When a $118 billion software company that was supposed to be “maturing” suddenly accelerates its growth rate instead of decaying it, investors are right to pay attention. That is exactly what happened on September 2, 2026, when Snowflake (NYSE: SNOW) reported fiscal second-quarter results that broke the multi-year pattern of slowing growth that had haunted the stock since its 2020 IPO. Product revenue grew 37% year-over-year to $1.49 billion, an acceleration from the low-30s pace of the prior year, and management raised its full-year product revenue guidance to $6.07 billion. The market responded with a roughly 20%+ single-session surge, and within days Morgan Stanley had lifted its price target to $470, Argus to $450, and Goldman Sachs to $436.
This article makes the case that the Snowflake AI Data Cloud reacceleration is not a one-quarter accident but a structural inflection driven by three forces: (1) the migration of enterprise AI workloads onto governed, first-party data — which is Snowflake’s home turf; (2) the rapid monetization of the Cortex AI product family, which turns Snowflake from a passive storage-and-query engine into an active AI application platform; and (3) a still-expanding remaining performance obligation (RPO) backlog that now stands at $9.0 billion, giving unusual visibility into future revenue.
Three key investment points frame the thesis. First, growth reacceleration plus margin expansion is the rarest and most valuable combination in software — Snowflake’s non-GAAP operating margin expanded more than 400 basis points to 15% even as top-line growth sped up, showing the model has operating leverage that the GAAP net loss headline obscures. Second, Snowflake’s consumption-based revenue model means that as customers build AI applications that repeatedly query data, revenue compounds automatically without new sales cycles — a structural tailwind unique to usage-priced infrastructure. Third, at a current price of $335.29 against an analyst consensus target of $438.10, the stock offers roughly 31% upside to consensus even after a strong year, and 32 of 35 covering analysts rate it a Buy.
This report covers, in order: what Snowflake actually does and how it makes money; a deep analysis of the cloud data and AI infrastructure industry it competes in; the durability of its economic moat; a full financial breakdown including the reality of its GAAP losses; a valuation exercise appropriate for a high-growth, not-yet-GAAP-profitable company; the specific risks that could break the thesis; and a concrete entry/exit plan. Because Snowflake carries a trailing-twelve-month EPS of −$3.17, a conventional P/E is not meaningful — so valuation here leans on price-to-sales, EV/Sales, and forward multiples, and we say so explicitly rather than forcing a misleading earnings multiple.
1. Company Overview
Snowflake operates what it calls the AI Data Cloud — a cloud-native platform that lets organizations store, integrate, analyze, share, and now build AI applications on top of their data, without managing the underlying infrastructure. The founding insight, back in 2012, was to separate storage from compute in the cloud, so customers could scale each independently and pay only for what they use. That architectural choice is the root of both the product’s popularity and its business model.
How Snowflake makes money — the consumption model. Unlike traditional software companies that sell fixed-price annual seats or licenses, Snowflake charges primarily for consumption. Customers buy “credits” that are burned when they run queries, load data, or invoke AI functions. The more a customer uses the platform, the more revenue Snowflake earns — with essentially no incremental sales effort. This is why the vast majority of Snowflake’s revenue is reported as product revenue (the credit consumption) rather than services. In the second quarter of fiscal 2027, product revenue was $1.49 billion out of roughly $1.55 billion in total revenue, meaning product is about 96% of the top line. The remainder is professional services and training.
Revenue by segment. Snowflake does not break out revenue into multiple product lines the way a diversified software vendor might; the credit model unifies almost everything. The most useful decomposition is product vs. professional services:
Revenue Component Q2 FY2027 (approx.) Share of Total Nature Product revenue $1.49B ~96% Consumption of compute/storage/AI credits Professional services & other ~$0.06B ~4% Implementation, training, support Total revenue ~$1.55B 100% —
Customers and market position. Snowflake serves well over 11,000 total customers, but the number that matters most is the count of large accounts: as of Q2 FY2027 there were 828 customers generating more than $1 million in trailing-twelve-month product revenue, up meaningfully year-over-year (the company reported 733 such customers at the end of fiscal 2026). This concentration of spend among large enterprises — including a substantial share of the Forbes Global 2000 — is the signature of a mission-critical platform rather than a discretionary tool. Snowflake sits at the center of the modern data stack, competing on one side with the cloud hyperscalers’ native warehouses (Amazon Redshift, Google BigQuery, Microsoft Fabric/Synapse) and on the other with data-and-AI platform Databricks.
