Berkshire Hathaway revealed a US $4.3 billion investment in Alphabet Inc. during Q3 2025, buying approximately 17.8 million shares. This strategic move occurred as Alphabet achieved its first US $100 billion revenue quarter. Berkshire’s purchase reflects a value-focused approach amid a tech market rotation, emphasizing Alphabet’s solid fundamentals and growth potential.
Berkshire Hathaway has disclosed a new US $4.3 billion position in Alphabet Inc. (GOOGL), confirming that Warren Buffett’s conglomerate entered the stock during the third quarter of 2025 — the same period in which Alphabet reported its first-ever US $100 billion revenue quarter.
According to Berkshire’s latest 13-F filing, the company purchased roughly 17.8 million shares of Alphabet, making it one of Berkshire’s ten largest equity holdings. The move surprised market watchers who have long associated Berkshire’s tech exposure primarily with Apple, which the firm trimmed in the same quarter.
A Contrarian Entry at a Trillion-Dollar Scale
Berkshire’s timing stands out. Alphabet shares were trading around US $270 – 280 during Q3 2025 — only modestly above their estimated intrinsic value range. While other institutional investors were rotating out of mega-cap tech after two years of outperformance, Berkshire appears to have treated Alphabet as a value compounder rather than a momentum play.
For Buffett followers, the purchase echoes a familiar pattern: buying into a cash-rich franchise once its growth narrative collides with valuation discipline. Alphabet fits that mold neatly — a business generating more than US $80 billion in free cash flow annually, returning US $15 billion in quarterly buybacks, and maintaining over US $100 billion in cash reserves.
Fundamentals Back the Move
Alphabet’s Q3 2025 report, released October 29, underscored that growth and prudence can coexist in Big Tech.
Revenue: US $102.3 billion (+16 % YoY)
Operating Income: US $31.7 billion (+23 %)
EPS: US $2.87
Google Cloud: +34 % YoY, margin rising to 9 %
CapEx: Raised to US $91 – 93 billion for AI data-center expansion
CEO Sundar Pichai described the period as “a reflection of how AI is transforming every corner of our business,” while CFO Ruth Porat stressed “disciplined long-term investment.”
Those remarks align closely with Buffett’s own playbook — durable cash flow, reinvestment discipline, and capital allocation guided by intrinsic value rather than quarterly optics.
Reinforcing the “Still Underpriced” Thesis
Our prior SWOTstock analysis of Alphabet’s Q3 results placed fair value near US $284 per share, with the market trading just above that level post-earnings. Berkshire’s purchase suggests that even at these prices, long-term investors still see a margin of safety — particularly as Alphabet’s AI infrastructure spending begins to translate into productivity and monetization gains across Search, YouTube, and Cloud.
For value-oriented readers, the implication is clear: when Berkshire buys into a trillion-dollar tech name after a record quarter, it’s not chasing growth — it’s buying durability.
Market Reaction
The disclosure briefly lifted Alphabet shares in after-hours trading on Friday, as investors digested the significance of Berkshire’s first new mega-cap tech stake in years. Analysts now expect fresh comparisons between Alphabet’s AI capital discipline and Apple’s maturing growth profile, which Berkshire has been gradually reducing.
As of mid-November 2025, Alphabet trades around US $277, giving the stake a paper value near its initial cost — a rare instance where Buffett’s patience and Alphabet’s execution appear perfectly aligned.
Disclosure: This article is based on public filings and Alphabet’s official Q3 2025 financial results. It does not constitute investment advice.
The article discusses the rising importance of edge AI in enterprise technology, emphasizing its role in reducing latency, enhancing privacy, and optimizing costs. Accenture is positioned well to capitalize on this trend due to its strategic acquisitions and industry relationships. Potential valuation growth is estimated at around $378 per share by 2030, contingent on successful execution.
If you follow enterprise tech, you’ve probably noticed that “edge AI” has shifted from buzzword to board-level priority. Companies want AI that runs close to where data is created—on phones, sensors, cameras, factory lines, cars—so decisions happen in milliseconds, data stays private, and costs don’t balloon in the cloud. This article looks at where Accenture sits in that shift, how crowded the field has become, and what all of this could mean for the stock. I’ll keep the tone conversational and minimize bullet points, while still laying out a clear, investor-minded view with a fair-value estimate at the end.
