"Got AI?"

By now, every salesperson for every technology firm is probably being asked “do you have AI” by prospects. And most answers, in most cases, will require the salesperson to ask a further question: “what is your use case?” 

Because right now, most technology buyers are likely reacting to the hype and “FOMO” (Fear of Missing Out). The pressure for businesses to "do something with AI" is going to lead to many deployments that fail to deliver the expected results, as often is the case for new technology. 

Of course, the other typical pressures also exist. Vendors and consultants suggesting AI as a “must-have” will drive such buyer requests. 

But firms might also be engaging in “innovation signaling;” trying to appear innovative to investors, customers, or partners by touting AI initiatives, regardless of substantive application.

Study/Source	Technology	Key Findings on Adoption Behavior	Use Case Clarity
McKinsey Global Survey on AI (2025)	AI, GenAI	Rapid AI adoption; many orgs use AI in multiple functions; limited enterprise-wide impact	Often unclear; many deployments exploratory
IBM AI Business Use Cases (2024)	AI	Lists productive AI use cases; highlights need for business alignment	Stresses importance of defined use cases
NBER Working Paper: Adoption of New Technology	ATMs, Telecom, etc.	Adoption driven by scale, network effects, and competitive pressure	Use cases often emerge post-adoption
Technology Adoption Lifecycle Model	General	Early adoption often driven by hype, FOMO, and signaling	Use case clarity increases over time
Forbes: AI as Fastest Adopted Tech (2023)	AI	AI adoption outpaces previous technologies; driven by hype	Use case definition lags adoption
Harvard Baker Library: Tech Adoption & Economies	Multiple	Adoption lags, driven by economic and social factors	Use cases sometimes secondary

Essentially, the “wasted effort and capital investment” happens because it typically takes some time and experience, plus business process change, before any important new technology can produce measurable outcomes. 

Study Name / Article	Date	Publisher	Key Conclusions
The Gartner Hype Cycle Approach: Understanding the Technology Adoption	2023-12-16	LinkedIn / Gartner	Outlines five phases: innovation trigger, peak of inflated expectations (hype/FOMO), trough of disillusionment, slope of enlightenment, plateau of productivity where useful applications emerge3.
The Role of FOMO in Digital Transformation	2021-01-27	MIT Press: Harvard Data Science Rev.	FOMO drives rapid, sometimes poorly planned digital adoption; over 70% of digital projects fail to deliver intended impact; useful applications emerge only with strategic alignment and learning6.
Technology Adoption Lifecycle - from hype to reality	2022-02-08	THE WAVES	Adoption often starts with hype or fear of missing out, followed by a crash in expectations, and eventually stabilizes as practical, value-driven uses are found7.
Technology Adoption Life Cycle-redefined	2023-12-06	THE WAVES	Adoption unfolds in stages among different user groups; initial hype/FOMO is replaced by economic justification and practical applications as technology matures2.
The Role of Fear of Missing Out and Experience in the Formation of ...	2022	ScienceDirect	FOMO is a significant driver of early technology adoption, but prior experience helps organizations move from hype to rational, use-case-driven adoption1.
Technology Adoption: Escaping the Hype to Maximize Decision ...	2023-01-12	HG Insights	The Hype Cycle model helps organizations distinguish between hype and real business value, guiding them toward effective, use-case-driven adoption8.

AI is not going to be much different, in that regard. 

CoreWeave Provides Food for Both Bulls and Bears

As perhaps often is the case with fast-growing young firms in new areas, observers of CoreWeave’s first quarterly report will find both reasons for optimism and concern. Bulls will point to strong revenue growth, backlogs, revenue growth rates, cash flow and profit margins. 

Bears will likely point to the firm’s present unprofitability, cash burn, debt burdens, need for additional significant capital investment, customer concentration, competition from bigger firms and the potential impact of macroeconomic forces (economic slowdown). As a background issue, some might point to stretched valuation ratios in financial markets overall. 

Such hopes and concerns are typical.

New firms in emerging sectors often experience rapid revenue growth but face uncertainty in sustaining it due to unproven business models, evolving markets, or reliance on a few key customers. Broader market acceptance also is an issue, as adoption rates can vary. 

And, by definition, any emerging firm will be unprofitable for some time. Such issues arguably are magnified for firms requiring heavy capital investment, with the resulting cash flow issues. Depreciation of technology assets also is an issue. 