Ownership and governance. Snowflake is a widely held large-cap with a shareholder base dominated by institutional investors — index funds, growth-focused mutual funds, and long-only managers hold the overwhelming majority of the float. Berkshire Hathaway famously participated in the IPO but has since exited. Insider ownership is modest, typical for a company several years past IPO, and the company is led by CEO Sridhar Ramaswamy, a former Google advertising executive and AI-search founder whose appointment in 2024 explicitly signaled the board’s intent to reposition Snowflake around artificial intelligence. That leadership pivot is now visibly reflected in the product roadmap and the reaccelerating numbers.
2. Industry Analysis
2-1. Market Size & Growth Trajectory
Snowflake competes in the intersection of three large and overlapping markets: cloud data warehousing/lakehouse, data integration and analytics, and — increasingly — enterprise AI application infrastructure. Snowflake’s own addressable-market framing has grown over time as the platform expanded from analytics into data engineering, data sharing, and AI; management has historically pointed to a total addressable market in the range of $340 billion, and the AI overlay arguably expands that further. Independent industry estimates for cloud data platforms and analytics software consistently place the combined market in the hundreds of billions of dollars with double-digit compound annual growth rates well into the back half of the decade.
The crucial point for investors is where in the cycle this industry sits. Enterprise data migration to the cloud is perhaps 30–40% complete by most estimates — meaning the majority of the world’s enterprise data still lives on-premises or in legacy systems. That is not a mature market approaching saturation; it is a market in the acceleration phase of a multi-decade platform shift. Layered on top is a second, newer S-curve: generative AI. Every enterprise that wants to deploy AI on its proprietary data first needs that data to be consolidated, governed, and queryable — precisely the problem Snowflake exists to solve. The AI wave therefore does not disrupt Snowflake’s market; it enlarges the reason to consolidate data on Snowflake in the first place.
2-2. Structural Growth Drivers
Driver 1: The AI-on-first-party-data imperative (the largest driver). The defining lesson of the first two years of the generative-AI boom is that foundation models are commoditizing — the durable value accrues to whoever controls the proprietary data the models reason over. Enterprises have learned that a general-purpose chatbot is far less valuable than an AI application grounded in the company’s own customer records, transactions, and documents. To build such applications safely, that data must be governed, access-controlled, and co-located with compute. Snowflake’s platform is where a large and growing share of that first-party enterprise data already lives. This turns Snowflake into natural infrastructure for enterprise AI: each new AI use case is a new set of queries, and each query consumes credits. Because AI workloads are computationally heavier than traditional business-intelligence queries, they consume credits at a faster rate — directly lifting revenue per customer. This is the single most important reason growth reaccelerated to 37% rather than continuing to decay toward 20%.
Driver 2: Consumption compounding and net revenue expansion. In a seat-based software model, once every employee has a license, growth stalls. In a consumption model, growth continues as long as usage grows — and usage of data compounds naturally as businesses generate more data and build more applications on it. Snowflake’s net revenue retention rate of 126% in Q2 FY2027 quantifies this: the average existing customer spent 26% more than a year earlier, before counting any new customers. While NRR has drifted down from the 140%+ of earlier years (a mathematical inevitability as the revenue base grows), a 126% expansion rate on a multi-billion-dollar base is an extraordinary organic tailwind. AI-driven consumption appears to be arresting, and possibly reversing, the multi-year NRR decline — a key metric to watch.
Driver 3: Platform expansion beyond the data warehouse. Snowflake has systematically broadened from analytics into adjacent workloads: data engineering (Snowpark), data sharing and monetization (the Snowflake Marketplace and clean rooms), application development (Native Apps), unstructured data, and now a full suite of AI products under the Cortex banner. In fiscal 2026 alone the company introduced more than 430 new product capabilities. Each expansion adds new consumption vectors and deepens the switching costs (discussed in Section 3). Management highlighted that its AI-oriented products — including its conversational analytics and agentic offerings — surpassed 9,100 accounts, adding more than 2,000 in a single quarter, while its collaboration/workspace product reached 5,800 accounts. These are early-stage products, but their adoption velocity indicates the platform’s expansion strategy is landing.