Edge AI, briefly—why it matters now
Edge AI means running models locally on devices rather than shipping everything to cloud data centers. The benefits are straightforward: lower latency, better privacy, less bandwidth, and the ability to operate even when connectivity is spotty. Think of a security camera that flags anomalies on-device, a factory sensor that predicts failures in real time, or a car that fuses vision and language models to assist the driver without calling home.
Generative AI gets more headlines, but edge AI sits where operational value is created—on the shop floor, in vehicles, at retail, inside hospitals. The two are connected: many enterprises will pair cloud-scale GenAI with compact models running at the edge. Any services firm that can bridge that gap has a shot at premium work.
How Accenture has built its edge-AI muscle
Over the last couple of years Accenture has been stitching together a mix of consulting depth and hands-on engineering. It acquired silicon design firms (Excelmax and Cientra), invested in a model-compression startup (CLIKA), and trained a very large portion of its workforce in AI practices. That combination lets the company talk strategy with the C-suite, design and test solutions with embedded systems teams, and then scale deployments across dozens of plants or thousands of devices. Few consultancies can credibly do all three.
Just as important, Accenture already sits inside the industries where edge AI is landing first: manufacturing, automotive, telecom, healthcare, energy. Those client relationships, plus a broad partner web with chipmakers and cloud providers, position the company to win repeat work as pilots graduate to rollouts.
The competitive reality
This is not an empty field. On the platform and hardware side, NVIDIA, Qualcomm, Intel, Apple and others drive silicon and software stacks; hyperscalers offer toolchains that extend to the edge; consulting rivals like IBM and Capgemini bring strong engineering pedigrees; Deloitte and McKinsey remain influential with boards and regulators. In a crowded landscape, Accenture’s edge is less about owning a platform and more about orchestrating outcomes—choosing the right models and hardware, compressing them to fit, integrating with legacy systems, and running change management at enterprise scale.
SWOT analysis with price impacts
Accenture’s strengths in edge AI are unusually tangible for a services firm. The chip-design acquisitions and the investment in model optimization give it a way to reduce the “last mile” friction that often kills edge projects: getting models small, fast, and reliable on constrained devices. Coupled with its global delivery network, that capability can add real growth optionality. In valuation terms, I see those strengths supporting roughly +5% to +8% upside versus a no-edge-AI baseline, because investors tend to pay up for firms that can both advise and execute.
Weaknesses are more prosaic but matter. Accenture does not sell chips or devices, so it relies on partners for the building blocks. And because the company is already very large, even successful edge programs may represent a modest slice of overall revenue for a while. Those factors can dampen the multiple and shave –2% to –4% from what otherwise looks like an AI-premium narrative.
Opportunities are where things get interesting. Edge AI spending is compounding as factories modernize, cars become rolling computers, and hospitals instrument workflows. Accenture can bundle cloud GenAI and on-device intelligence into “reinvention” programs that attack cost, speed, and safety at once. If execution matches the pipeline, that story can support another +7% to +12% of valuation tailwind as investors price in higher growth durability.
Threats are real and mostly competitive. If hardware vendors and hyperscalers push turnkey offerings faster than expected, services can look more like commodity integration. If clients deploy more slowly, or if ROI takes longer to prove in regulated industries, momentum can stall. Put a –3% to –6% drag on valuation for those risks and you have a balanced, but still favorable, tilt.
Scenarios and fair value (illustrative)
Because Accenture doesn’t break out “edge AI revenue” as a line item, we model the impact at the level investors actually trade on: earnings power and the multiple the market is willing to pay. To keep this grounded, I anchor on reasonable ranges for EPS growth and P/E by 2030, then weigh the outcomes.
Bull case (40% probability). Edge programs scale alongside cloud GenAI work. AI-related revenue becomes a visible growth wedge, margins hold, and investors reward execution. If EPS reaches about $16 by 2030 and the market assigns a 28× multiple, you get an implied price near $448.
Base case (45%). Edge AI contributes meaningfully but remains under 10% of total revenue. Growth is steady, not explosive. With EPS around $14 and a 25× multiple, the implied price is about $350.
Bear case (15%). Adoption is slower, work skews toward integration, and the multiple compresses. With EPS near $12.5and a 22× multiple, the stock sketches to roughly $275.
Weighting those three paths yields a probability-weighted fair value of ~$378. It is not a moonshot number; it reflects confidence that Accenture will keep winning complex, multi-year AI programs where edge and cloud meet, without assuming platform-owner economics.
(Note: current share price fluctuates; the scenario math is illustrative rather than price-tick precise.)