But bulls will look past all that, pointing to eye-popping growth. CoreWeave reported Q1 2025 revenue of $981.6 million, surpassing the consensus estimate of $853 million, representing a 420 percent year-over-year increase.

Management forecasts $1.06 billion to $1.1 billion in Q2 revenue and $4.9 billion to $5.1 billion for full-year 2025, implying a 363 percent growth rate, exceeding analyst expectations of $4.61 billion.

The revenue backlog stood at $25.9 billion as of March 31, 2025, including $14.7 billion in remaining performance obligations (RPO) and $11.2 billion in estimated future revenue from committed contracts.

Adjusted EBITDA reached $606.1 million, up 480 percent YoY, with an adjusted EBITDA margin of 62 percent, improved from 55 percent a year ago.

Adjusted operating income was $162.6 million, reflecting a 550 percent YoY increase, indicating operational efficiency despite heavy investments.

On the other hand, CoreWeave reported a diluted EPS loss of $1.49, significantly worse than the expected loss of $0.12.

GAAP net income for 2024 was negative at -$863 million, and economic earnings were even lower at -$1.4 billion, contrasting with the more favorable adjusted EBITDA of $1.2 billion, raising questions about financial reporting reliability.

Net interest expense surged to $263.8 million, approximately 27% of quarterly revenue, up 5.5x YoY, driven by a $2.3 billion debt facility with a 14% effective interest rate.

Quarterly loan payments, starting in January 2025, are tied to cash flow and GPU depreciation, with $500 million due per quarter by October 2025, posing a significant financial burden.

CoreWeave anticipates capital expenditures of $20 billion to $23 billion for 2025, raising concerns about sustainability and cash flow.

In 2024, 77 percent of CoreWeave’s revenue came from just two customers, and the firm faces competition from hyperscalers (Microsoft, Amazon, Google) and other startups alike.

But that is what makes markets. 

Hyperscaler Data Center and AI Capex Does Not Seem to be Slowing

The first quarter 2025 quarterly financial reports from Amazon, Alphabet, Microsoft, and Meta provide strong evidence that investments in data centers, particularly for AI and cloud computing, have not slackened, recent near panic notwithstanding.

Amazon reported capital expenditures of $24.3 billion in Q1 2025, a 74 percent year-over-year increase. The company’s CFO, Brian Olsavsky, indicated that 2025 CapEx is expected to exceed $100 billion, with the "vast majority" allocated to Amazon Web Services (AWS) and AI infrastructure.

AWS generated $29.3 billion in revenue, up 17 percent year-over-year, with an operating income of $11.55 billion and a 39.5 percent operating margin, the highest since at least 2014. 

CEO Andy Jassy also emphasized that AWS growth is constrained by data center capacity, not demand. 

Microsoft announced plans to invest $80 billion in fiscal year 2025, largely for AI-driven data centers. In Q1 2025 (fiscal Q3 for Microsoft, ending March 31, 2025), capital spending on property and equipment rose 50 percent year-over-year to $14.92 billion, exceeding analyst expectations of $14.58 billion.

Azure and other cloud services grew 33 percent year-over-year (34 percent in constant currency), with 12 percentage points attributed to AI services. The Intelligent Cloud segment, including Azure, generated $26.8 billion in revenue, up 20 percent, with an operating income of $11.1 billion. 

CEO Satya Nadella also noted that demand exceeds available capacity, driving data center expansion.

Microsoft’s investments include expanding Azure data centers by 40 percent in Europe over the next two years and terminating some data center leases to optimize AI-focused infrastructure.

A $80 billion capex commitment, 50 percent increase in Q1 spending, and strong Azure growth suggest continued growth.

Alphabet projected $75 billion in capex for 2025, primarily for data center infrastructure to support its cloud and AI initiatives. Google Cloud revenue reached $12.26 billion, up 28 percent year-over-year, slightly below expectations of $12.27 billion but with improved margins of 17.8 percent (up from 9.4 percent a year ago).

The $75 billion capex plan and 28 percent cloud revenue growth suggest both the investment push and revenue realization. 

Meta increased its 2025 capex guidance to $64 billion to $72 billion, up from $60 billion to $65 billion.

The point: AI and data center investments by the hyperscalers do not seem to be slackening, some fears of that sort notwithstanding.

Can "Articulate Medical Intelligence Explorer" Outperform Primary Care Physicians?

A study suggests Articulate Medical Intelligence Explorer, a large language model (LLM)-based AI system optimized for diagnostic purposes, might perform better than primary care physicians.

