哲學家黑格爾曾說：「人類從歷史中學到的唯一教訓，就是人類沒有從歷史中吸取任何教訓。」在 AI 浪潮隨著股市行情一波波上漲之際，矽谷的科技巨頭們卻大舉裁員與凍結人事，讓美國資訊科系畢業生面臨前所未有的就業危機。對比 2021 年時，矽谷工程師擁有令人稱羨的年薪與大筆分紅，如今盛況已不可同日而語。產業趨勢發生如此大幅度的反轉，當然有數不清的成因，但這本《科技泡沫》裡面提到的問題，或許正是壓垮駱駝的最後一根稻草。
Google、亞馬遜、臉書和 Netflix，在網際網路產業中，有太多耀眼的科技公司由原本的默默無聞，蛻變成富可敵國的跨國企業，同時也讓許多早期投資人賺得盆滿鉢滿。因此，創業投資（簡稱創投）在美國投資產業中一直是撐起半邊天的熱門項目，而科技創業也成為許多青年才俊前赴後繼的職涯選項。然而，本書作者伊曼紐．馬喬里卻明確指出，現今創投產業與新創公司之間存在著日益嚴重的弊病，造就了無數的資源錯置與財務損失。
2000 年爆發網路泡沫時，那斯達克股市大跌 75%，矽谷裁員 20 萬人。此後 20 多年間，又陸續發生許多經濟危機，以美國為首的各國政府，像老狗玩不出新把戲般，只能靠大量印鈔救市。不論是量化寬鬆還是日本行之有年的零利率政策，都是助長泡沫的貨幣政策。1998 年亞洲金融風暴助長了網路泡沫，網路泡沫又助長了 2008 年金融海嘯；而 2019 年因新冠疫情產生的量化寬鬆，正是促成矽谷新創公司如雨後春筍般湧現的背後活水，但過多的資金也導致亂象叢生。
新創公司追求爆炸式成長，最後多半演變成一種吹牛比賽，在經營公司時忽視顯而易見的風險，更仰賴三種類型的奇蹟來促成自身的成功：
市場奇蹟：消費者行為突然改變（例如：突然願意為原本免費的服務付費）。技術奇蹟：宣稱將推出目前做不到、未來也尚不知如何達成的技術或服務（例如《惡血》一書所描述的情節）。人才奇蹟：深信只要聚集足夠的人才與頂尖人物，就能做出前所未有的商業成果（目前 AI 產業的投資氛圍恰好類似於此）。
原本創投資金會採分批投入的種子輪方式，從小額資金開始嘗試，當新創公司逐步證明自身價值後，才漸進式投入更多資金以降低風險。然而，在資金氾濫的大環境下，亂槍打鳥的投資卻變成了有心人士蓄意欺騙的溫床。
跑馬圈地是中國大陸網路產業最常採用的競爭手法，利用龐大的資金補貼搶占目標市場的高市占率，隨之而來的是連年巨額虧損。但創投公司非常吃這一套，畢竟資金來源源源不絕，只差一個能把故事講好的對象。其背後的投資邏輯是：只要有足夠的耐心與資本，等到新創公司達到足夠的規模，總有一天會帶來鉅額獲利來彌補先前的損失。
然而事實上，這些燒錢的新創公司根本沒有打造出超額補貼以外的護城河。一旦有其他資本雄厚的公司意圖投入同一市場，就會影響原本預估的獲利，爭相削價競爭便演變成深不見底的紅海市場。在網路產業中，真正算得上護城河的經營優勢，除了網絡效應（適用於社群網站、電商平台）之外，就是轉換成本（適用於手機與電子裝置）；至於規模經濟、商業模式與先行者優勢，都不是可以長久依賴的靠山。這種護城河的幻覺包含了「十倍」的迷思、「規模」的迷思與「品牌」的迷思。
其實作者著手撰寫本書，源於他在新創公司任職時發現的奇怪現象：科技業在資金過剩的情況下出現招募狂潮，以優渥的福利與薪水，搶占市場上能找到的所有資訊工程師。然而，許多新創公司甚至是矽谷科技巨頭，根本沒有足夠的任務分配給這些超額徵聘的人力。這導致許多人只好裝忙，或是把原本簡單的工作拆解細化，讓每個人手上都有事可做。而敏捷開發的風行也帶來了另一種浪費工時的反效果—墨守成規地要求每天開會，卻沒有將時間花在真正的開發工作上，反而讓許多想混水摸魚的工程師樂得輕鬆。
《科技泡沫》是一本難得一見的好書，鞭闢入裡地點出前幾年網路產業中的種種亂象。我認為最近矽谷幾次的大規模裁員，表面上是 AI 工具確實帶來了效率提升、降低了企業的人力成本；但實際上，可能是這些大公司借題發揮，想利用 AI 當作擋箭牌，在利率上升、零成本資金消逝之際，趕緊修正先前過於樂觀的擴張策略，回歸理性的營運步調。
The philosopher Hegel once said, "The only thing we learn from history is that we learn nothing from history." While AI trends keep pushing stock markets higher, tech giants in Silicon Valley are cutting jobs and freezing hiring. This has created an unprecedented job crisis for computer science graduates in the U.S. Compared to 2021, when Silicon Valley engineers enjoyed enviable salaries and big bonuses, today's situation is completely different. There are countless reasons for such a big shift in industry trends. However, the issues mentioned in the book The Tech Bubble might be the straw that broke the camel's back.
