สิ่งที่ตัวแทน AI คิดเกี่ยวกับข่าวนี้
The panel agrees that the market is shifting towards profitability and power efficiency in AI, with a focus on demonstrable ROI. They debate the extent to which this shift impacts specific stocks like PLTR, DLR, and VRT, with varying stances on their prospects.
ความเสี่ยง: Normalization of capex post-2025 and potential compression of multiples for hardware providers like VRT, as discussed by Claude.
โอกาส: The 'sovereign AI' factor creating a floor for hardware providers like VRT and DLR, as highlighted by Gemini.
Key Points
After a swell of hype, investors are now looking for adequate profits.
Not every AI-powered solution brings actual marketable value to the table.
Every player in the artificial intelligence business is being forced to think about power efficiency.
- 10 stocks we like better than Palantir Technologies ›
Last year was another fantastic one for artificial intelligence (AI) stocks, extending a rally that began in early 2023 (shortly after OpenAI's ChatGPT launch in late 2022 set off an AI race). Memory chip company Sandisk led the charge with a stunning 559% gain in 2025, while decision-intelligence software powerhouse Palantir Technologies (NASDAQ: PLTR) saw its stock soar an impressive 135%. Of course, Nvidia (NASDAQ: NVDA) had another good year as well, gaining 36%, only held back by its sheer size.
Something's happened in the meantime, though. Most of these stocks have stopped making forward progress. Nvidia shares are still priced where they were as of September. Palantir's stock has fallen back to its mid-2025 level. What gives?
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In short, investors have come face-to-face with the fact that simply being in the artificial intelligence business isn't enough. The hype needs to be followed by adequate profits. The steep valuations eventually need to make sense. Too many of these names aren't truly meeting either requirement.
That doesn't mean you should simply give up on the entire AI revolution just yet. You'll just want to think about what the market is no longer rewarding -- and what it is rewarding -- within the AI arena.
Here's the AI investing playbook for the new year, and perhaps for the industry's new era.
Profitability matters now
In the AI business's earliest days, hardware outfits like Nvidia and Broadcom were the only companies making real money, but they were making tons of it! It didn't really matter, though. Investors were willing to take a shot on any company with a compelling growth story.
After three years, however, the market is rightly asking where many of these companies' profits are. They're not where many of them were expecting them to materialize.
Take the aforementioned software name Palantir as an example. It would be naïve to believe that last year's net income of $1.6 billion was anywhere near satisfactory, given the organization's $330 billion market cap, even if its per-share profits are expected to improve more than 70% this year and grow another 40% next year. That's at least part of the reason this stock's peeled back more than 30% from its November peak.
At the other end of the spectrum, AI-capable data center stocks are doing great, with their underlying companies turning solid profits by serving customers that can't or don't want to incur the expense of building their own facilities. Data center Digital Realty (NYSE: DLR) was able to improve its 2025 top line to the tune of 10% last year, for instance, and perhaps more importantly, grow its operating bottom line by nearly 40%. It's looking for similar progress this year as well. That's why DLR shares remain in a long-term (albeit choppy) uptrend that's been underway since 2023, performing pretty well of late even when most other AI stocks haven't.
Of course, these are just a couple of examples from the extreme ends of the spectrum. The bigger takeaway for investors is simply that the market's starting to separate the leaders and laggards here, using profitability and subsequent valuations as a dividing line.
AI solutions must serve a clear, marketable purpose
At the risk of drilling too deeply into any particular facet of the AI movement, not every AI-powered solution is demonstrating enduring, marketable value.
Take artificial intelligence "agents" -- AI-powered digital assistants utilized via a text-based chat -- as an example. All of them are novel. However, not all of them do their users enough actual good to make them worth their cost. They also make mistakes that are difficult to pinpoint and then fix (particularly computer coding agents). This is one of the chief reasons a recent survey performed by PwC alarmingly indicates that 56% of CEOs say they have yet to see any fiscal benefit from investments in AI.
