工作的未來在孩子的臥室裡成形

這篇文章是Faiā董事總經理喬治·西奧西爾斯（George Siosi Samuels）的客人貢獻。看看Faiā如何致力於在這裡保持技術進步的最前沿。

If you've watched a kid interact with artificial intelligence (AI) lately, you've seen the future of work taking shape. Whether it's a 10-year-old calmly explaining to a chatbot why its answer needs more debugging or a teen tweaking an algorithm to brainstorm a school project, there's a new kind of thinking taking hold.

如果您最近看過一個孩子與人工智能（AI）互動，那麼您已經看到了工作的未來。無論是一個10歲的年輕人向聊天機器人解釋為什麼它的答案需要更多調試，還是一個青少年調整算法來集思廣益的學校項目，都有一種新的思考來掌握。

As someone building products with AI at a company like Faiā, I'm noticing interesting patterns. They go deeper than just tech adoption—we're seeing the next generation of talent completely redefine what the workplace will be. What's happening at kitchen tables today is a sneak peek into the cubicles, boardrooms and remote dashboards of tomorrow. Here's what we can learn and how it'll ripple through enterprise.

當有人在Faiā等公司中使用AI製造產品時，我注意到有趣的模式。他們的發展不僅僅是技術採用，我們已經看到下一代人才完全重新定義了工作場所的狀況。今天在廚房桌子上發生的事情是偷看明天的隔間，董事會和遠程儀表板。這是我們可以學習的內容以及它將如何通過企業觸動。

Steering AI with smarter inputs

用更智能輸入轉向AI

Let's start with the basics: AI doesn't run itself—it thrives on human input. Kids are already masters of sharper questions getting sharper results. Ask a generic "What's blockchain?" and you'll get a textbook dump; ask "How could blockchain cut supply chain costs by 20%?" and you've got something actionable.

讓我們從基礎知識開始：AI不會自行運行 - 它在人類的意見上蓬勃發展。孩子們已經是尖銳的問題的主人，取得了尖銳的結果。問一個通用的“什麼是區塊鏈？”而且您將獲得一本教科書；詢問“區塊鏈如何將供應鏈削減20％？”而且您有一些可行的東西。

Now, that's not just a kid skill—it's a workforce superpower. A 2023 study from the Massachusetts Institute of Technology (MIT) found that professionals who purposely refine their prompts improve AI output accuracy by up to 40%. The employees of 2035 won't be the ones who can withstand the most data—they'll be the ones who know how to steer AI toward signal, not noise.

現在，這不僅僅是孩子的技能，它是勞動力超級大國。馬薩諸塞州技術研究所（MIT）的2023年研究發現，故意完善其提示的專業人員將AI產出準確性提高了40％。 2035年的員工將無法承受最多的數據，他們將成為知道如何將AI轉向信號而不是噪音的人。

Enterprises that spot this early can build teams that don't just use tools—they optimize them. They'll integrate best practices for getting the most out of every query. It's a level of agility we'll need as tech evolves even faster.

早期發現的企業可以建立不僅使用工具的團隊，還可以優化它們。他們將整合最佳實踐，以充分利用每個查詢。隨著技術的發展甚至更快，這是我們需要的敏捷度。

AI as a workforce copilot

AI作為勞動力副駕駛

Then there's the partnership angle. Children aren't treating AI like a glorified calculator—they're turning it into a collaborator. A middle-schooler might use it to quickly mock up a business pitch idea, then add an image and have the bot suggest improvements in real-time as they type.

然後是合夥角度。孩子們不會像榮耀的計算器那樣對待AI，而是將其變成合作者。中學生可能會使用它來快速嘲笑商業宣傳的想法，然後添加圖像並讓機器人在輸入時實時改進。

Sound familiar? It's the same dynamic we're chasing in agile teams: rapid iteration, creative problem-solving, human-machine synergy. Now, institutions like Gartner are predicting that by 2027, 70% of enterprises will rely on AI as a “copilot” for decision-making, boosting productivity by 25%.

