Tag: technology

NVIDIA Q2 FY2027 Earnings: AI Demand Accelerates, but NVIDIA Is Taking on More of the Risk

NVIDIA reported a record quarterly revenue of $96.2 billion, driven by a significant increase in Data Center sales, which reached $89 billion. Despite strong growth and positive market response, increased commitments for supply and infrastructure raise risks. Analysts estimate a fair value of around $305 per share, making it an attractive investment option but with caution advised due to potential economic pressures on customer spending.

TL;DR Summary

NVIDIA (NVDA:NASDAQ) delivered another exceptional quarter. Revenue more than doubled to $96.2 billion, Data Center revenue reached $89.0 billion, and non-GAAP earnings per share increased 120% to $2.22.

Management’s Q3 revenue guidance of $108 billion indicates that demand continues to accelerate even without any assumed China Data Center compute revenue. The strongest signal was not just hyperscaler spending: revenue from AI clouds, industrial companies and enterprise customers grew 25% sequentially and 138% year over year.

The market initially responded enthusiastically. NVIDIA rose 8.7% in the first trading session after earnings, before giving back part of the gain the following day. At the August 28 closing price of $217.55, the shares remained 3.8% above their pre-earnings level.

However, the quarter also revealed a significant evolution in NVIDIA’s risk profile. Supply and capacity commitments jumped from $119 billion to $279 billion. Accounts-receivable days increased from 45 to 60, and NVIDIA is committing capital, cloud capacity and potentially substantial financial guarantees to support the wider AI infrastructure ecosystem.

Our central fair-value estimate is approximately $305 per share, with a reasonable range of $270 to $330. At $217.55, the stock offers attractive upside for a tech-savvy growth investor—but the investment case increasingly depends on whether NVIDIA’s customers can generate enough AI revenue to sustain their infrastructure spending.

Quarter Recap

NVIDIA reported fiscal second-quarter results on August 26, 2026, covering the period ended July 26.

Revenue reached a record $96.2 billion, increasing 18% from the previous quarter and 106% from a year earlier. The company’s growth was once again driven by demand for accelerated computing and AI infrastructure.

GAAP gross margin was 75%, broadly unchanged sequentially and 260 basis points higher than a year earlier. The margin improvement was driven by a more favorable Blackwell Ultra product mix.

GAAP operating income reached $63.7 billion, up 124% year over year, while non-GAAP operating income increased at the same rate to approximately $64.0 billion.

GAAP diluted earnings per share were $2.46, compared with $1.08 a year earlier. Non-GAAP EPS increased 120% to $2.22.

The distinction between GAAP and non-GAAP earnings is important in this quarter. GAAP net income included approximately $7.8 billion of gains from equity securities. Those gains should not be treated as recurring operating earnings, making non-GAAP EPS the more suitable foundation for valuation.

Key Highlights

Data Center revenue more than doubled

Data Center revenue reached $89.0 billion, increasing 18% sequentially and 117% year over year. It now accounts for approximately 92.5% of NVIDIA’s total revenue.

Blackwell Ultra was the primary growth driver. Management said Blackwell represented the vast majority of revenue, while the next-generation Vera Rubin platform had already entered production.

The scale of NVIDIA’s Data Center business is extraordinary. It generated almost twice as much quarterly revenue as the entire company did one year earlier.

AI demand is broadening beyond hyperscalers

Hyperscale revenue reached $48.7 billion, up 13% sequentially and 102% year over year.

More importantly, AI Clouds, Industrial and Enterprise revenue reached $40.3 billion. This category increased 25% sequentially and 138% year over year.

That diversification matters. One of the main concerns surrounding NVIDIA has been that its growth depends on a small number of technology companies continually increasing their capital expenditure. The latest results suggest that demand is spreading to AI-native companies, sovereign customers and traditional enterprises.

This does not eliminate hyperscaler concentration, but it makes the growth thesis broader than it was several quarters ago.

Operating leverage remained exceptional

GAAP operating expenses increased 55% year over year, largely because of higher computing-infrastructure and compensation costs.

That is a substantial increase in absolute terms, but it was far below the 106% growth in revenue. As a result, GAAP operating margin expanded from 60.8% to 66.2%.

NVIDIA is simultaneously funding new processors, networking products, software and AI models while still allowing revenue growth to translate into higher operating profitability.

Q3 guidance points to continued acceleration

Management expects Q3 revenue of approximately $108 billion, plus or minus 2%.

At the midpoint, this represents sequential growth of approximately 12.2%. The guidance assumes no Data Center compute revenue from China, meaning that the next quarter does not require a recovery in that market.

Management expects GAAP and non-GAAP gross margins of approximately 74%, down from 75% in Q2. The decline is manageable, particularly during the transition to increasingly complex rack-scale infrastructure, but future margin direction deserves close attention.

Vera Rubin has entered production

The Vera Rubin platform is ramping into production, with racks running at partners including Microsoft Azure, Google Cloud, Oracle Cloud Infrastructure, CoreWeave and Nebius.