Short-term vs. long-term dynamics. In the short term (next 12–18 months), the growth story is dominated by AI workload consumption and the raised guidance to $6.07 billion in FY2027 product revenue, with a Q3 guide of $1.59 billion (above the ~$1.50 billion consensus). Over the long term (3–5+ years), the story is the continued migration of enterprise data to the cloud, the compounding of consumption, and Snowflake’s ability to capture a share of the enormous AI-application layer being built on top of enterprise data.
2-3. Competitive Landscape
Snowflake competes against two very different types of rivals: the cloud hyperscalers, who bundle a data warehouse into their broader cloud, and Databricks, a private company built around the “lakehouse” and data-science/AI use cases.
Company Positioning Approx. Revenue Scale Profitability Key Advantage Snowflake (SNOW) AI Data Cloud, consumption-priced ~$5.4B TTM, ~$6.1B FY27 guide (product) GAAP loss; ~15% non-GAAP op margin Ease of use, governance, cross-cloud neutrality, sharing Databricks (private) Data + AI lakehouse ~$4B+ annualized run-rate (private) Reportedly near breakeven Strength in data science / ML / open formats Amazon Redshift (AWS) Native cloud warehouse Bundled in AWS Profitable (within AWS) Deep AWS integration, price Google BigQuery (GCP) Serverless warehouse Bundled in GCP Profitable (within GCP) Serverless scale, BigQuery ML Microsoft Fabric / Synapse Unified analytics on Azure Bundled in Azure Profitable (within Azure) Office/Power BI bundle, enterprise reach
Why Snowflake is better positioned than its peers. Against the hyperscalers, Snowflake’s decisive advantage is cross-cloud neutrality: it runs on AWS, Azure, and Google Cloud, so a customer is not locked into a single hyperscaler and can share data seamlessly with partners on any cloud. Large enterprises deliberately value this vendor independence. Snowflake also consistently wins on ease of use and governance — it requires far less tuning and administration than the hyperscalers’ native tools, which lowers the total cost of ownership despite a higher headline price. Against Databricks, Snowflake historically owned the business-intelligence/SQL analytics workload and the “just works” experience, while Databricks owned the data-science/ML workload; the two are now converging on each other’s turf, and Snowflake’s aggressive Cortex AI roadmap is its answer to Databricks’ AI-native heritage. The competitive question that will define the next five years is whether Snowflake can win the AI-application layer as convincingly as it won the analytics layer.
3. Economic Moat Analysis
Moat Type 1: Switching Costs (the primary moat)
Snowflake’s most durable competitive advantage is the switching cost that accumulates once an enterprise adopts the platform as its central data repository. Migrating a company’s data warehouse is one of the most disruptive projects an IT organization can undertake: it involves rewriting queries and data pipelines, retraining analysts, re-establishing governance and access controls, and revalidating regulatory compliance — all while the business continues to run on the existing system. The concrete evidence that these switching costs are real and rising is the net revenue retention rate of 126%: customers do not merely stay, they systematically spend more each year. Equally telling is the remaining performance obligation of $9.0 billion, up substantially year-over-year — this is contracted future revenue that customers have committed to, a direct measure of how deeply embedded Snowflake is in their multi-year plans. The 828 customers each spending over $1 million annually are, by definition, running mission-critical workloads that would be enormously painful to move. Every additional workload a customer adds — a new AI application, a new data-sharing relationship, a new Native App — raises the exit cost further.
Moat Type 2: Network Effects via Data Sharing
Snowflake’s second moat is a genuine network effect, which is rare in infrastructure software. Through the Snowflake Marketplace and secure data sharing, customers can share live data with partners, suppliers, and customers who are also on Snowflake — without copying or moving the data. Each new organization that joins Snowflake makes the platform more valuable to every existing member, because there is one more counterparty to exchange data with directly. This is the classic dynamic that made networks like credit-card systems and marketplaces so defensible. As data-sharing relationships proliferate, they create a gravitational pull: a company whose key partners are all on Snowflake faces strong pressure to join, and once joined, faces strong pressure to stay. Clean rooms — which allow two parties to analyze combined data without either revealing its raw data to the other — deepen this effect in privacy-sensitive industries like advertising, media, and financial services.