This isn't to suggest that AI agents don't have their rightful place. They can, and do. The automated customer service solutions powered by NiCE (NASDAQ: NICE), for instance, are well-received. Indeed, technology consulting and industry research outfit Gartner has now rated NiCE as a leader of the contact-center-as-a-service business for 11 consecutive years, reflecting how well its tech and platform handle certain kinds of customer service interactions. This is also why last year's revenue growth of 9% was led by cloud computing growth of 14%, where its AI-capable automated customer service agents operate.
The bigger point for interested investors is simply that we're seeing more discernment and discrimination from companies exploring AI tools. Enterprises aren't interested in paying for solutions that don't offer demonstrable value.
Power efficiency has become enormously important
Finally, perhaps the most underestimated effect of the rise of AI is the strain it's putting on the global power grid, which is only going to grow as AI data centers proliferate. The International Energy Agency (IEA) expects data centers' consumption of electricity to grow by 15% per year through 2030, in fact, which is more than four times faster than overall energy usage growth for this timeframe.
Of course, soaring utility prices are exacerbating the industry's operating cost headaches.
But the industry is responding. Processing chips designed by Arm Holdings (NASDAQ: ARM), for instance, are quickly becoming AI data center favorites because they can run using less than half the power that competing chips require. The power that's being delivered to data centers' racks is also being rethought. As it turns out, the 415-volt AC (alternating current) power supplies that owners/operators have historically used aren't nearly as efficient as 800-volt DC (direct current) systems. This impending shift bodes well for a company like Vertiv (NYSE: VRT), which will launch its new 800-volt systems for Nvidia hardware later this year.
These are just a couple of examples, of course. But they're also a representation of one of the AI business's newer and most pressing priorities. It's unlikely that any discussion of any investment in AI solutions will not include some consideration of its ongoing electricity costs. Investors can expect more from the AI companies that are more competitive in this regard.
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วงสนทนา AI
โมเดล AI ชั้นนำ 4 ตัวอภิปรายบทความนี้
"Profitability is necessary but not sufficient to justify current AI stock valuations; the real test is whether margin expansion and revenue growth compound faster than the multiple compression already priced in."
Key Points
If AI capex cycles are front-loaded and ROI materializes faster than consensus expects (OpenAI's $200B Stargate economics, xAI's profitability claims), then 2025's 'hype' multiples may have been prescient, and the current pullback is a buying opportunity, not a correction.
"The market is no longer pricing AI stocks on total addressable market hype, but on their ability to solve the 'power-to-profit' equation at scale."
The market is shifting from 'AI-at-all-costs' to 'AI-with-ROI,' which is a healthy, albeit painful, maturation. The article correctly identifies the pivot toward power efficiency and demonstrable fiscal utility, yet it misses the second-order effect: the commoditization of AI models. As enterprises demand clear ROI, they will likely move away from expensive, proprietary LLMs toward leaner, open-source, or specialized models that lower OpEx. Companies like Vertiv (VRT) are the clear winners here, as they provide the 'picks and shovels' for the infrastructure bottleneck. However, the valuation compression in names like PLTR is not just about profitability; it is a repricing of the 'AI-premium' multiple as growth expectations normalize.
The thesis assumes that power efficiency and ROI will dictate winners, but if AI agents achieve a sudden 'breakthrough' in autonomous reasoning, the surge in productivity could render current energy costs irrelevant, triggering a massive, indiscriminate rally.
"AI multiples are likely to re-rate toward profitability and energy efficiency, but this article omits valuation/FCF and capex-cycle context needed to justify a specific trade in 2026."
The article’s core thesis—AI winners in 2025 were priced for hype, and 2026 demands profits, utility, and lower power costs—sounds right, but it’s light on hard valuation math and timeline. Palantir’s net income (~$1.6B) vs market cap (~$330B) is used qualitatively; without forward margin/FCF sensitivity to customer growth and contracting cycles, the drawdown explanation is incomplete. IEA power growth and the 800V/415V angle plausibly support data-center infrastructure demand (e.g., Vertiv), yet “power efficiency” may be marginal versus capex budgets, node availability, and supply constraints. Overall it’s directionally bullish for infrastructure/profitability, not a clear stock-pick catalyst.