This reduces the risk of a material product-cycle pause between Blackwell and Rubin.

NVIDIA’s annual product cadence is also becoming an important competitive advantage. Customers are not simply buying more accelerators; they are moving through recurring generations of processors, networking and integrated systems.

The principal risk is execution. These rack-scale systems require advanced packaging, memory, networking, cooling and power infrastructure. Supply constraints or integration problems anywhere in the system could affect deployment schedules.

The AI buildout is consuming more of NVIDIA’s balance sheet

The most consequential disclosure was the expansion of NVIDIA’s future commitments.

Supply and capacity commitments increased from $119 billion in the previous quarter to $279 billion, primarily because NVIDIA is securing memory and other components required to meet anticipated demand.

Total disclosed future commitments reached approximately $366 billion, including:

  • Supply and manufacturing capacity.
  • Cloud-service agreements.
  • Data-center leases.
  • Equity investments.
  • Capital expenditures.

NVIDIA also disclosed $36 billion of AI-cloud agreements under which it may purchase capacity not sold to third-party customers.

Separately, its maximum gross guarantee exposure included $3.5 billion related to certain AI-cloud partners and as much as $105 billion connected to a phased data-center development intended for OpenAI.

These maximum figures are not current losses or immediate liabilities. Many obligations are conditional, phased and spread over several years. Nevertheless, they represent a material change in NVIDIA’s role.

NVIDIA is no longer only supplying the AI infrastructure industry. It is helping secure components, cloud capacity, financing and physical data-center development for that industry.

Cash conversion weakened during the quarter

Operating cash flow was $24.1 billion, up from $15.4 billion a year earlier but down sharply from $50.3 billion in Q1.

Some of the sequential decline reflected cash-tax payments and working-capital requirements. However, accounts receivable increased to $63.1 billion, while days sales outstanding rose from 45 to 60.

NVIDIA attributed the increase to extended payment terms under large, multi-quarter agreements with investment-grade customers.

Inventory also rose from $25.8 billion to $31.6 billion as NVIDIA prepared for Rubin production.

Neither development proves that demand is weakening. Both are consistent with a company preparing for rapid expansion. But they show that more cash is becoming tied up in inventory and customer financing.

SWOT Analysis

NVIDIA’s operating performance strengthened the growth thesis, but the quarter also widened the range of potential outcomes. The estimated price impacts below are sensitivity ranges rather than additive forecasts.

Strengths

  • Exceptional revenue momentum: estimated price impact of +12% to +25%. Revenue increased 106% year over year, while Q3 guidance implies another 12% sequential increase.
  • Dominant AI infrastructure platform: +10% to +20%. NVIDIA combines processors, networking, systems and software, making its competitive advantage broader than raw accelerator performance.
  • Broadening demand: +8% to +18%. AI Clouds, Industrial and Enterprise revenue grew 138% year over year, indicating that adoption is expanding beyond the largest hyperscalers.
  • Strong operating leverage: +8% to +15%. Revenue grew almost twice as quickly as operating expenses, lifting GAAP operating margin to 66.2%.
  • Sustained pricing power: +7% to +15%. NVIDIA maintained a 75% gross margin while scaling complex Blackwell Ultra infrastructure.
  • Rubin product transition: +8% to +18%. Early production reduces product-cycle risk and creates another reason for customers to expand and upgrade their infrastructure.
  • Strong liquidity: +3% to +8%. NVIDIA held $56.6 billion in cash, cash equivalents and marketable debt securities, in addition to $42.8 billion of marketable equity securities.

Weaknesses

  • Dependence on Data Center spending: estimated price impact of −12% to −25%. Approximately 92.5% of revenue now comes from Data Center, leaving NVIDIA exposed to any AI capital-spending slowdown.
  • Customer concentration: −8% to −18%. One direct customer represented 16% of Q2 revenue, while three customers each represented at least 13% of first-half revenue.
  • Longer customer payment terms: −5% to −12%. Days sales outstanding increased from 45 to 60 as NVIDIA extended terms on large customer agreements.
  • Weaker cash conversion: −5% to −12%. Quarterly operating cash flow was considerably below reported net income because of taxes and working-capital investment.
  • Investment gains inflated GAAP earnings: −3% to −8%. Approximately $7.8 billion of Q2 GAAP earnings came from gains on equity securities.
  • Increasing inventory exposure: −4% to −10%. Inventory rose to $31.6 billion ahead of Rubin, increasing the potential cost of forecasting or execution errors.