Moat Durability Assessment
Will these moats hold for 5–10 years? The switching-cost moat is highly durable: the sheer difficulty of migrating a governed enterprise data estate does not diminish over time, and it grows as more workloads are added. The network-effect moat is durable so long as Snowflake remains the neutral, cross-cloud standard for data sharing. The principal risk to the moat is the hyperscalers, who can bundle competitive data services into their clouds at aggressive prices and integrate them tightly with the AI tools enterprises are adopting — Microsoft Fabric bundled with Copilot and Power BI is the sharpest example. The counterargument is threefold: (1) Snowflake’s cross-cloud neutrality is a feature the single-cloud hyperscalers structurally cannot copy, because their commercial incentive is lock-in; (2) large enterprises deliberately maintain multi-cloud strategies precisely to avoid hyperscaler dependence, which favors a neutral layer like Snowflake; and (3) Snowflake’s ease-of-use and governance advantages have proven sticky across a decade of hyperscaler competition. On balance, the moat appears intact and, in the AI era, arguably strengthening — because governed, cross-cloud data is exactly what safe enterprise AI requires.
4. Financial Analysis
Snowflake’s financial profile is that of a high-growth infrastructure company that is deliberately prioritizing growth and R&D over near-term GAAP profitability. Reading these financials requires holding two facts at once: the company is growing revenue rapidly with strong cash generation on a non-GAAP basis, and it still reports large GAAP net losses driven overwhelmingly by stock-based compensation.
Revenue trend (fiscal year ends January 31).
Fiscal Year Total Revenue Product Revenue Product Growth YoY FY2024 (Jan 2024) ~$2.81B ~$2.67B ~36% FY2025 (Jan 2025) ~$3.63B ~$3.46B ~29% FY2026 (Jan 2026) ~$4.66B ~$4.48B ~30% TTM (through Q2 FY2027) $5.43B ~$5.1B — FY2027 guidance — $6.07B ~36% (accelerating intra-year)
The story behind the numbers: growth decelerated steadily from FY2024 into FY2025 as the base grew and as customers optimized their spending — the “efficiency era” of 2023–2024 hit consumption models hard. FY2026 stabilized in the low-30s. Then, in the first half of FY2027, product growth reaccelerated to 37% in Q2, the standout development in this report, driven by AI workload consumption. Management’s raised FY2027 product guide of $6.07 billion (up from $5.84 billion in May) and a Q3 guide of $1.59 billion codify that the reacceleration is expected to persist, not fade.
Key operating metrics (the numbers that matter most for a consumption business):
– Net revenue retention: 126% — existing customers expanding spend.
– Remaining performance obligations (RPO): $9.0 billion — contracted future revenue; grew ~42% year-over-year exiting FY2026 to $9.77B and remains near $9–10B.
– Customers >$1M TTM product revenue: 828 — the high-value cohort.
– Non-GAAP product gross margin: ~75% — healthy for infrastructure that pays hyperscalers for underlying compute.
Margins and profitability. On a non-GAAP basis, Snowflake is solidly profitable at the operating line: non-GAAP operating margin expanded more than 400 basis points year-over-year to about 15% in Q2 FY2027, and management guides to a 14.5% non-GAAP operating margin for the full year. On a GAAP basis, however, the company reported a net loss of $191.7 million (−$0.55 per share) in the quarter — an improvement from the −$297.9 million (−$0.89 per share) loss a year earlier. On a trailing-twelve-month basis, Finviz reports net income of −$1.09 billion and trailing EPS of −$3.17, with a GAAP operating margin of −22.3% and a net profit margin of −20.1%. The gap between the 15% non-GAAP operating margin and the negative GAAP margin is almost entirely stock-based compensation — a real cost to shareholders through dilution, but not a cash cost. TTM return metrics reflect the GAAP losses: ROE −48.2% and ROA −12.9%, figures that are not meaningful for valuing a deliberately loss-generating growth company and should not be over-weighted.
Balance sheet and cash flow. Snowflake maintains a strong liquidity position with several billion dollars in cash and investments, and the reported Debt/Equity of 1.29 reflects convertible notes rather than traditional leverage; the company is not financially stressed. Critically, Snowflake is free-cash-flow positive: it reported a ~6% non-GAAP adjusted free cash flow margin in Q2 (a seasonally lighter quarter) and reaffirmed a full-year adjusted FCF margin guide of ~23%. On a ~$6.1B+ revenue base, a 23% FCF margin implies well over $1.4 billion of annual free cash flow — which is why the GAAP net loss, while real, does not imply financial fragility.
