Why ‘Real-Time AI’ Is AI’s Next Wave, and Why Meta Platforms Could Be a Massive Winner

The post Why ‘Real-Time AI’ Is AI’s Next Wave, and Why Meta Platforms Could Be a Massive Winner appeared first on 24/7 Wall St..

At the Pure Accelerate Summit in Las Vegas, Rob Lee, Chief Technology and Growth Officer at Everpure (NYSE:P), the company recently rebranded from Pure Storage, told a Bloomberg Businessweek Live audience that the enterprise AI conversation has flipped. Six to 12 months ago, customer dialogue centered on experimentation. Today, those meetings open with ROI questions. The session’s framing said the quiet part out loud: enterprise customers are done experimenting and now demanding results.

Lee’s analogy: deploying AI does not automatically create business value, just as putting up a webpage in the 1990s did not automatically generate ROI. His thesis is that the next wave of returns belongs to companies whose architecture lets agents process and act on data in real time, a shift he calls “data primacy.” The consumer payoff, according to Lee, is personalized experiences that refresh in real time rather than over “days.”

Why Meta Sits at the Center of the Real-Time AI Thesis

Lee described Meta as “one of the first of the AI customers that we worked with,” and said Everpure supports the company across IT, AI model development, and cloud infrastructure. That positioning matters because Meta Platforms (NASDAQ:META) is running the largest real-time recommendation system on the planet, and management is doubling down.

On the Q1 2026 earnings call, CFO Susan Li explained that same-day posts now represent more than 30% of recommended Reels on both Instagram and Facebook, more than double a year ago, while over 500 million users on each of Facebook and Instagram now watch AI-translated videos weekly. Q1 ranking improvements drove a 10% lift in time spent on Instagram Reels and the largest quarter-over-quarter gain in Facebook video time in four years. That is real-time AI translating directly into engagement.

Meta is funding the buildout at a scale that dwarfs peers. FY2026 capex guidance was raised to $125 billion to $145 billion, citing higher component pricing and additional data center costs to support AI infrastructure. The Q1 report itself, filed with the SEC, showed revenue of $56.31 billion, up 33.08% year over year, with operating income of $22.87 billion.

Mark Zuckerberg framed the architectural moat plainly: “AI agents get better when you fully optimize the stack. That is why we believe we need to be a company that builds frontier models in addition to building the agents.” If Lee’s data primacy thesis is right, that vertically integrated stack, paired with 3.56 billion daily active people generating fresh signal, is the asset.

Everpure’s Angle on the Same Wave

Everpure is selling the picks and shovels for that real-time architecture. Q1 FY2027 revenue came in at $1.05 billion, up 35.3% year over year, with subscription ARR of $2.00 billion, up 19% and RPO of $3.8 billion, up 41%. Management raised FY27 revenue guidance up to $4.41 billion to $4.51 billion.

On NeoClouds, Lee said demand for GPU-related storage solutions is “growing quite rapidly,” and that these providers carry traditional storage needs resembling the large enterprise customers Everpure already serves. He conceded hyperscalers could theoretically replicate Everpure’s flash technology, but argued roughly 15 years of enterprise refinement represent a meaningful moat.

The Setup for Investors

Meta shares are down 12.4% year to date and trade at a forward P/E of 18x, with analyst consensus at $827.32 against a recent $577.22. Polymarket traders assign Meta a 13.5% probability of holding the #1 AI model by year end, behind Google and OpenAI. Lee’s bull case rests on Meta operationalizing real-time AI at a scale only it can reach, and converting that into ad pricing and agent monetization — independent of where Meta lands on the model leaderboard. That is the wave to track.

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