After 40 days of heavy bombing and two more weeks of daily attacks, the U.S. is now relying on a naval blockade to force Iran to fully reopen the Strait of Hormuz and return global oil markets to normalcy.

But the regime has plans to go into attack mode soon if there’s no diplomatic progress, forcing the U.S. back into major combat just as vulnerabilities have emerged in its own military.

Iran has shifted from a defensive stance to a “fully offensive” ​one, a senior Iranian official told Reuters on Monday, citing the stalemate with the U.S. on talks.

Unless the U.S. implements the June ceasefire deal in a few weeks, Iran will launch a “timely and precise” ​attack to break the blockade, the official warned.

“Iranian entities must be prepared to escalate tensions ​in the Strait of Hormuz and wider region, as Iran will be ready to make ⁠decisions and take action on difficult decisions,” the official added.

The threat comes after Iran recently reorganized its military to be more aggressive as factions in the government abandon hopes for negotiations.

Sources told The Wall Street Journal Arab intelligence detected preparations for a wider war, including the deployment of Iranian commanders, weapons, and intelligence to regional militias aligned with the regime.

Iran’s Islamic Revolutionary Guard Corps has also drawn up plans for more escalation, such as sabotaging internet cables in the Persian Gulf, fomenting unrest in neighboring states with large Shia populations, and even a potential ground assault in Kuwait, the report added.

“There is also a widespread view in Iran that the main war has not yet begun,” Mohammad Hassan Sangtarash, a Tehran-based defense analyst close to the Iranian government, told the Journal. “What we have seen so far is increasingly interpreted through the lens of ‘salami-slicing’ tactics—limited, incremental escalation designed to weaken capabilities before a larger confrontation.”

Despite seeing its conventional forces decimated by the U.S.-Israeli bombardment earlier in the war, Iran has seen its tactical situation improve recently.

Iran has developed new missiles that are better at evading air defenses, making U.S. military assets and allied oil infrastructure around the region more vulnerable.

The U.S. military has also expended much of its interceptor stockpile, which is now so low it reportedly factored into Trump’s decision to call off a major re-escalation of war.

In addition, even maintaining the naval blockade has strained U.S. forces as the U.S.S. Abraham Lincoln aircraft carrier struggles with mental health and supply issues amid a record-long time at sea. Another carrier is on the way to take its place, but other ships performing blockade operations are likely facing similar logistical concerns.

The conditions could be ripe for Iran to test U.S. resolve. And given the harm the U.S. blockade was causing, Iran wasn’t expected to stand idly by, especially now that it has more military leverage to exploit.

Majidreza Hariri, the head of the Iran-China Joint Chamber of Commerce, recently admitted the U.S. blockade will eventually inflict more economic damage than actual war.

To avoid this, he urged the regime to do whatever it takes to end the blockade, “whether through negotiation, supplication, threats, or even war.”

“We must also eliminate the perception in the U.S. that it can resort to such an action whenever it wants, and make it understand that the consequences of such a move could be severe,” Hariri added.

It has been a historic period for memory and storage stocks. Once relegated to a bucket of lowly valued “commodity” stocks, the agentic AI revolution has spurred an absolutely massive increase in memory and storage demand.

Not only has demand increased, but the technology has also evolved from an interchangeable commodity to a strategic enabler for AI systems. The agentic era has therefore spurred massive stock price increases for memory and storage giants Micron (MU +2.30%), SK Hynix (SKHY +0.40%), and Sandisk (SNDK +7.40%).

However, those huge gains gave way to a big pullback in July because of profit-taking, fears of more efficient models from China, short-seller skepticism, and the “blow-up” of the AI-focused hedge fund Situational Awareness. Even after a bounce back in August, these stocks remain 15% to 30% below their June highs.

Is the recent pullback a harbinger of more pain and a “bubble bursting,” or an opportunity to buy the dip? Last week, Elon Musk wrote a three-word sentence on his social media that strongly points to the latter.

“Few realize this”

Last week on X, a private technology executive noted, “Memory, not compute, is the rate limiter of the Agentic Era,” to which Elon Musk replied, “Few realize this.”

Memory, not compute, is the rate limiter of the Agentic Era.

— Peter H. Diamandis, MD (@PeterDiamandis) August 14, 2026

Few realize this

— Elon Musk (@elonmusk) August 14, 2026

In the first wave of generative artificial intelligence, the environment was dominated by simple questions or prompts directed at the AI system, which would then find the answer. That relatively simple AI application places a lot of onus on the GPU and its massively parallel-processing capabilities.

However, in the agentic AI era, in which AI is tasked with planning and executing tasks independently, the game has changed. Now the focus has pivoted to planning, thinking, data retrieval, testing, and retesting agentic outputs. That has exponentially expanded CPU-heavy “planning” tasks.

Not only that, but each “task” also requires vast amounts of storage and memory. In a recent blog post from Micron, the company wrote that every single agent instance requires:

• State and KV/context staging — keeping track of where it is in its reasoning loop.
• Tool outputs and queues — buffering results from API calls and code execution.
• Container/sandbox memory — isolated runtime environments for safe execution.
• Vector/index data — for retrieval-augmented generation and semantic search.
• OS and runtime overhead — the base cost of keeping thousands of environments alive.

Each one of these requirements entails memory to support it. Micron also notes that much of the memory for these workloads isn’t traditional “commodity” DRAM, but specialized, high-capacity, high-bandwidth DRAM.

These smarter memory architectures require more capital equipment to produce; for instance, memory makers have noted that high-bandwidth memory requires at least three times as much capital equipment per bit to produce as traditional server DRAM.

That means the supply of advanced memory required for agentic AI is becoming harder to meet, just as demand is exploding, which is why DRAM prices have boomed.

Not to be outdone, NAND flash, which stores information even when a system is turned off, though it is slower than DRAM, is also growing fast. That’s because massive KV-cache memory chains for long-context agents — basically, the prior context AI agents must “remember” to produce more tokens — require a lot of data to be offloaded to NAND-based SSDs. Both Micron and SK Hynix produce NAND along with DRAM, while Sandisk is a NAND “pure play.”

Image source: The White House.

Inference has catapulted memory to the forefront

Training even the best frontier models requires a lot of memory, but that amount is ultimately capped at the amount needed to fill a GPU. In other words, GPUs can only read so much memory at once, so training a model requires a lot of GPUs, along with a requisite amount of memory.

However, as we enter an era in which more and more consumers and enterprises use agentic AI as a daily habit, the demand for memory appears almost endless. If an AI agent operates over a long period of time, it will have to constantly read and write the KV cache ad infinitum.

That’s why researchers at Goldman Sachs just released awe-inspiring estimates of future AI token usage. By 2030, the investment bank estimates that agentic AI will consume roughly 120 quadrillion tokens per month: 24 times the token usage of early 2026.

Thus, it’s no wonder that Elon Musk highlights memory as the biggest silicon-based constraint for AI moving forward. Even as more supply comes online in 2028, it appears the demand will be there to absorb it. Thus, the current memory up-cycle may last longer than many investors realize.