Path to GAAP profitability / margin expansion story. The core financial thesis is operating leverage. Gross margins are already high (~75% non-GAAP product), so as revenue scales, non-GAAP operating margin should keep climbing toward the company’s long-term targets. The bridge to GAAP profitability depends on stock-based compensation shrinking as a percentage of revenue over time — a trend the company has been executing on, and one investors should monitor quarter by quarter alongside share-count dilution.
5. Valuation
Why not P/E. With trailing EPS of −$3.17, Snowflake has no meaningful trailing P/E — a conventional earnings multiple is not applicable to a company posting GAAP losses. The forward P/E on the consensus EPS-next-year estimate of $3.00 is 111.7×, which is optically extreme and reflects that the market is valuing Snowflake on growth and cash flow, not near-term earnings. Accordingly, we anchor the valuation on price-to-sales / EV-to-sales and a forward-looking view, cross-checked against consensus and cash generation.
Current multiples (verified against the fetched data):
– Market cap: $118.29 billion on 352.45 million shares at $335.29.
– Trailing P/S: 21.8× (market cap ÷ TTM revenue of $5.43B — self-checks to the reported 21.77).
– Forward P/S: ~19.5× on the FY2027 product revenue guide of $6.07B (or ~18.8× on total FY2027 revenue of roughly $6.3B).
– P/B: 55.0× (not meaningful for an asset-light software company).
Step-by-step base-case valuation. We value Snowflake on forward revenue, the standard approach for high-growth, pre-GAAP-profit software:
1. Start with FY2027 product revenue guidance of $6.07B, growing ~36% and accelerating.
2. Roll forward to a next-twelve-months (NTM) product revenue estimate of approximately $7.0B, assuming growth stays in the high-20s to low-30s given AI-driven reacceleration.
3. Apply a forward P/S multiple. Snowflake currently trades near 19.5× forward sales. For a business reaccelerating to mid-30s growth with 75% gross margins and 23% FCF margins, a 20× multiple on NTM product revenue is defensible but not aggressive.
4. 20× × $7.0B ≈ $140B enterprise value, plus a few billion of net cash, over 352.45M shares → a base-case fair value of roughly $400–410 per share.
Scenario analysis:
Scenario Key Assumptions Implied Fair Value Return vs. $335.29 Bull Growth sustains 30%+, AI monetization drives NRR back up, multiple holds ~24× NTM ~$480 +43% Base Growth normalizes high-20s/low-30s, ~20× NTM product revenue ~$405 +21% Bear Growth decelerates toward ~20%, multiple compresses to ~14× on macro/competition ~$250 −25%
Comparison to analyst consensus. The consensus price target is $438.10 (Finviz), implying ~31% upside from the current price, and 32 of 35 covering analysts rate the stock a Buy with only 3 Holds. Post-earnings, Morgan Stanley moved to $470, Argus to $450, and Goldman Sachs to $436. Our base case of ~$405 is modestly below the Street’s $438 — we agree with the direction (meaningful upside) but apply a slightly more conservative multiple to reflect the rich starting valuation and the persistent GAAP losses. In other words, we think the consensus is directionally right but is pricing the reacceleration close to perfection. The margin of safety comes not from the multiple being cheap — it is not — but from the durability of the moat and the visibility of the $9B RPO backlog.
6. Risk Factors
Risk 1: Valuation and multiple compression. This is the most immediate risk. At ~20× forward sales and a 111× forward P/E, Snowflake is priced for sustained high growth and AI monetization. If the reacceleration proves to be a one- or two-quarter phenomenon rather than a durable trend, or if the broader market de-rates high-multiple software (as happened acutely in 2022), the multiple could compress sharply even if the business performs adequately. A move from 20× to 14× forward sales — not unusual in a risk-off environment — implies roughly 30% downside independent of any operational miss. Investors buying here must accept that they are paying a premium for growth, and that premium is the first thing the market takes back when sentiment turns. The 52-week range of $118.30 to $384.56 is a vivid reminder of how violently this stock can move.
