AI Is A Macro Variable Now - The Investment Logic Has Changed

(MENAFN- Investor Ideas) Investorideas ( newswir ) a trusted platform for investing ideas including AI stocks issues market commentary from Tal Elyashiv, Founder ofSPiCE VC

Two data points came out of Morgan Stanley's AI market analysis recently that I keep coming back to.

First, $3 trillion in AI-related infrastructure investment will flow through the global economy by 2028, with more than 80% of that still ahead.

Second, 21% of S&P 500 companies now cite AI benefits in their results. The companies delivering measurable returns are seeing cash flow margin expansion at roughly 2x the global average. The companies in the second group, the ones reporting AI benefits without margin expansion, are absorbing the cost without capturing the upside.

Those two facts together tell a more interesting story than either one does alone.

When a technology becomes a macro variable

For the past three years, AI has been covered as a tech sector story, with the focus on which AI companies are winning, which models are strongest and which stocks to own.

That framing is now outdated. Morgan Stanley's conclusion, which BlackRock echoed in its Q2 outlook, is that AI has crossed into macro territory. The capital expenditure decisions of a handful of companies are, in Morgan Stanley's words, "so large that the micro is macro." That language describes spending plans large enough to move GDP. Tech themes do not move GDP. Structural economic forces do.

When I wroteInvesting in Revolutions, I described what happens when a transformational technology reaches the phase where it stops being a sector story and becomes a force field, where the question shifts from "which companies are building this" to "which businesses are positioned to thrive in a world where this is how things work." We are at that transition point now.

The investment logic changes when a technology becomes structural. The work is no longer about picking the winners of a race. It is about understanding which businesses have the architecture to capture value in a world where AI is ambient, where AI lives inside every workflow, every pricing model and every competitive dynamic. Some companies are built for that world. Many are not. The gap between the two is beginning to show up in the numbers.

The 2x split

The Morgan Stanley data on cash flow margin expansion is the most important data point in any AI investment discussion right now, and it gets far less attention than the total spending figures.

21% of S&P 500 companies cite AI benefits. That sounds like progress, and it is. Inside that group, the divide is sharp. The companies seeing measurable margin expansion have demonstrated pricing power. The companies without pricing power are adopting AI and absorbing the infrastructure cost without capturing the economics.

The distinction between companies that adopt AI and companies that profit from AI is one I have written about under a different name. In Investing in Revolutions, I call them Beneficiaries and Casualties. The Beneficiary integrates a new technology at scale in a way that extends its competitive position. The Casualty adopts the tool, pays for the infrastructure and watches the value flow to someone else, a competitor with a stronger market position, or the technology vendor charging for the capability.

The Morgan Stanley data shows that the Casualty risk in AI is real and visible right now. Adopting AI without the underlying pricing power or structural advantage to monetize it can land as a margin headwind. The infrastructure cost shows up on the income statement. Customers will not pay more for it. Competitors are doing the same thing, so the expected competitive advantage does not arrive.

The 2x split is the market sorting Beneficiaries from Casualties in real time.

What determines which side a company lands on

Three characteristics define the companies capturing value from AI.

The first is pricing power in the underlying business. If customers will pay more for a better, faster or more personalized product or service, and if AI helps deliver that, the economics work. If the product is commoditized and customers are already price-sensitive, AI reduces costs, but competitive pressure passes the savings to the customer rather than the income statement.

The second is data specificity. The companies winning with AI have proprietary data, customer behavior, operational history and domain-specific knowledge that a general AI model does not have. That data makes their AI more accurate, more useful and harder to replicate. It is a moat. Companies deploying generic AI on top of public data do not have that moat.

The third, and the one most often underestimated, is organizational capability. An AI system is only as good as the context it operates on. Context means the right data flowing into the right workflows with the right human oversight. The companies that have invested in their data architecture, their integration layers and their change management can make AI work at scale. The companies that have not are learning that bolting AI onto broken infrastructure does not fix the infrastructure.