Opportunities

  • Global AI infrastructure expansion: estimated price impact of +15% to +35%. Multiple frontier laboratories, AI clouds, enterprises and sovereign customers are expanding capacity simultaneously.
  • Inference and agentic AI demand: +10% to +25%. Reasoning models and AI agents could generate substantially more inference workloads than conventional chat applications.
  • Recurring product upgrades: +10% to +22%. The transition from Blackwell to Rubin can make AI infrastructure an ongoing replacement and expansion market.
  • Enterprise and sovereign adoption: +8% to +20%. Growth outside hyperscalers could make NVIDIA’s revenue base more diversified and durable.
  • Networking and software monetization: +6% to +15%. NVIDIA can capture a larger share of AI-factory spending by selling integrated systems rather than individual chips.
  • Potential China recovery: +3% to +10%. Any legally permitted return to the Chinese Data Center market would be incremental because management’s Q3 guidance assumes no China compute revenue.
  • Infrastructure partnerships: +5% to +18%. Financing and capacity arrangements could remove obstacles preventing customers from deploying more NVIDIA systems.

Threats

  • Unproven customer economics: estimated price impact of −20% to −40%. If AI applications cannot generate sufficient revenue or cost savings, customers may eventually reduce infrastructure spending.
  • Commitments and guarantees: −15% to −35%. NVIDIA is accepting more supply, counterparty and infrastructure risk to support the expansion of its ecosystem.
  • Circular-demand concerns: −10% to −25%. Investors may assign a lower valuation if more sales depend on NVIDIA investing in customers, committing to their capacity or extending payment terms.
  • Margin normalization: −8% to −18%. Higher memory costs, system complexity, tariffs and competition could reduce NVIDIA’s unusually high gross margin.
  • Custom accelerators and competing platforms: −12% to −30%. Hyperscalers are developing internal chips, while other semiconductor companies are expanding their accelerator offerings.
  • Export restrictions: −6% to −15%. China currently contributes little to Data Center revenue, but continued restrictions remove a large potential market and encourage competing local ecosystems.
  • Supply-chain execution: −8% to −20%. NVIDIA depends on advanced packaging, memory, networking, cooling and external manufacturing partners.
  • Valuation sensitivity: −10% to −25%. At a market capitalization above $5 trillion, even strong operating results may not prevent a decline if interest rates rise or the market assigns a lower earnings multiple.

Valuation Scenarios

The valuation uses estimated FY2028 earnings, giving NVIDIA approximately 12–18 months to execute beyond the reported quarter.

The analysis begins with estimated FY2027 normalized EPS of approximately $9.10 to $9.20. It excludes unrealized investment gains and uses management’s Q3 revenue, margin, expense and tax guidance.

Bear case: $159

The bear case assumes that the AI infrastructure cycle slows sharply after the current order backlog is fulfilled.

  • FY2028 revenue reaches approximately $425 billion.
  • Revenue growth slows to approximately 5%.
  • Non-GAAP net margin falls to 45%.
  • EPS is approximately $7.94.
  • The market applies a 20-times earnings multiple.
  • Estimated value is approximately $159 per share.
  • Downside from $217.55 is approximately 27%.
  • Assigned probability is 25%.

This scenario does not require NVIDIA to lose its technological leadership. It assumes that customers moderate spending, margins normalize and investors apply a lower multiple because of NVIDIA’s growing commitments and financing exposure.

Base case: $313

The base case assumes that AI demand remains strong but normalizes from its present triple-digit growth rate.

  • FY2028 revenue reaches approximately $527 billion.
  • Revenue grows approximately 30%.
  • Non-GAAP net margin is approximately 51%.
  • EPS is approximately $11.19.
  • The market applies a 28-times earnings multiple.
  • Estimated value is approximately $313 per share.
  • Upside from $217.55 is approximately 44%.
  • Assigned probability is 55%.

This scenario assumes a successful Rubin ramp, continued enterprise and AI-cloud adoption, modest margin normalization and no material losses from NVIDIA’s commitments or guarantees.

Bull case: $469

The bull case assumes that inference, AI agents, sovereign AI and physical AI create another major acceleration in demand.

  • FY2028 revenue reaches approximately $608 billion.
  • Revenue grows approximately 50%.
  • Non-GAAP net margin remains around 54%.
  • EPS reaches approximately $13.78.
  • The market applies a 34-times earnings multiple.
  • Estimated value is approximately $469 per share.
  • Upside from $217.55 is approximately 116%.
  • Assigned probability is 20%.

This outcome requires NVIDIA to maintain platform leadership while its customer-support arrangements successfully accelerate genuine third-party demand.

Probability-weighted value

Weighting the three scenarios produces an estimated value of approximately $306 per share.

To avoid false precision, our final central fair-value estimate is $305, with a reasonable range of $270 to $330.

At $217.55:

  • The shares trade approximately 29% below the $305 central estimate.
  • Appreciation to fair value would be approximately 40%.
  • The shares trade approximately 19% below the conservative $270 fair-value boundary.
  • Appreciation to $270 would be approximately 24%.

Verdict

NVIDIA’s Q2 FY2027 results provided strong evidence that the AI infrastructure cycle remains intact.