聽起來很熟悉嗎？這與我們在敏捷團隊中追逐的動態相同：快速迭代，創造性的解決問題，人機協同作用。現在，像Gartner這樣的機構預測，到2027年，70％的企業將依靠AI作為決策的“副駕駛”，將生產力提高25％。

The difference is, these kids see AI as a partner, not a crutch. For businesses, that mindset translates to workers who don't outsource thinking—they amplify it. Imagine a junior analyst who pairs AI's market analysis with their own instinct to spot a key trend faster than a legacy system ever could. That's the multiplier effect we're seeking.

不同之處在於，這些孩子將AI視為伴侶，而不是拐杖。對於企業而言，這種思維方式轉化為不外包思維的工人，他們會放大它。想像一下，一位初級分析師將AI的市場分析與自己的本能配對，以比傳統系統更快地發現關鍵趨勢。這就是我們正在尋找的乘數效​​應。

Expertise as an efficiency edge

專業知識作為效率優勢

Here's a trend worth noting: expertise cuts through the clutter. A kid who's obsessed with coding can ask AI a question like, "How do I optimize this smart contract for minimal gas fees on PoS chains?" and get there in one shot, while a newbie will burn 30 minutes cycling through basics. It's efficiency in action, and in technical terms, it's about reducing token spend for optimal throughput.

這是一個值得注意的趨勢：雜亂無章的專業知識。一個沉迷於編碼的孩子可以問AI一個問題，例如“我如何優化這份智能合同，以最少的POS鏈費用？”一鏡頭就到達那裡，而新手將燃燒30分鐘的基礎知識。這是行動效率，從技術上講，它是為了減少令牌支出以進行最佳吞吐量。

A 2024 study by Stanford showed that domain experts use 50% fewer queries to achieve the same results as novices when working with large language models. On a large scale, that generation will value deep knowledge in blockchain, biotech or any field as a competitive edge. The blockchain architect who can code a protocol in three prompts will outpace the one who fumbles through ten. Expertise isn't dying—it's the fuel for smarter automation.

斯坦福大學（Stanford）的一項2024年的研究表明，在使用大型語言模型時，域專家使用的查詢減少了50％，以獲得與新手相同的結果。在大規模的情況下，這一代將重視區塊鏈，生物技術或任何領域的深厚知識，以此作為競爭優勢。可以在三個提示中編碼協議的區塊鏈架構師將超過那些摸索十個提示的人。專業知識並不死，這是更聰明的自動化的燃料。

Setting boundaries for better outcomes

設定界限以提高更好的結果

And finally, it’s all about questions. Kids today aren't busy memorizing encyclopedias—they're asking "Why?" and "What if?" to get the bot thinking. It's not trivia hunting; it's strategic thinking.

最後，一切都是關於問題的。今天的孩子們並不忙於記住百科全書 - 他們問“為什麼？”和“如果？”以獲取機器人的思考。這不是瑣事狩獵；這是戰略思維。

Now, the future workforce won’t be judged by what they know—AI will have that part covered. But they’ll be assessed by the questions they can ask to generate new value. Picture a supply chain manager asking, "What's the bottleneck in our Southeast Asia node?" versus "How do we cut container delays by 20% using real-time data from Southeast Asia to optimize routing and factor in seasonality for bulk cargo?" The second question drives value.

現在，未來的勞動力將不會被他們所知道的判斷 - AI將涵蓋這一部分。但是，他們將通過他們可以要求產生新價值的問題來評估它們。想像一個供應鏈經理問：“我們東南亞節點的瓶頸是什麼？”相對於“我們如何使用來自東南亞的實時數據將容器延遲減少20％，以優化散裝貨物的季節性路由和因素？”第二個問題推動了價值。

A recent McKinsey report forecasts that by 2030, 80% of new job growth will favor skills like critical questioning over rote knowledge. So, the enterprises that foster that curiosity now—through specialized training programs, internal culture or even

麥肯錫最近的一份報告預測，到2030年，80％的新工作增長將有利於諸如批判性質疑之類的技巧而不是死記硬背的知識。因此，通過專業培訓計劃，內部文化甚至