Recently The New York Times reported the following:
The Impending, Inescapable Deluge of A.I. (1/3)
“It’s hard to get your mind around the scale”
The NYT - By Adam Satariano, Paul Mozur, Jacqueline Gu and
Cade Metz - The reporters, based in London, Taipei, New York and San Francisco,
have covered the rise of artificial intelligence for years.
July 29, 2026
The milestones for artificial intelligence keep getting
grander.
In 2023, an A.I. system passed the bar exam. In 2025, the
technology helped scientists identify a suspected cause of Alzheimer’s disease.
In May, A.I. had advanced so far that it solved a complex math problem that had
stumped experts for 80 years. Last week, two A.I. systems under testing went
rogue and hacked into a company’s database.
And this is still just the beginning.
From the American Midwest to the Persian Gulf, hundreds of
major data centers now under construction will be turned on in the coming
years. They are set to deliver an avalanche of computing power to develop and
run A.I. that has no equal in the history of the technology industry, with
breakthroughs that once felt revolutionary likely to become increasingly
routine.
Behind each leap in A.I. are corresponding jumps in computing power. Today, there are about 20 million A.I. chips crammed into the data centers that underpin the technology’s growing abilities and usage worldwide, according to the research firm Epoch AI. That figure is expected to double roughly every nine months, putting the world on pace to have about 200 million of the chips by the end of 2028 — 10 times current levels.
In size and ambition, this moment compares to the building of the railroads in the 1800s, President Franklin D. Roosevelt’s New Deal in the 1930s, and the Manhattan Project to create an atomic weapon in the 1940s, technologists said.
“This is the largest scale infrastructure build-out in the history of humanity,” said Rob Wachen, a co-founder of the microchip firm Etched, which has raised more than $1 billion to meet the growing demand for A.I. components.
Peter DeSantis, who leads foundational A.I. models at Amazon — which provides computing power to the A.I. firms Anthropic, OpenAI and others — said the Seattle company has doubled its computing capacity since 2022 and would double it again by next year. “It’s hard to get your mind around the scale,” he said.
Fueling the surge is the belief that A.I. can take on more human responsibilities and solve increasingly complicated tasks with the more data and computing power you feed it. This tenet, sometimes called “the Scaling Laws,” has become the driving force behind this technological era. Those with the most computing power will create the most advanced A.I. systems, capturing the biggest share of profit and value, tech leaders argue. The biggest engine, they say, will win the race.
Confidence in the Scaling Laws has led A.I. leaders to make ever bolder predictions. Dario Amodei, the chief executive of Anthropic, has said that if these laws hold for another year or two, A.I. will be able to perform huge amounts of white-collar work. Demis Hassabis, the head of Google’s A.I. lab DeepMind, wrote recently that A.I. could usher in “10x of the Industrial Revolution at 10x the speed.”
Scientists and technologists see the coming deluge of computing power leading to drug discoveries and robotics advances, and industry analysts said it would drive more everyday use of A.I. in people’s personal and professional lives.
But the build-out has also stoked a backlash, spurring protests in many communities over how data centers could harm the environment, raise electricity prices and drain water. In the United States, data centers are shaping up as a major issue for November’s midterm elections, with a growing national movement pushing back against the tech industry and its billionaires.
Economists and investors have raised concerns that tech firms are spending faster than they can profit from A.I. Past infrastructure booms have been followed by downturns before the benefits of the technology were realized. The railroad boom in the 1800s, electrification in the 1920s and the dot-com bubble in the late 1990s were punctuated by economic recessions and a stock market crash as companies that overspent went out of business.
“Each time you’ve had a technological revolution, this kind of bubble bursting happened,” said Philippe Aghion, who won the Nobel in economic science in 2025 for research on innovation-driven economic growth. “A.I. is like the fourth industrial revolution and it has this aspect to it that generates a bubble.”