Revenue more than doubled. Data Center demand accelerated. Growth broadened beyond hyperscalers. Blackwell Ultra supported a 75% gross margin, and Rubin entered production without an evident pause in customer demand.

At $217.55, investors do not need NVIDIA to maintain triple-digit growth indefinitely to justify a higher valuation. Our central case assumes approximately 30% FY2028 revenue growth and produces a fair value close to $305.

That makes the risk-reward attractive for a tech growth investor.

However, the quarter also marked a turning point. NVIDIA is increasingly using its balance sheet, supplier commitments, investments, cloud-capacity agreements, extended payment terms and guarantees to support the AI infrastructure ecosystem.

The critical investment question is therefore changing.

It is no longer simply:

Will customers continue buying NVIDIA systems?

The more important question is:

Can those customers generate enough economic value from AI to finance the systems without progressively greater support from NVIDIA?

If the answer is yes, NVIDIA may still be in the early stages of a much larger infrastructure cycle. If the answer is no, the company’s expanded commitments could magnify the eventual slowdown.

Our conclusion is that NVIDIA remains an attractive growth investment near $217.55, but it should not be treated as low risk merely because it trades below estimated fair value. The operating moat is exceptional; the ecosystem’s financial durability is still being tested.

Call to Action

Would you buy NVIDIA at approximately $218, or do its growing infrastructure commitments and customer-financing exposure make you demand a larger margin of safety?

Follow SWOTstock for earnings analysis that separates operating performance, market expectations and valuation risk.

Disclaimer

This article is for informational and educational purposes only and does not constitute investment advice, financial advice or a recommendation to buy or sell any security.

Valuation estimates depend on assumptions about revenue growth, profitability, share count and valuation multiples. Actual results may differ materially. Investors should conduct their own research and consider their objectives, financial circumstances and risk tolerance before making an investment decision.

Official sources include NVIDIA’s Q2 FY2027 earnings release, CFO commentary, Form 10-Q and official earnings webcast.

AI Revenue Is Appearing. But Does It Justify the Data-Center Boom?

The debate on AI’s economic viability focuses on its rapid adoption across industries versus its ability to generate sufficient revenue. While monday.com shows promise with increased AI revenue, questions about profitability and gross margins remain. The industry’s future hinges on proving that AI can deliver sustainable financial returns to justify heavy infrastructure investments.

The central economic question surrounding artificial intelligence is no longer whether people will use it.

Usage is growing rapidly across design, marketing, customer support, software development and enterprise workflows. That usage is already translating into demand for GPUs, cloud capacity, electricity and new data centers.

The unresolved question is whether the applications consuming all that infrastructure can make enough money from AI to pay for it.

Recent results from Figma and HubSpot illustrated the concern. Both companies reported substantial AI adoption, but neither provided enough evidence to show that AI usage was producing proportional incremental revenue.

monday.com has now supplied a more encouraging data point. Its AI-product annual recurring revenue doubled sequentially and represented 17% of net-new ARR during the second quarter. Customers are also exhausting their included AI-credit allowances and purchasing additional credits.

That is genuine monetization.

But it does not resolve the economic question. monday.com did not disclose the absolute amount of AI revenue, the growth in AI consumption or the gross margin earned on that revenue.

The evidence is moving from “AI usage without visible revenue” toward “AI revenue without proven profitability.”

For the application-software industry—and ultimately for the massive data-center investment cycle—that distinction matters.

Infrastructure demand arrived before application economics

The AI investment cycle developed in an unusual order.

Normally, profitable applications create demand for the infrastructure needed to support them. With generative AI, infrastructure was built first, based largely on expectations of future demand.

Hyperscalers ordered GPUs, expanded cloud regions and committed billions of dollars to new data centers before most enterprise applications had established durable pricing models.

That sequence was understandable. Compute capacity was scarce, model development was accelerating and no major technology company wanted to risk falling behind.

But the infrastructure ultimately requires an economic buyer.

An AI application must create enough value for customers to pay for it. The resulting revenue must then cover inference, hosting, product development, sales and support. Only after those costs can the application generate an attractive return.

The economic chain therefore looks like this:

Customer productivity must create willingness to pay.

Willingness to pay must become recurring application revenue.

Application revenue must exceed AI delivery and development costs.

Application profitability must sustain demand for cloud and data-center capacity.

AI usage alone completes none of those steps.

What Figma and HubSpot showed

Figma and HubSpot demonstrated that AI can spread quickly when it is embedded within an existing application.

Figma reported that more than 80% of its larger customers were using AI credits weekly. HubSpot said more than 55% of its Professional and Enterprise customers had adopted its Breeze AI capabilities, while agentic actions had more than tripled.

Those are impressive adoption figures. However, the companies did not clearly separate paid AI usage from credits, trials and capabilities included within existing subscriptions.

The financial results also demonstrated why the distinction matters.

Figma’s second-quarter revenue grew 48% to $370.1 million, but its free-cash-flow margin declined from 24% to 14%. HubSpot increased revenue by approximately 20% to $911.7 million and expanded its operating margin, but its non-GAAP gross margin declined by 1.5 percentage points.