What could change this view
Two things would push the needle higher. First, proof that model-compression and embedded engineering are shortening time-to-value on real deployments—think a global auto program or a multi-country factory network moving from pilot to standard with measurable savings. Second, clearer disclosure connecting AI bookings to revenue and margin expansion, so investors can track conversion rather than treating it as a narrative line.
On the downside, watch for customers delaying capital plans, hyperscalers tightening their grip on the edge toolchain, or a visible shift in project mix from “design and build” to lower-margin staff augmentation.
Bottom line
Edge AI isn’t a side show; it’s the place where AI meets the physical world. Accenture’s blend of consulting reach, embedded engineering from its acquisitions, and model-optimization capability puts it in a strong position to lead enterprise edge deployments. The field is busy and the company is already large, so don’t expect edge AI alone to redefine the business overnight. But as part of a broader AI reinvention engine, it can support healthier growth and a sturdier multiple. On the numbers above, that argues for a fair value around $378, with the bias skewed to the upside if execution stays crisp.
Disclosure & methodology: This article synthesizes public information on Accenture’s recent acquisitions and AI investments, industry reports on edge-AI adoption, and a scenario framework based on plausible EPS and P/E ranges through 2030. Accenture does not separately disclose edge-AI revenue, so assumptions are required; figures are illustrative, not precise forecasts. This is for education and discussion only and is not investment advice.
Salesforce Q1 FY26 earnings: strong cash flow, $1B+ AI revenue, and raised guidance—but the stock dipped. Discover our in-depth SWOT analysis, valuation scenarios, and why long-term tech investors may see upside.
🚀 TL;DR – Earnings Solid, Outlook Raised, But Street Unconvinced
Salesforce (NYSE: CRM) delivered on many fronts in Q1 FY26: strong free cash flow, a raised full-year outlook, and impressive AI momentum with over $1B in annualized AI revenue. Yet the market reaction was subdued. Shares slipped ~3% post-earnings as investors weighed modest growth against growing acquisition dependency. For long-term investors focused on enterprise AI, the current pullback may offer an attractive re-entry point — but execution risk remains high.
Salesforce reported Q1 FY26 revenue of $9.8 billion, reflecting 8% growth year-over-year. The company continues to demonstrate financial discipline, posting a 32.3% non-GAAP operating margin and generating $6.3 billion in free cash flow — a key metric that reinforces the strength of its subscription-based business model.
However, the earnings story wasn’t just about operational metrics. Management spent considerable time during the call highlighting Salesforce’s evolving identity as an AI-first enterprise software provider. The AI and Data Cloud segment reached a milestone of over $1 billion in annualized recurring revenue, up 120% from the prior year. Additionally, Agentforce — the company’s AI-powered sales assistant platform — closed over 8,000 deals, with 50% of them already monetized.
Despite these advancements, investor enthusiasm appeared tempered. Salesforce also unveiled its intention to acquire Informatica in a transaction valued at over $8 billion. While the strategic rationale centered on data integration and platform expansion, some investors viewed it as a sign that organic AI monetization remains in its early innings.
📌 Key Highlights
(📌 Visual Placeholder: Q1 FY26 Metrics Snapshot)
Revenue: $9.8B (+8% YoY)
Subscription & Support Revenue: $9.3B (+8% YoY)
Non-GAAP Operating Margin: 32.3%
Free Cash Flow: $6.3B (+4% YoY)
AI & Data Cloud ARR: $1B+ (+120% YoY)
Agentforce Deals: 8,000+ closed, 50% paid
Shareholder Return: $3.1B (including $2.7B in buybacks)
FY26 Guidance: Revenue raised to $41–41.3B, EPS to $11.27–11.33
🧠 SWOT Analysis – Is Salesforce Building Sustainable AI Moats?
To evaluate Salesforce’s trajectory, we use a SWOT framework — layering qualitative insight with quantitative impact ranges to assess where the stock could go next.
✅ Strengths
Salesforce’s high-margin business continues to generate substantial cash, supporting both R&D and shareholder returns. With a 32.3% non-GAAP operating margin and $6.3B in quarterly free cash flow, the company remains financially resilient. Meanwhile, the $1B+ in AI annual recurring revenue — up 120% year-over-year — signals that the firm’s early bets on generative AI are beginning to materialize.
Estimated Price Impact: +5% to +10% ($13–27)
⚠️ Weaknesses
At 8% year-over-year growth, revenue is expanding — but not at a pace that excites growth-focused investors. Combined with an $8B acquisition of Informatica, some view the quarter as a reminder that Salesforce still leans heavily on M&A for platform expansion. This can dilute long-term returns if integration is poorly executed or if synergy realization takes longer than anticipated.
