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Three of tech’s most influential figures, Jeff Bezos, Jensen Huang, and Masayoshi Son, are publicly drawing the boundaries of the AI debate as roughly $380 billion has flowed into AI-related companies this year.
Their commentary lands as Amazon reportedly warns staff over runaway token spending, sharpening the question of whether the AI boom reflects durable productivity gains or an inflating capital bubble.
Capital Flows Tilt Sharply Toward AI
AI-related companies have issued about $140 billion in investment-grade bonds this year, which is roughly 49% of total IG issuance.
The same firms attracted around $220 billion in venture funding, or 87% of the total. High-yield credit added another $21 billion.
Combined, AI-linked capital totaled about $380 billion across the three channels, or about 64% of all capital flows tracked.
That intensity tracks with rising Big Tech AI capex, which BlackRock says now sets the macro market backdrop.
SoftBank joined the buildout this week with a €75 billion ($87 billion) commitment to develop 5 gigawatts of AI data center capacity in France, announced alongside French President Emmanuel Macron in Paris.
Nvidia’s Jensen Huang and SoftBank CEO Defend the Buildout
Nvidia (NVDA) chief executive Jensen Huang dismissed claims that AI is hollowing out the labor market.
“People are talking about AI decreasing jobs, it’s complete nonsense,” Jensen said in his NVIDIA GTC Taipei 2026 keynote.
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Elsewhere, Masayoshi Son told CNBC the current cycle eclipses the late-1990s internet wave by a wide margin.
“I think this is like more than 10x, probably 50x bigger than dotcom,” CNBC reported, citing Son.
Bezos has framed the moment as an industrial bubble rather than a financial one, arguing in recent remarks that even speculative excess leaves behind productive infrastructure once weaker projects fail.
Costs Force a Sharper Reckoning
Amazon executive David Treadwell asked staff to stop using AI for trivial tasks after the company reportedly burned through roughly half a billion dollars of tokens in a single month.
Uber, Salesforce, Meta, and Microsoft have circulated similar internal cautions, while hyperscaler free cash flow is near a decade low.
Meanwhile, Matthew Sigel challenges the narrative that AI infrastructure is seven times more expensive than legacy systems.
The VanEck strategist argues that flagship models can summarize a 500-page book for roughly $2.50, against $375 to $400 per million tokens for human-packaged content.
Forecaster Will Sommer estimates that hyperscalers need about $7 trillion in revenue over the next three years to clear a 7% return on invested capital, a backdrop that has fed AI bubble revenue concerns and visible AI financing strains.
The coming earnings cycles will test whether productivity gains close the gap before investors lose patience.
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