Neither result proved that AI caused the margin changes. Other investments, product-mix effects and timing factors were involved.

But they exposed an analytical blind spot: companies were providing increasingly precise information about AI adoption while offering very little information about AI revenue and unit economics.

The market could see the usage. It could not see the profit.

Figma’s official Q2 2026 results

HubSpot’s official Q2 2026 investor presentation

monday.com provides a more encouraging answer

monday.com’s results differ in one important respect: the company provided direct evidence that customers are spending more because of AI.

AI-product ARR doubled sequentially and generated 17% of the net-new ARR added during the quarter.

More importantly, management said customers are reaching the limits of their included AI-consumption allowances and purchasing additional credits. Customers are not simply experimenting with a feature included in their subscription. Some are consuming enough AI to make an incremental purchasing decision.

The company’s seats-plus-credits structure gives it two revenue engines:

Human workers generate seat revenue.

AI workers generate consumption revenue.

Before introducing consumption pricing, monday.com generally needed customers to add employees or deploy the software to more users to expand an account. AI credits potentially allow customer spending to grow even if human headcount remains unchanged—or declines.

This is particularly relevant because one of AI’s proposed benefits is that companies can accomplish more work with fewer employees. Traditional per-seat software pricing becomes vulnerable if that prediction proves correct. Consumption pricing provides a possible way for software vendors to participate in the productivity gains rather than lose revenue because of them.

monday.com therefore offers early evidence that application companies can turn AI execution into a new revenue stream.

Why workflow AI may monetize better

The emerging difference may be between AI that assists and AI that executes.

A general AI assistant can help write an email, summarize a document or generate ideas. Customers may value those functions but still view them as features that should be included within an existing subscription.

The willingness to pay becomes clearer when AI performs a measurable unit of work:

  • Resolving a customer-service request
  • Qualifying a sales lead
  • Updating a CRM record
  • Processing an operational workflow
  • Building an automation
  • Completing a repetitive project-management task

In these cases, the customer can compare the cost of the AI credits with the time or labor saved.

monday.com also benefits from being an incumbent application provider. Its platform already contains customer data, workflows, permissions and integrations. A standalone AI startup may have a capable model, but it must still obtain access to the relevant data and fit itself into the customer’s existing processes.

The incumbent does not need to replace the entire workflow. It can place AI inside it.

This supports a broader conclusion for SAP, Intuit, ServiceNow, Salesforce and other established application companies. Their traditional software businesses may be an advantage rather than a liability. They can distribute AI to existing customers, connect it to trusted business data and experiment with monetization without relying entirely on AI revenue to finance the company.

But 17% of net-new ARR is not 17% of the business

The monday.com evidence must not be overstated.

AI represented 17% of net-new ARR—not 17% of total ARR. The company crossed $1.5 billion in overall ARR during July, while management acknowledged that the absolute AI figure remains small.

During the earnings call, an analyst estimated that AI could represent approximately 1% of total ARR. Management did not confirm that estimate and declined to provide an absolute number.

The 17% figure tells us that AI is becoming a meaningful component of incremental growth. It does not tell us that AI is already a major component of the existing business.

Nor can we compare revenue growth with consumption growth. monday.com described usage patterns as deep and accelerating but did not disclose the number of AI actions, credits consumed or associated computing costs.

We know that some customers are topping up. We do not know how quickly their consumption is growing relative to what they pay.

That relationship determines the economics.

Monetization does not necessarily mean profitability

monday.com’s cost of revenue increased from $31.2 million to $42.7 million, or approximately 37%, while revenue grew 22%.

GAAP gross margin declined from 90% to 88%, while non-GAAP gross margin declined from 90% to 89%.

Adjusted free cash flow also fell from $64.1 million to $52.3 million, and the associated margin declined from 21% to 14%.

These changes cannot be attributed entirely to AI. monday.com did not disclose AI infrastructure costs, and working-capital movements contributed significantly to the cash-flow decline. Currency, product investment and business mix could also have affected gross margin.

At the same time, the company improved its non-GAAP operating margin from 15% to 17%. That demonstrates considerable operating leverage, although much of the improvement came from sales, marketing and administrative efficiency rather than from stronger gross margin.

The result leaves us with an incomplete but important picture:

AI is producing incremental revenue.

The absolute amount remains small.

The gross margin on that revenue is unknown.

Companywide gross margin has moved modestly in the wrong direction.

Operating efficiency elsewhere is currently absorbing the pressure.

This may be commercially viable. But it is not yet proof that AI applications can generate traditional SaaS margins.

monday.com’s official Q2 2026 earnings materials

Why this matters for data-center investment

A dollar of AI application revenue is not automatically equivalent to a dollar of traditional software revenue.