In the internet industry, bright tech companies like Google, Amazon, Facebook, and Netflix grew from unknown startups into super-rich multinational corporations. At the same time, early investors made huge profits. Because of this, "venture capital" (VC) has long been a major part of the U.S. investment industry, and starting a tech company became a popular career path for many talented young people. However, the book's author, Emmanuel Marot, clearly points out that growing problems between today's VC industry and startups have caused wasted resources and massive financial losses.
When the "dot-com bubble" burst in 2000, the Nasdaq dropped 75%, and Silicon Valley cut 20,000 jobs. Over the next 20 years, more economic crises followed. Governments led by the U.S. seemed to have no new tricks and could only print large amounts of money to save the market. Whether it was "quantitative easing" (QE) or Japan's long-standing "zero-interest-rate policy," these monetary policies all fueled economic bubbles. The 1997 Asian Financial Crisis helped create the dot-com bubble, which then contributed to the 2008 global financial crisis. Similarly, the QE policy during the 2019 COVID-19 pandemic fueled the rapid growth of Silicon Valley startups—though too much money also led to widespread chaos.
Startups chasing explosive growth often end up in a bragging contest. They ignore obvious business risks and rely on three types of miracles to succeed:
Market Miracle: Consumer behavior changes suddenly (for example, people suddenly pay for services that used to be free).Technology Miracle: Promising technology or services that cannot be built today and no one knows how to make in the future (like the story in the book Bad Blood).Talent Miracle: Believing that gathering enough top talent will naturally lead to unprecedented business success (a mindset very similar to today's AI investment trend).
Normally, VC funds invest step-by-step through "seed rounds," starting small and adding more money as startups prove their value to lower risk. But in an environment flooded with cash, random investing became fertile ground for scammers.
"Land grabbing" is a favorite competitive strategy in China's internet industry. Companies use heavy money subsidies to grab market share, accepting years of huge losses. Yet, VC firms love this approach because money keeps flowing, and they just need someone who can tell a good story. The logic is simple: with enough patience and money, once a startup grows large enough, it will eventually make huge profits to cover past losses.
In reality, these cash-burning startups never built a real "moat" beyond cash subsidies. Once another rich company enters the market, expected profits drop, and price wars turn the market into a deep "red ocean." In the internet world, true moats are "network effects" (for social media and e-commerce platforms) and "switching costs" (for phones and electronics). Things like "economies of scale," "business models," and "first-mover advantage" are not reliable over time. These fake moats include the myths of "10x," "scale," and "brand."
The author wrote this book based on strange things he saw while working at a startup. Flush with excess cash, tech companies went on hiring sprees, offering high pay and great perks to snap up every engineer available. However, many startups—and even Silicon Valley giants—did not have enough work for all these hires. As a result, many employees pretended to be busy or broke simple tasks into tiny steps so everyone had something to do. The popular "Agile development" method also backfired by wasting work hours—forcing daily meetings instead of real coding work, allowing lazy engineers to slide by easily.
The Tech Bubble is a rare and great book that clearly explains the chaos in the internet industry over recent years. In my view, the recent wave of tech layoffs may seem like AI tools are boosting efficiency and cutting labor costs. In reality, large companies may just be using AI as an excuse. As interest rates rise and free money disappears, they are rushing to fix their over-optimistic expansion strategies and return to sensible business operations.
