Internet EXPLODES w/ New Jensen Interview

The interview highlights a significant divergence between Wall Street's conservative growth estimates for a major tech company and the more optimistic outlook from industry leaders. While analysts project flatlining growth post-2026, the company's leadership expresses confidence in exceeding these projections, attributing this gap to a fundamental shift in computing.

This shift is characterized by the end of general-purpose computing and the rise of accelerated and AI computing, transforming hyperscale infrastructure from CPUs to GPUs. This transition is already impacting existing workloads and is poised to unlock entirely new applications, augmenting human intelligence and driving substantial economic growth estimated in the trillions.

The exponential demand for AI computing necessitates massive infrastructure investment, evidenced by the projected tenfold increase in data center power and the emergence of gigawatt-scale data centers. This surge in demand for compute power signifies a return to a more tangible economic impact, requiring significant expansion in physical infrastructure and skilled labor, effectively merging the "bits" economy with the "atoms" economy.

• General-purpose computing is over, replaced by accelerated and AI computing.
• AI is already present in search, recommender engines, and shopping, shifting from CPUs to GPUs.
• The future involves AI supercomputers and factories augmenting human intelligence, potentially adding $10 trillion to the $50 trillion global GDP.
• AI computing requires significant infrastructure, with data centers growing exponentially in size and power demand.
• The demand for AI is driving a resurgence in the physical economy, requiring more construction, tradespeople, and raw materials.

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The Disconnect Between Wall Street and Industry Leaders [0:06]

• There's a "massive divergence of belief" between industry leaders and Wall Street regarding future growth.
• Sell-side analysts' consensus estimates show flatlining growth post-2026, with only 8% growth projected from 2027 through 2030.
• Company leadership is "comfortable with that" and has "no trouble beating the numbers on a regular basis."
• The disconnect is attributed to analysts not believing in the sustained demand, fearing shortages could lead to a glut.

The core issue lies in a significant gap between industry leaders' optimistic outlook and Wall Street analysts' conservative forecasts, with the company confident in surpassing these projections.

"So clearly >> we're comfortable with that. Look, we're comfortable with that. We have no >> trouble beating the numbers on a regular basis."

The Shift to Accelerated Computing [2:00]

• The first point to consider is the "laws of physics" argument: general-purpose computing is over, and the future belongs to accelerated and AI computing.
• Trillions of dollars of computing infrastructure worldwide need to be refreshed, and this refresh will be in accelerated computing.
• General-purpose computing is shifting to accelerated computing and AI, a reality recognized by partnerships, including with Intel.
• The first use case of AI is already widespread in search, recommender engines, and shopping.
• Hyperscale computing infrastructure is transitioning from CPUs to GPUs for AI.

This fundamental shift means that traditional computing is being superseded by accelerated and AI-driven solutions, impacting all levels of computing infrastructure.

"So the first thing you have to realize is that general purpose computing and nobody disputes that. Everybody goes, "Yeah, we completely agree with that. General purpose computing is over. Moore's law is dead."

The Impact and Opportunities of AI [3:53]

• AI is not just about creating new opportunities but about shifting how things are done.
• The future involves AI supercomputers and factories that generate tokens to augment human intelligence.
• Human intelligence, representing a significant portion of global GDP ($50 trillion), will be augmented by AI.
• Augmenting a $100,000 employee with a $10,000 AI can make that employee two to three times more productive, a change being implemented across the company.
• This increased productivity leads to faster growth, more hiring, and greater top-line revenue.

AI's role extends beyond novelty to become a tool for augmenting human capabilities and driving substantial economic expansion.

"And that $50 trillion is going to get augmented by something. Suppose I were to hire a $100,000 employee and I augmented that $100,000 employee with a $10,000 AI."

The Economics of AI Infrastructure [6:02]

• AI is different from past software as it's constantly generating tokens and thinking, requiring machines to run all the time.
• This continuous operation necessitates an AI factory and infrastructure.
• If $10 trillion of tokens are generated annually with 50% gross margins, $5 trillion needs an AI infrastructure.
• With global annual capital expenditure around $5 trillion, the math for AI infrastructure seems to make sense.
• The market for AI is currently estimated at $400 billion annually, with a projected 4-5x increase.

The computational demands of AI, particularly its continuous token generation, require dedicated infrastructure, representing a significant and growing market.

"in order for the AI to think it needs a factory and so let's say that that 10 trillion ion dollar of token generated >> 50% gross margins and five trillion of >> it needs a factory needs an AI infrastructure"

The Analysts' Short-Sightedness [8:02]

• The discrepancy between industry leaders' demand forecasts and analysts' predictions is seen as "short-sighted."
• Analysts are incentivized differently, not investing their own money and facing penalties for wrong bullish calls.
• Their focus is often quarterly, with a maximum outlook of 12 months, leading them to only consider 2026 revenues.
• 2027 projections on their spreadsheets are often just placeholders.
• This conservatism is amplified by past experiences like the dot-com bubble and bust.

Analysts' incentives and short-term focus lead to conservative financial projections that often miss the long-term transformative potential of new technologies.

"Well, it almost makes you laugh at how short-sighted analysts on Wall Street really can be."

The End of General Computing and Moore's Law Metaphorically [10:40]

• The statement that "general computing is over" means the center of gravity for computation has shifted from CPUs to parallel accelerated computing, primarily for AI.
• While CPUs will still exist and grow, the primary growth engine has moved.
• Metaphorically, Moore's Law is dead because the paradigm of free performance growth solely from transistor density has ended.
• This shift is viewed as a positive for the economy, moving beyond "bits" and screens to impact the "atoms" of the physical world.

The era of general-purpose computing driven by Moore's Law is concluding, replaced by accelerated computing for AI, which has broader economic implications beyond the digital realm.

"When Jensen says that the era of general computing is over, what he's talking about is that the center of gravity in growth in computation has moved away from the CPU and towards parallel accelerated computing mostly for artificial intelligence."

The Exponential Demand for Compute and Power [15:00]

• Under the accelerated computing paradigm, compute growth demand far exceeds what Moore's Law can supply.
• This has led to deep vertical integration to become computing systems engineers, reinventing the data center stack.
• Performance per watt is growing faster than Moore's Law, but this drives an even faster growth in computing demand.
• This is the fundamental reason for the increasing demand for watts of compute each year.
• Data centers are becoming exponentially larger, with plans for gigawatt-scale facilities.

The insatiable demand for AI computing is outpacing technological advancements in performance efficiency, leading to a massive and escalating need for power and infrastructure.

"This allows them to grow performance per watt much faster than Moore's Law. But that in turn drives computing demand growth that's even faster than what they're able to deliver with their performance per watt growth."

The Real Economy Impact of AI Infrastructure [16:34]

• The exponential demand for electricity to power AI data centers brings the focus back to the real economy and physical resources.
• Building the necessary AI infrastructure requires moving atoms on a massive scale, impacting various industries.
• There's a growing demand for tradespeople like electricians, plumbers, and welders, as well as construction workers, buildings, and roads.
• Once data centers are built, new factories and raw materials will be needed to produce robots that can embody AI intelligence.

The massive build-out of AI infrastructure necessitates a significant expansion in the physical economy, creating demand for labor, materials, and manufacturing.

"There's just no way for us to build out all of the electricity generation that's going to be required to power the next 10 years worth of AI infrastructure without moving atoms on a massive scale."