Traditional SaaS products often enjoy high gross margins because the cost of serving an additional customer is relatively small. Generative AI introduces a more direct relationship between usage and expense. Every additional query, generated design, automated workflow or agentic action consumes computing resources.

If application companies cannot charge customers more than the cost of that consumption, increased usage can produce revenue growth without attractive profit growth.

That would not immediately eliminate demand for data centers. Infrastructure spending is supported by multiple sources, including model training, consumer applications, cloud migration, sovereign AI and competition among hyperscalers.

But application profitability matters to the durability of the cycle.

If AI applications struggle to earn acceptable returns, software providers will respond by:

  • Limiting included usage
  • Raising prices
  • Routing simple tasks to smaller models
  • Adopting cheaper open-source models
  • Moving suitable workloads to devices
  • Reducing unnecessary agentic actions
  • Negotiating lower cloud and inference prices
  • Abandoning AI functions customers will not pay for

These actions would make AI more efficient. They could also reduce the rate at which compute demand grows.

The danger for data-center investors is therefore not necessarily that AI usage suddenly collapses. The more plausible risk is that economic optimization causes compute demand to grow more slowly than infrastructure capacity.

A moderate shortfall matters when investment assumptions require exceptionally high utilization over many years.

monday.com refines rather than rejects the thesis

Figma and HubSpot suggested that impressive AI adoption does not automatically produce visible incremental revenue.

monday.com shows that a consumption-based workflow model can produce genuine paid demand.

Together, the three companies point toward a more nuanced thesis:

AI monetization is likely to be strongest when the application performs measurable work, operates inside an established workflow and charges according to consumption. But whether that revenue can retain traditional software margins remains unproven.

This is more encouraging for incumbent application companies than for standalone AI startups.

An incumbent can support early AI investment with existing subscription revenue. It already possesses customers, workflows, integrations and distribution. A startup dependent entirely on AI usage must recover inference, development and customer-acquisition costs from a new and still uncertain revenue stream.

But the same evidence is less reassuring for the infrastructure investment thesis. The industry has already committed capital as if large-scale, profitable application demand were inevitable. The application layer is only beginning to demonstrate that customers will pay—and has barely begun to disclose what profit remains.

The next evidence we need

The next stage of the AI investment debate should move beyond adoption statistics.

Investors should ask application companies to disclose:

  • AI ARR or revenue in absolute dollars
  • Paid consumption rather than total usage
  • The percentage of customers purchasing additional credits
  • Repeat top-up and renewal behavior
  • AI revenue growth relative to AI consumption growth
  • The gross margin on AI products
  • Whether AI spending expands or replaces traditional subscriptions

These figures would allow investors to distinguish commercially valuable AI from expensive engagement.

Salesforce will be particularly important because it already reports both Agentforce ARR and Agentic Work Units. If AI consumption, paid revenue and margins grow together, it would provide stronger evidence that application-layer economics can support the infrastructure cycle.

If consumption grows much faster than revenue—or revenue grows without corresponding gross profit—the concern will become harder to dismiss.

SWOTstock conclusion

monday.com has moved the debate forward.

It has shown that customers will pay for AI consumption when the technology performs identifiable work within an established business platform. Its seats-plus-credits model may offer software companies a way to benefit from AI-driven productivity even if human seat growth slows.

But it has not completed the economic case.

The company has not disclosed how much AI revenue it generates in absolute terms, how quickly consumption is growing or what margin it earns after computing costs. Gross margin and cash conversion have weakened, although the evidence is insufficient to identify AI as the sole cause.

For application software, the result is encouraging.

For the data-center buildout, it is only the beginning of the proof.

AI revenue is becoming real. Whether it becomes profitable enough to justify the infrastructure built around it remains the question that matters most.

AI Usage Is Booming. But Is Anyone Making Enough Money From It?

Figma and HubSpot illustrate a gap between AI adoption and profitable monetization. While both companies show strong customer engagement with AI, their financial metrics reveal challenges in converting usage into profits. Upcoming results from monday.com will further test whether this trend is widespread or isolated, impacting future infrastructure demand.

Figma and HubSpot reveal an emerging gap between AI adoption and profitable monetization. monday.com will provide the next important test.

The artificial-intelligence investment thesis has largely rested on one reassuring fact: demand for computing capacity continues to exceed supply.

Applications are generating more images, code, reports, customer-service responses and autonomous actions. Cloud providers need more GPUs, data centres and electricity to serve that usage. As long as consumption continues rising, the enormous AI infrastructure build-out appears justified.

But this argument may be missing one crucial question:

Are the applications generating that demand making enough money from it?

Recent results from Figma (FIG:NYSE) and HubSpot (HUBG:NASDAQ) suggest that AI usage can grow much faster than profitable AI revenue. Customers clearly want AI capabilities, but their willingness to pay may not keep pace with the cost of supplying them.

This does not mean AI demand is artificial or that the data-centre boom is about to collapse. It does, however, raise a more subtle risk: today’s usage may be real without being economically sustainable at today’s infrastructure growth rate.

monday.com’s next earnings report could provide an important third data point.