Estimated Price Impact: –3% to –5% ($8–13)
🌱 Opportunities
The most obvious upside lies in the scaling of Agentforce and global AI deployment. Management noted that over 8,000 Agentforce deals were signed, with paid conversion already at 50%. On top of that, international expansion — particularly in Japan, the UK, and Canada — could provide incremental growth via cross-product bundling and new verticals.
Estimated Price Impact: +4% to +8% ($11–22)
🧨 Threats
Salesforce is not immune to macroeconomic uncertainty. Should enterprise IT budgets tighten further, even AI-led offerings could face delayed adoption. Add to that the integration risk tied to Informatica, and the bear case begins to take shape. Investors have seen how difficult it can be to maintain focus and cost discipline amid large-scale acquisitions.
Estimated Price Impact: –3% to –6% ($8–16)
📋 SWOT Summary Table
📐 Valuation Scenarios – Realistic Upside, But No Easy Wins
🟢 Bull Case – Target: $320 (Probability: 30%)
A best-case scenario assumes Salesforce executes flawlessly: Agentforce expands rapidly, international AI rollouts outperform, and Informatica is integrated smoothly. In this case, margin expansion and top-line acceleration could support a price of $320.
⚫ Base Case – Target: $290 (Probability: 50%)
In a more measured scenario, AI and cloud revenue continue to build gradually while macro headwinds and integration friction create a modest drag. Here, the valuation rests on steady execution — not breakout success.
🔴 Bear Case – Target: $260 (Probability: 20%)
The bear case includes a slower-than-expected AI ramp, growing customer budget constraints, and post-acquisition inefficiencies. Margins may hold, but revenue growth could fall short.
Weighted Fair Value:
(0.3 × $320) + (0.5 × $290) + (0.2 × $260) = $293
🔍 Peer Comparison – Where Salesforce Stands in the AI-Enterprise Cloud Race
While Salesforce has made impressive strides in monetizing AI, investors are right to compare its positioning against other enterprise software giants. Let’s take a closer look at how Salesforce stacks up against Microsoft and ServiceNow — two of the most visible players in enterprise AI and workflow automation.
Microsoft (MSFT) remains the dominant force in cloud infrastructure and productivity software, with its AI integration deeply embedded in products like Office 365, Azure OpenAI, and Dynamics. Although Microsoft has been less transparent about standalone AI ARR, its cross-product integration strategy has kept it at the forefront of enterprise adoption. Its advantage lies in seamless native integration — rather than monetizing AI as a separate revenue line, it’s baking it into everything.
ServiceNow (NOW), on the other hand, is pursuing a focused strategy in workflow automation with GenAI capabilities tied to task orchestration, IT operations, and HR service delivery. While it doesn’t disclose AI revenue explicitly, estimates suggest significant uptake across modules, especially post its strategic partnerships with NVIDIA and Microsoft. Its modular SaaS structure allows for more agile, vertical-specific AI adoption.
Salesforce (CRM) is unique in that it publicly discloses AI ARR, which recently crossed the $1B threshold (up 120% YoY). This offers greater transparency — a potential edge with analysts and investors — but also sets higher expectations. Salesforce’s AI strategy is tied closely to its Data Cloud and the Agentforce platform, but the question remains whether it can scale these innovations organically or will rely on acquisitions like Informatica to accelerate adoption.
Overall, Salesforce appears to be ahead in AI monetization transparency, but trails in seamlessness of integration (vs. Microsoft) and vertical execution (vs. ServiceNow). The coming quarters will be critical in demonstrating that these early AI wins are scalable — not just showcase projects.
🧭 Verdict – AI Execution Will Make or Break This Re-Rating
At ~$267, Salesforce is trading about 9% below its probability-weighted fair value of $293. That’s not a deep discount — but for investors willing to wait on Agentforce and international AI scaling, it may represent a reasonable opportunity.
Still, this is not a momentum trade. Salesforce must show it can deliver consistent AI-driven revenue growth without leaning too heavily on M&A to do it.
📩 Call to Action
Follow @SWOTstock and subscribe to get clear, no-spin earnings breakdowns.
⚠️ Disclaimer
This article is based solely on Salesforce’s official Q1 FY26 earnings report and management’s public comments. It is not investment advice.
SWOT Stock - Smart Investing Made Simple 
Leave a comment