 

The study included 159 case scenarios from providers in Canada, the United Kingdom and India, 20 primary care physicians compared to AMIE, and evaluations by specialist physicians and patient-actors. 

“AMIE demonstrated greater diagnostic accuracy and superior performance on 30 out of 32 axes according to the specialist physicians and 25 out of 26 axes according to the patient-actors,” researchers say. 

The randomized, double-blind crossover study found AMIE achieved higher top-1 and top-3 “differential diagnosis accuracy,” with the correct diagnosis ranked first in 29 percent of cases and within the top 10 in 59 percent of cases, the study found. 

Ever Had to Explain "Cloud" to a Non-Technical User?

Many of us have had the experience of explaining what “cloud” means, or comparing a traditional legacy telecom network architecture to the internet’s design. 

The simple answer likely remains the best: it’s a way, on a network diagram, to show non-specific parts of the network or parts one specific participant does not own or control. In other words, the cloud symbol abstracts all the rest of the network that is out of scope from the standpoint of a particular participant. 

Since the internet uses a virtualized infrastructure, the cloud symbol is a convenient way to show that other devices, network elements and processes are running, but a particular participant does not need to know the details, or actually care very much. 

All that is quite different from past representations of the legacy voice network, which is highly structured, even if many of its processes likewise can be abstracted.

How Will AI Assistants Affect Search, Really

It’s difficult to predict precisely how AI assistants are going to reshape ad-supported search, except to note that some substitution is going to happen. More queries will rely on the summary AI responses, without users navigating to source websites. 

As AI assistants handle a growing number of user queries, there will be measurement challenges around user engagement and therefore advertising effectiveness.

Attribution is a clear issue. When AI assistants are used, it is harder to track the user's path from initial query to final conversion (purchase or sign-up, for example). 

AI assistants synthesize information without direct clicks on specific links, making traditional last-click attribution models less effective or even obsolete. In other words, the consumer journey is largely invisible.

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Referral tracking likewise is more difficult. While some traffic might be identifiable on some AI platforms, AI assistants will generally not make measurement easy or even possible. 

Also problematic are ways to correlate AI interactions with real-world outcomes such as in-store visits.

Click-through rates lose relevance when there is no click-through to measure.

Platforms are experimenting with ads within AI results, including sponsored links or products in Google SGE.

If You Haven't Given Up on Investing in AI

Wedbush analysts pick 30 public firms they believe will " define the future of the AI theme over the coming years."

Analysts led by Daniel Ives argue the “AI revolution” represents the biggest technology transformation in over 40 years

"The start of this $2 trillion of AI spending all began with the launch of ChatGPT at the end of 2022 and built out by Godfather of AI Jensen (Nvidia's CEO Jensen Huang) and Nvidia as they are the only game in town with their chips the new gold and oil," said Wedbush. 

Here’s the list:

Hyperscalers: Microsoft (NASDAQ:MSFT), Google (NASDAQ:GOOG) (NASDAQ:GOOGL), Amazon (NASDAQ:AMZN) and Oracle (NYSE:ORCL).

Software: Palantir Technologies (NASDAQ:PLTR), Salesforce (NYSE:CRM), IBM (NYSE:IBM), ServiceNow (NYSE:NOW), Snowflake (NYSE:SNOW), Adobe (NASDAQ:ADBE), Pegasystems (NASDAQ:PEGA), MongoDB (NASDAQ:MDB), C3.ai (NYSE:AI), Elastic (NYSE:ESTC), Innodata (NASDAQ:INOD), AND SoundHound AI (NASDAQ:SOUN).

Consumer Internet: Apple (NASDAQ:AAPL), Meta (NASDAQ:META), Alibaba (NYSE:BABA) and Baidu (NASDAQ:BIDU).

Cybersecurity: Palo Alto Networks (NASDAQ:PANW), Zscaler (NASDAQ:ZS) and CyberArk Software (NASDAQ:CYBR).

Autonomous/Robotics: Tesla (NASDAQ:TSLA) and Oklo (NYSE:OKLO).

Semiconductor/Hardware: Nvidia (NASDAQ:NVDA), Advanced Micro Devices (NASDAQ:AMD), Taiwan Semiconductor Manufacturing (NYSE:TSM), Broadcom (NASDAQ:AVGO) and Micron Technology (NASDAQ:MU).

"It's all about the use cases exploding," said Ives.