Figma: Strong AI Adoption, Weaker Cash-Flow Conversion

Figma’s second-quarter results appeared impressive on the surface.

Revenue increased 48% year over year to $370.1 million, accelerating for a third consecutive quarter. Net dollar retention remained strong at 136%, while more than 80% of customers generating over $10,000 in annual recurring revenue were consuming AI credits weekly.

Figma also reported that more than half of these larger customers were using the Figma agent weekly by the end of July.

This is not evidence of weak demand. Customers are actively incorporating AI into their design, prototyping and development workflows.

Figma has also begun monetizing that usage. Its AI-credit system combines monthly allowances with paid credit packages, giving customers the option to purchase additional consumption.

Yet the financial cost of delivering that growth is becoming visible.

Figma’s free cash flow declined to $53.2 million, while its free-cash-flow margin fell from 24% a year earlier to 14%. Non-GAAP operating margin was only 10%, and the company recorded a GAAP operating loss of $117.3 million.

Management also acknowledged that continued investment in AI products could cause gross margins to fluctuate, particularly while newer AI features are not yet fully monetized.

The problem is not that Figma lacks AI users. The problem is that each generative action—creating a design, prototype, image, video or code layer—requires additional computing resources.

Traditional software can serve another user at very low incremental cost. Generative software incurs a new inference cost whenever a user asks the system to create something.

Figma therefore presents an important warning:

AI can accelerate revenue and strengthen customer engagement while still weakening the profitability of each additional dollar of growth.

That distinction matters far beyond Figma. Figma Q2 2026 results

HubSpot: Usage Is Rising Faster Than Proven Monetization

HubSpot provides a second—and in some ways more revealing—example.

Its AI agents are gaining real traction:

  • More than 55% of HubSpot’s Pro+ customers use Breeze Assistant or its agents.
  • Agent adoption increased from the high-single digits to the high teens by July.
  • Monthly agentic actions increased more than threefold during 2026.
  • Data Agent reached more than 16,000 activated customers.
  • Prospecting Agent reached almost 17,000.
  • Customer Agent was being used by more than 10,000 customers during the quarter.
  • Customer Agent resolved approximately 72% of support tickets without human escalation.

These figures suggest customers are progressing beyond experimentation. HubSpot’s agents are increasingly performing meaningful work.

But the monetization evidence is much less convincing.

During the quarter, HubSpot lowered the entry prices for several agents, introduced free trials and shifted toward outcome-based pricing. Management explained that customers wanted proof of value before purchasing AI products and greater predictability over their costs.

This is a critical signal.

Even when an AI product delivers measurable outcomes, customers may resist open-ended token or consumption bills. They want limits, spending controls and a clearer relationship between what they pay and the business value they receive.

HubSpot’s pricing changes encouraged adoption, but they also created a near-term headwind to credit expansion and net revenue retention.

The most revealing moment came when an analyst asked management how much AI-credit consumption came from paid usage rather than bundled allowances or free trials.

Management did not provide the split.

Instead, it emphasized customer reach, depth of usage, agentic actions and business outcomes. Those indicators are positive, but they do not answer the financial question.

If agentic actions have tripled, how much incremental paid revenue did they generate? More importantly, how much gross profit remained after paying the model and infrastructure costs required to perform those actions?

HubSpot does not yet disclose:

  • AI-credit revenue;
  • paid AI customers;
  • paid versus bundled credit consumption;
  • inference cost per credit;
  • AI gross profit;
  • gross margin by agent.

Without these figures, investors cannot determine whether rapid agent adoption is creating a profitable new revenue stream or an increasingly expensive feature that HubSpot must provide to keep its platform competitive.

The company’s margin results offer a partial clue.

HubSpot’s non-GAAP gross margin declined by 1.5 percentage points to 84%, with gross profit growing more slowly than revenue. HubSpot did not attribute the entire decline to AI, so it would be inappropriate to treat this as definitive proof of inference-cost pressure.

However, the direction is consistent with what Figma reported: AI usage is accelerating while gross-margin economics become more challenging.

Unlike Figma, HubSpot successfully offset this pressure elsewhere. Non-GAAP operating margin expanded from 17% to 20.3%, while free cash flow increased 44% to $167.9 million.

HubSpot is reorganizing teams, increasing internal productivity and controlling operating expenses. These efficiencies are currently more than enough to absorb the pressure at the gross-margin level.

That makes HubSpot financially stronger—but it does not prove that its AI products are independently profitable.

HubSpot may be funding its AI transformation through savings from the established software business while waiting for AI monetization to mature. That strategy is sustainable for some time, but it cannot permanently replace attractive AI unit economics. HubSpot Q2 2026 results, HubSpot Q2 earnings materials

Real Usage Is Not the Same as Sustainable Demand

Figma and HubSpot demonstrate why the AI infrastructure debate cannot be resolved by measuring usage alone.