Risk 2: Hyperscaler and Databricks competition. Snowflake’s moats are real but not impregnable. Microsoft, Amazon, and Google can bundle competitive data-and-AI services into their clouds at aggressive prices and integrate them with the AI copilots enterprises are already buying — Microsoft Fabric plus Copilot is the clearest threat. Databricks, meanwhile, is converging on Snowflake’s analytics turf while retaining its data-science and open-format strengths, and is a formidable competitor for exactly the AI workloads that are now the growth engine. If a hyperscaler’s bundle or Databricks’ AI-native platform wins a disproportionate share of new AI workloads, Snowflake’s reacceleration could stall. The consumption model cuts both ways here: just as usage compounds up, it can shrink quickly if customers shift new workloads elsewhere, because there is no locked-in seat count to cushion the decline.
Risk 3: Stock-based compensation and dilution. Snowflake’s large GAAP losses are driven overwhelmingly by stock-based compensation, and while that is a non-cash expense, it is a genuine cost to shareholders in the form of dilution. If the share count keeps rising faster than the business can grow per-share value, some of the operating leverage investors are counting on will leak away to employees rather than owners. The path to GAAP profitability — as opposed to non-GAAP — depends on management shrinking SBC as a percentage of revenue over time. Investors should track diluted share-count growth and SBC-as-percent-of-revenue every quarter; a failure to bend those curves would undermine the long-term equity story even if revenue growth remains healthy.
Risk 4: Macro sensitivity of consumption revenue. Because revenue tracks usage rather than contracted seats, Snowflake is directly exposed to enterprise IT budget cycles. In a recession or a renewed “cost-optimization” push like 2023, customers can dial down consumption quickly, and revenue growth decelerates in real time — as the company experienced in prior years. This makes the stock more macro-sensitive than a pure subscription-seat business.
7. Conclusion & Exit Plan
Investment rating: Buy. Snowflake offers a rare combination in software — reaccelerating revenue growth (37% product growth) and expanding margins (non-GAAP operating margin +400bps to ~15%), underpinned by a strengthening switching-cost-and-network-effect moat and a $9 billion contracted backlog. We stop short of “Strong Buy” only because the valuation already prices in a great deal of good news and because the company remains GAAP-unprofitable, leaving limited margin of safety if the reacceleration stumbles.
Entry price range. We view $300–335 as a reasonable accumulation zone — essentially the current level and modest pullbacks. Given the stock’s volatility (a 52-week low of $118.30), disciplined investors may prefer to build a position in tranches rather than all at once, leaving capital to add on any market-driven drawdown toward the low-$300s or below.
Exit conditions:
– Target achieved: trim toward our base-case fair value of ~$405 (near consensus); take additional profits into the bull-case ~$480 if AI monetization and NRR clearly inflect higher.
– Fundamental break: reduce or exit if product revenue growth decelerates back below ~25% for two consecutive quarters without a corresponding step-up in margins, or if net revenue retention resumes a sustained decline below ~120% — either would signal the AI-consumption thesis is not compounding as expected.
– Time-based: reassess the full thesis in 6–12 months, or immediately after any quarter in which guidance is cut.
Item Detail Company Snowflake Inc. (SNOW) Current Price $335.29 Target Price ~$405 (base) / $438 consensus Upside +21% (base) / +31% (consensus) Rating Buy Key Thesis AI workloads reaccelerate consumption growth to 37% on a strengthening switching-cost + network-effect moat Main Risk Rich valuation (~20× forward sales) vulnerable to multiple compression
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Disclaimer:
This content is general investment information provided to an indefinite/unspecified audience by a quasi-investment advisory business registered under Korea’s Financial Investment Services and Capital Markets Act, and is not personalized 1:1 investment advice tailored to any individual investor. This analysis is for informational purposes only and is not a solicitation to invest. All investment decisions and their consequences rest solely with the investor. The estimates and assumptions in this report are as of the writing date (2026-09-08) and may not materialize depending on market conditions and geopolitical variables. Financial data used reflects sources such as company filings and analyst consensus, and the scenarios and price targets represent the author’s conservative assessment. All investments carry the risk of principal loss, and past performance or analytical track record does not guarantee future results. As of the writing date, the author does not hold a position in this stock. The author’s holdings and positions may change without prior notice depending on market conditions.
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