工作的未来在孩子的卧室里成形

这篇文章是Faiā董事总经理乔治·西奥西尔斯（George Siosi Samuels）的客人贡献。看看Faiā如何致力于在这里保持技术进步的最前沿。

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.

如果您最近看过一个孩子与人工智能（AI）互动，那么您已经看到了工作的未来。无论是一个10岁的年轻人向聊天机器人解释为什么它的答案需要更多调试，还是一个青少年调整算法来集思广益的学校项目，都有一种新的思考来掌握。

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.

当有人在Faiā等公司中使用AI制造产品时，我注意到有趣的模式。他们的发展不仅仅是技术采用，我们已经看到下一代人才完全重新定义了工作场所的状况。今天在厨房桌子上发生的事情是偷看明天的隔间，董事会和远程仪表板。这是我们可以学习的内容以及它将如何通过企业触动。

Steering AI with smarter inputs

用更智能输入转向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.

让我们从基础知识开始：AI不会自行运行 - 它在人类的意见上蓬勃发展。孩子们已经是尖锐的问题的主人，取得了尖锐的结果。问一个通用的“什么是区块链？”而且您将获得一本教科书；询问“区块链如何将供应链削减20％？”而且您有一些可行的东西。

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.

现在，这不仅仅是孩子的技能，它是劳动力超级大国。马萨诸塞州技术研究所（MIT）的2023年研究发现，故意完善其提示的专业人员将AI产出准确性提高了40％。 2035年的员工将无法承受最多的数据，他们将成为知道如何将AI转向信号而不是噪音的人。

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.

早期发现的企业可以建立不仅使用工具的团队，还可以优化它们。他们将整合最佳实践，以充分利用每个查询。随着技术的发展甚至更快，这是我们需要的敏捷度。

AI as a workforce copilot

AI作为劳动力副驾驶

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.

然后是合伙角度。孩子们不会像荣耀的计算器那样对待AI，而是将其变成合作者。中学生可能会使用它来快速嘲笑商业宣传的想法，然后添加图像并让机器人在输入时实时改进。

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%.

听起来很熟悉吗？这与我们在敏捷团队中追逐的动态相同：快速迭代，创造性的解决问题，人机协同作用。现在，像Gartner这样的机构预测，到2027年，70％的企业将依靠AI作为决策的“副驾驶”，将生产力提高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.

不同之处在于，这些孩子将AI视为伴侣，而不是拐杖。对于企业而言，这种思维方式转化为不外包思维的工人，他们会放大它。想象一下，一位初级分析师将AI的市场分析与自己的本能配对，以比传统系统更快地发现关键趋势。这就是我们正在寻找的乘数效​​应。

Expertise as an efficiency edge

专业知识作为效率优势

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.

这是一个值得注意的趋势：杂乱无章的专业知识。一个沉迷于编码的孩子可以问AI一个问题，例如“我如何优化这份智能合同，以最少的POS链费用？”一镜头就到达那里，而新手将燃烧30分钟的基础知识。这是行动效率，从技术上讲，它是为了减少令牌支出以进行最佳吞吐量。

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.

斯坦福大学（Stanford）的一项2024年的研究表明，在使用大型语言模型时，域专家使用的查询减少了50％，以获得与新手相同的结果。在大规模的情况下，这一代将重视区块链，生物技术或任何领域的深厚知识，以此作为竞争优势。可以在三个提示中编码协议的区块链架构师将超过那些摸索十个提示的人。专业知识并不死，这是更聪明的自动化的燃料。

Setting boundaries for better outcomes

设定界限以提高更好的结果

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.

最后，一切都是关于问题的。今天的孩子们并不忙于记住百科全书 - 他们问“为什么？”和“如果？”以获取机器人的思考。这不是琐事狩猎；这是战略思维。

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.

现在，未来的劳动力将不会被他们所知道的判断 - AI将涵盖这一部分。但是，他们将通过他们可以要求产生新价值的问题来评估它们。想象一个供应链经理问：“我们东南亚节点的瓶颈是什么？”相对于“我们如何使用来自东南亚的实时数据将容器延迟减少20％，以优化散装货物的季节性路由和因素？”第二个问题推动了价值。

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

麦肯锡最近的一份报告预测，到2030年，80％的新工作增长将有利于诸如批判性质疑之类的技巧而不是死记硬背的知识。因此，通过专业培训计划，内部文化甚至