For cloud providers, every additional AI action generates immediate demand for computing capacity. Whether the application making the request earns a profit is initially irrelevant. The cloud provider receives revenue whenever the application consumes resources.

But application companies cannot indefinitely purchase compute that they cannot profitably resell.

They can temporarily absorb the cost through:

  • existing software profits;
  • venture funding;
  • free trials;
  • bundled AI allowances;
  • lower operating expenses;
  • strategic subsidies;
  • expectations of future monetization.

Eventually, however, applications must generate enough revenue to cover their inference costs and still produce an acceptable return.

If customers resist higher prices, application providers will have several options:

  • restrict free usage;
  • reduce included credits;
  • route simpler tasks to cheaper models;
  • use smaller specialized models;
  • replace AI with conventional automation where possible;
  • introduce spending thresholds;
  • charge only for successful outcomes;
  • negotiate lower cloud and model prices.

All these responses improve application economics. But they may also weaken the assumption that every additional AI interaction requires permanently rising amounts of expensive computing capacity.

AI usage could continue growing while the compute required for each action falls.

This creates a potential break between the two trends:

Application usage may continue rising without infrastructure spending continuing to rise at the same rate.

Why This Matters for the Data-Centre Build-Out

The current data-centre boom assumes that rapidly expanding AI consumption will eventually support massive long-term infrastructure investment.

Figma and HubSpot do not disprove that assumption. Two companies are not enough to establish an industry-wide trend, and both companies continue to grow.

But they expose the part of the investment chain that deserves closer scrutiny.

The economic sequence is supposed to work as follows:

AI infrastructure enables applications. Applications create customer value. Customers pay for that value. Application companies earn attractive margins. Those profits support greater cloud consumption. Cloud providers then invest in additional infrastructure.

Figma and HubSpot suggest a possible weakness in the middle of that chain.

Customers are using the products. The applications are delivering outcomes. But pricing remains difficult, free or bundled consumption remains important, and the profitability of AI-specific revenue remains unclear.

If this pattern becomes widespread, the infrastructure boom could eventually encounter a demand-quality problem.

The issue would not be a lack of activity. It would be a lack of economically sustainable activity.

monday.com Is the Next Test

monday.com is scheduled to report second-quarter results on August 10.

The company increasingly presents itself not merely as work-management software but as an AI work platform where agents can automate workflows and perform tasks for customers.

This makes monday.com a useful third test because its workloads differ from those of Figma and HubSpot.

Figma represents generative design and development.

HubSpot represents marketing, sales and customer-service agents.

monday.com represents general workflow automation and work agents.

If monday.com reports rapidly increasing agent usage while maintaining strong gross margins and generating identifiable incremental revenue, it would suggest that the problem is product-specific. Creative generation may be unusually compute-intensive, while structured workflow agents may produce better economics.

But the warning would become much stronger if monday.com reports:

  • rapid growth in agent activations or actions;
  • no separately identifiable AI revenue;
  • extensive bundled or trial usage;
  • lower agent prices;
  • weak conversion from usage to paid credits;
  • cost of revenue growing faster than revenue;
  • deterioration from its historically high gross margin;
  • strong engagement without corresponding improvement in retention or customer expansion.

The decisive relationship will be whether AI revenue and gross profit rise alongside AI actions.

If usage accelerates much faster than monetization while gross margins decline, monday.com would become a third application-layer example of the same emerging pattern.

Three companies would still not prove that the entire software industry faces uneconomic AI adoption. But the evidence would become difficult to dismiss as a company-specific issue.

monday.com Q2 earnings announcement

The SWOTstock View

The AI infrastructure thesis should not be judged only by whether data centres are full today.

Current demand can remain exceptionally strong while the financial returns of the applications generating that demand remain uncertain.

Figma shows that AI can accelerate revenue while weakening cash-flow conversion.

HubSpot shows that AI actions can grow more than threefold while the company lowers prices, expands trials and declines to separate paid consumption from bundled usage.

Neither result proves that AI applications are fundamentally unprofitable. Both companies have strong existing platforms, growing customer adoption and multiple ways to improve efficiency and monetization.

But together they raise a legitimate question:

What happens to long-term infrastructure demand if AI applications generate enormous usage but only modest incremental profit?

The immediate answer is probably very little. Existing data-centre projects, hardware orders and customer commitments will continue.

The longer-term answer depends on whether application companies can convert adoption into profitable revenue before the next major infrastructure investment cycle begins.

If they cannot, applications will optimize, restrict and cheapen their AI consumption. Infrastructure demand may continue growing, but much more slowly than raw usage statistics imply.

That is why monday.com’s report matters.

The next stage of the AI investment debate will not be decided by another record number of prompts, tokens or agent actions. It will be decided by whether those actions produce enough revenue and gross profit to pay for the infrastructure behind them.

For investors, usage is no longer the final proof point.

Profitable usage is.