(to be continued in part 2)
Translation
即將來臨、無法逃避的人工智能洪流（1/3）
“其規模之大令人難以置信”
人工智能的里程碑越來越宏大。
2023年，一個人工智能系統通過了律師資格考試。
2025年，這項技術幫助科學家確定了阿茲海默症的一個疑似病因。今年5月，人工智能取得了長足的進步，解決了困擾專家80年的複雜數學難題。上週，兩個正在測試中的人工智能系統失控，入侵了一家公司的資料庫。
而這只是個開始。
從美國中西部到波斯灣，數百個正在建設中的大型數據庫將在未來幾年內投入使用。它們將提供大量的運算能力，用於開發和運行人工智能，其發展速度在科技史上前所未有，曾經被視為革命性的突破很可能變得日益普遍。
人工智能的每一次飛躍都伴隨著運算能力的相應飛躍。根據研究公司Epoch AI的數據顯示，目前全球約有2,000萬個人工智能晶片被部署在數據庫資中，這些數據庫支撐著人工智能技術日益增長的能力和應用。預計這一數字將以大約每九個月翻一番的速度增長，到2028年底，全球將擁有約2億個這樣的晶片 - 是目前的10倍。
技術專家表示，就規模和雄心而言，這一時刻堪比19世紀的鐵路建設、1930年代富蘭克林羅斯福總統的新政以及1940年代旨在製造原子彈的曼哈頓計劃。
微晶片公司 Etched 的共同創辦人 Rob Wachen 說:「這是人類史上規模最大的基礎設施建設」。該公司已籌集超過10億美元，以滿足日益增長的人工智能組件需求。
亞馬遜是為 Anthropic、OpenAI 等提供運算能力的公司。亞馬遜負責基礎人工智能模式的 Peter DeSantis 表示，其位於西雅圖的公司自2022年以來已將其運算能力翻了一番，並將於明年再次翻倍。他說: 「其規模之大令人難以置信」。
推動這項成長的動力源於人們對人工智能的信念。輸入的資料和運算能力越多，人工智能就能承擔更多人類職責，解決日益複雜的任務。這項原則，有時被稱為“規模定律”，已成為推動這個科技時代的動力。科技領袖們認為，擁有最強運算能力的人將創造出最先進的人工智能系統，從而攫取最大份額的利潤和價值。他們說，最強大的引擎將贏得這場競賽。
對規模定律 的信心促使人工智能領域的領導者們做出越來越大膽的預測。 Anthropic 執行長 Dario Amodei 表示，如果這些定律在未來一兩年內仍然有效，人工智能將能夠完成大量的白領工作。谷歌人工智能實驗室 DeepMind 的負責人 Demis Hassabis 最近撰文指出，人工智能可能會「以十倍的速度帶來十倍的工業革命」。
科學家和技術專家認為，即將到來的運算能力洪流將推動藥物研發和機器人技術的進步。產業分析師也表示，這將促使人工智能在人們的個人和職業生活中得到更廣泛的應用。
然而，數據庫的建設也引發了強烈反對，許多社區爆發了抗議活動，人們擔憂數據庫會破壞環境、推高電價並消耗大量水資源。在美國，數據庫正逐漸成為11月中期選舉的一個重要議題，一場日益壯大的全國性運動正在抵制科技業及其億萬富翁。
經濟學家和投資者擔憂，科技公司在人工智能領域的支出速度超過了其獲利能力。以往的基礎設施建設熱潮之後都出現了經濟衰退，直到最終實現到這項技術的益處。 19世紀的鐵路繁榮、20世紀20年代的電氣化以及20世紀90年代末的網絡泡沫都伴隨著經濟衰退和股市崩盤，因為過度支出的公司紛紛倒閉。
因對創新驅動型經濟成長的研究而榮獲2025年諾貝爾經濟學獎的Philippe Aghion說: 「每次科技革命之後，都會出現這種泡沫破裂」;「人工智能就像第四次工業革命，它具有產生泡沫的特質」。
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