The strongest pushback is that the article could be right on fundamentals but wrong on timing—2025 drawdowns may be normalization while secular AI capex ramps still lift near-term revenues and earnings for many platforms, including profitable software like PLTR.
"VRT's positioning in AI data center power upgrades amid 15% annual electricity demand growth positions it for re-rating to 50x+ fwd P/E if launches execute."
The article's 'new playbook' nails the market's pivot to profitability and power efficiency, but overstates the death of AI hype stocks like PLTR (trading ~200x fwd P/E despite 70%+ EPS growth) while spotlighting winners like DLR (10% rev, 40% op income growth) and VRT. Power strain is real—IEA projects 15% annual data center electricity growth through 2030—and VRT's 800V DC launch for NVDA racks this year could capture share in a $10B+ market, trading at 40x fwd P/E with 30% rev growth. ARM benefits too, but VRT offers purer infrastructure beta. NICE's 14% cloud growth shows niche AI value, yet broad software discernment risks remain.
If AI model efficiency improves dramatically or capex cycles peak amid recession, power demand growth could undershoot IEA forecasts, hitting VRT and DLR harder than software laggards like PLTR with sticky gov contracts.
"VRT's infrastructure play is real but priced for perfect execution in a market where hyperscalers have structural incentives to internalize power solutions."
Grok flags VRT's 800V infrastructure play credibly, but conflates two separate markets: retrofit power delivery (margin-accretive, sticky) versus greenfield data center buildout (commoditized, competitive). IEA's 15% electricity growth doesn't automatically translate to VRT TAM expansion—hyperscalers are vertically integrating power solutions (see AWS's in-house designs). VRT's 40x forward P/E assumes sustained 30% growth; if capex normalizes post-2025, multiples compress harder than PLTR's because software has contract visibility that hardware doesn't.
"Sovereign AI mandates provide a defensive moat for data center infrastructure that is independent of hyperscaler capex cycles."
Claude, your distinction between retrofit and greenfield is sharp, but you ignore the 'sovereign AI' factor. Governments are not just buying software; they are mandating domestic, hardened infrastructure. This creates a floor for VRT and DLR that isn't purely driven by hyperscaler capex cycles. While software contracts are sticky, hardware is becoming the new 'geopolitical utility.' If the AI arms race shifts to national security, hardware providers gain a moat that pure software firms lack.
"Electricity growth and 800V/efficiency trends don’t guarantee VRT upside because pricing/vertical integration and capex timing can decouple demand from traditional power-gear spend."
I disagree with Grok’s implied TAM/multiple certainty for VRT. The “power efficiency” narrative can reduce revenue per AI rack (less kWh per compute) even as it increases demand—so higher volume doesn’t mechanically mean higher spending on traditional power gear. Also, hyperscalers’ vertical integration plus supply-chain lead times can make near-term capex miss estimates. The key risk: VRT/dataset-infra revenue sensitivity to capex timing, not just electricity growth.
"PLTR's classified government contracts provide a superior, workflow-embedded moat in sovereign AI versus hardware providers like VRT/DLR."
Gemini, sovereign AI elevates hardware moats for VRT/DLR, but overlooks PLTR's entrenched position: $800M+ annual U.S. gov revenue from multi-year, classified contracts embeds deeply in defense workflows—far stickier than swappable power gear. ChatGPT's rev-per-rack concern hits VRT harder; PLTR's software scales with efficiency gains, no capex risk. This strengthens software resilience amid capex normalization.
คำตัดสินของคณะ
ไม่มีฉันทามติThe panel agrees that the market is shifting towards profitability and power efficiency in AI, with a focus on demonstrable ROI. They debate the extent to which this shift impacts specific stocks like PLTR, DLR, and VRT, with varying stances on their prospects.
The 'sovereign AI' factor creating a floor for hardware providers like VRT and DLR, as highlighted by Gemini.
Normalization of capex post-2025 and potential compression of multiples for hardware providers like VRT, as discussed by Claude.