation: The enormous capital requirements for competitive AI infrastructure—$70-125 billion annually—create barriers that favor large incumbents over startups. While AI theoretically democratizes certain capabilities (any developer can call an API), the underlying infrastructure remains concentrated in a few hands. This could reduce innovation, entrench existing market power, and limit competitive dynamics.

However, history shows that dominant infrastructure providers often enable waves of innovation on top of their platforms. Amazon's AWS, originally built to support Amazon's own needs, became the foundation for thousands of startups and new business models. Similarly, Microsoft's Azure, Google Cloud, and Amazon's AI infrastructure could enable applications and business models not yet imagined, with economic value ultimately exceeding the infrastructure investment.

Regulatory Scrutiny: The market dominance evident in Q3 results will intensify regulatory attention. The Federal Trade Commission is already investigating AI market concentration. The European Union's AI Act is implementing comprehensive regulation. As AI impacts employment, privacy, and market competition, governments will face pressure to intervene. Companies spending hundreds of billions on AI infrastructure while eliminating jobs may face political backlash, potentially leading to regulations affecting business models, data usage, and market structure.

The Profitability Timeline: Perhaps the most critical question is timing: When will AI investments generate returns justifying their costs? Google's Q3 results suggest that companies integrating AI into existing profitable businesses can achieve relatively quick returns. Meta's results indicate that companies making long-term bets on new AI applications face years of investment before monetization. Microsoft's experience shows that even clear AI demand doesn't automatically translate to profitability if capacity constraints and high costs squeeze margins.

For investors, the lesson is clear: AI is transforming business and technology, but not all AI investments are equal. Companies with clear monetization paths, integration into existing profitable businesses, and demonstrated discipline deserve premiums. Companies making speculative long-term bets should trade at discounts until they prove returns. The market's Q3 2025 reactions suggest this differentiation is beginning to occur.

Conclusion: The Reckoning Has Begun

Big Tech's Q3 2025 earnings represent a watershed moment—the end of the AI hype phase and the beginning of the AI accountability phase. Markets are no longer content with promises of future transformation; they demand evidence of current value creation. Companies must now prove that their hundreds of billions in AI infrastructure spending will generate returns proportional to their costs, not in some distant future but in quarters ahead.

Google demonstrated that integrating AI into profitable core businesses can accelerate growth while managing costs. Amazon showed that AI infrastructure can drive revenue growth if demand is genuine and sustainable. Microsoft illustrated that even strong positions don't guarantee easy profits when capacity constraints and competitive dynamics require massive ongoing investment. Meta learned that investor patience for long-term bets without clear monetization timelines has limits, especially when spending accelerates beyond initial projections.

The broader implications extend beyond quarterly financial results. AI is reshaping the economy, transforming work, concentrating wealth, and creating both unprecedented opportunities and profound risks. The Q3 2025 earnings season marks the moment when the technology industry, financial markets, and society began demanding answers to difficult questions: Who benefits from AI-driven productivity gains? Are current investment levels sustainable? What happens to workers whose roles AI replaces? How do we ensure AI development serves broad societal interests rather than narrow corporate or shareholder returns?

The answers will unfold over coming quarters and years. What's clear now is that the era of "trust us on AI" has ended. The era of "prove it" has begun. Companies that can demonstrate genuine value creation, sustainable business models, and responsible development will thrive. Those that can't may discover that today's record revenues are tomorrow's cautionary tales.

References

1. Microsoft Investor Relations. (2025, October 29). Microsoft announces Q3 FY2026 earnings results. https://www.microsoft.com/en-us/investor/earnings/fy-2025-q3/press-release-webcast

2. CNBC. (2025, October 29). Alphabet reports Q3 2025 earnings, beats expectations as Google Cloud revenue surges. https://www.cnbc.com/2025/10/29/alphabet-google-q3-earnings.html

3. Meta Investor Relations. (2025, October 29). Meta reports third quarter 2025 results. https://investor.atmeta.com/investor-news/press-release-details/2025/Meta-Reports-Third-Quarter-2025-Results/

4. CNBC. (2025, October 30). Amazon Q3 2025 earnings: AWS growth accelerates as AI demand surges. https://www.cnbc.com/2025/10/30/amazon-amzn-q3-earnings-report-2025.html

5. Reuters. (2025, October 29). Microsoft's cloud surge lifts revenue above expectations. https://www.reuters.com/business/microsofts-cloud-surge-lifts-revenue-above-expectations-2025-10-29/

6. Business Insider. (2025, October 30). Big Tech is spending over $400 billion on AI. Here's what they're buying. https://www.businessinsider.com/big-tech-spending-on-ai-capex-q3-2025-10

7. The New York Times. (2025, October 29). Tech companies are on an AI spending spree. https://www.nytimes.com/2025/10/29/business/dealbook/tech-ai-spending-spree.html

8. Bloomberg. (2025, October 4). Why AI bubble concerns loom as OpenAI, Microsoft, Meta ramp up spending. https://www.bloomberg.com/news/articles/2025-10-04/why-ai-bubble-concerns-loom-as-openai-microsoft-meta-ramp-up-spending

9. The Guardian. (2025, October 8). The AI valuation bubble is now getting silly. https://www.theguardian.com/technology/nils-pratley-on-finance/2025/oct/08/the-ai-valuation-bubble-is-now-getting-silly

10. Yale Insights. (2025). This is how the AI bubble bursts. https://insights.som.yale.edu/insights/this-is-how-the-ai-bubble-bursts

11. IndexBox. (2025, October 30). Big Tech reports revenue gains amid AI investment scrutiny. https://www.indexbox.io/blog/big-tech-reports-revenue-gains-amid-ai-investment-scrutiny/

12. TechBuzz. (2025, October 30). Big Tech's $250B AI bet sparks bubble fears after Q3 earnings. https://www.techbuzz.ai/articles/big-tech-s-250b-ai-bet-sparks-bubble-fears-after-q3-earnings