The $3 trillion question

$3 trillion in AI infrastructure investment by 2028, with more than 80% still ahead. The scale describes an industrial buildout on the order of the railroad era or the electrification of American industry.

I have spent years studying how transformational technology waves create and destroy value. The consistent pattern: the largest wealth creation does not happen at the beginning, when attention is focused on the technology pioneers. It happens in the middle, when the infrastructure is built and the question shifts to which businesses use it most effectively. We are in that middle phase now.

The $3 trillion will flow. The companies receiving it will build. Some of what gets built will be genuinely transformative. The value created by that transformation will accumulate heavily in the businesses with the structural characteristics to capture it: pricing power, proprietary data and organizational capability.

The 2x margin differential already visible in the data will widen as AI becomes more embedded and structural advantages compound.

The relevant question for any investor right now is whether a given business sits in the layer of AI that captures value, or the layer that pays for infrastructure another business profits from.

What this means for how I am investing

I am writing this partly because of a decision I made recently that reflects this thesis directly.

I have joined True Global Ventures as a General Partner, alongside my continuing role as Managing Partner at SPiCE VC. TGV Fund 6 focuses on AI-first vertical companies: businesses built from the ground up around AI for specific domains, where the data is proprietary, the workflow integration is deep and the pricing power is real.

That decision is not a coincidence. It is where the Investing in Revolutions framework points when I ask the question: at this stage of the AI cycle, where does the value accumulate?

The Originator phase, investing in the foundational AI models and infrastructure, is largely played. The Beneficiary phase, identifying the businesses that use AI to build durable competitive advantage, is where the most interesting opportunity sits right now.

The 2x split in the Morgan Stanley data is the evidence.

Tal Elyashiv is the founder and Managing Partner of SPiCE VC, General Partner of True Global Ventures, co-founder of Securitize and author of Investing in Revolutions and Blockchain Prophecies.

Research tech stocks at Investorideas's free stock directory

/TSS/stock_list

About Investorideas-Where you find the best investing ideas

/About/

Investorideas is a trusted platform for retail investors, serving as a hub for innovative investing ideas for over 25 years. Known for its pioneering coverage of sectors like mining, cleantech, defense, and water stocks, the company and its executives have been featured in high-profile media outlets for its expertise. Investorideas is always at the forefront of investment trends, offering coverage of diverse industries such as AI, mining, and tech. With a mission to empower investors, the company provides breaking news, sector-focused articles, podcasts, and exclusive interviews with leading experts. In addition, its award-winning branded content, AI-driven short videos, and podcasts highlight key investing insights and feature prominent industry leaders.

Why partner with Investor Ideas? Discover our powerful news, PR, social media, and podcast solutions for tech stocks at Investorideas

/Investors/Services

Why should companies choose Investor Ideas for digital advertising and guest posts?

/Advertise/

Follow us on X: @investorideas, @stocknewsbites

Follow us on Facebook:

Follow Investorideas and the Exploring Mining Podcast on YouTube:

Investors stay ahead by signing up for free stock news alerts at Investorideas

/Resources/Newsletter

Join our free Telegram community.

Contact us for more info

Call 800 665 0411.

Disclaimer/Disclosure Our site does not make recommendations for purchases or sale of stocks, services or products. Nothing on our sites should be construed as an offer or solicitation to buy or sell products or securities. All investing involves risk and possible losses. This site is currently compensated for news publication and distribution, social media and marketing, content creation and more. Disclosure is posted for each compensated news release, content published /created if required but otherwise the news was not compensated for and was published for the sole interest of our readers and followers. Contact management and IR of each company directly regarding specific questions. More disclaimer info: Moredisclaimer anddisclosure info/About/Disclaimer Global investors must adhere to regulations of each country. Please read Investorideas privacy policy: /About/Private_Policy

MENAFN30102026000142011025ID1111892342