我最近一直在思考这个问题。作为一名在城市研究与 AI 交叉领域工作的人，我不断听到各种变体的担忧：”AI 会取代城市科学家吗？”“当 AI 什么都能做时，做这些研究还有什么意义？”“在 AI 时代，我还应该读城市科学方向的博士吗？”

这些都是合理的问题。我们正处在一个 AI 模型可以生成城市图像、预测城市模式、分析卫星影像，甚至撰写研究论文的时代。就在上周，我测试了一个基础模型，基于城市地图、城市数据与城市图像产出全面的城市分析——这原本需要研究者花费数天才能完成。结果既令人印象深刻，也 honestly 有些不安。

我知道一些顶尖研究者正在努力开发更复杂的城市分析模型，以应对不同场景与应用。但可以预见，这类工作——技术建模、模式识别、数据处理——很可能在未来十年内由 AI 完成。这引出一个令人不适的问题：传统城市科学家是否会被降格为整理材料、授课与开展现场调查，而 AI 承担分析的重活？

让我们直面房间里的大象：如果 AI 能以更低成本更快地完成我们的大部分工作，谁还会付钱给我们？当基础模型能以极低成本产出类似分析时，我们如何 justify 自己的薪资？这不仅是存在主义问题，更是经济问题。

但我在这一领域工作的体会是：问题并不真正在于我们还能剩下什么——而在于什么是* uniquely 属于我们*的贡献，以及 crucially，我们能创造 AI 无法创造的价值。

AI 尚不能（至少目前不能）做的事

让我从一个故事说起。几个月前，我在做一个分析新加坡街景热舒适的项目。我们拥有全部数据——街景图像与行人调查。我们训练了复杂模型，能基于街景影像以令人印象深刻的精度预测舒适水平。AI 漂亮地完成了它的工作。

但当我思考其含义时，触动我的是：技术精度只是故事的一部分。真正重要的问题是：”为什么这个社区在变化？”“这对居住于此的人意味着什么？”“我们应该如何干预，如果有的话？”“我们的分析在服务谁的利益？”

AI 看到了模式。我们看到了人。

这是第一点仍 uniquely 属于我们的：情境理解。城市不仅是优化问题或模式识别任务。它们是人类需求、文化实践、历史遗产与社会动态的鲜活生态系统。AI 可以处理信息，却无法理解为何某个街角对社区重要，或为何技术上”最优”的方案可能因忽视地方语境而失败。

我并非在浪漫化人类直觉。我在谈更根本的东西：提出”为何重要？”与”为谁？”的能力。这些判断需要来自生活经验、文化意识与伦理推理——AI 尽管强大，却不具备。

第二点是问题建构。AI 极擅长解决我们交给它的问题。但谁决定哪些问题值得解决？谁确定我们应优化交通流量而非行人安全、碳排放或社会公平？这些不是技术问题——它们是价值判断，塑造整个城市研究与政策的方向。

我见过太多技术出色却根本方向错误的 AI 驱动城市项目，因为它们优化了错误的目标。最大化住房密度却不考虑社区凝聚力的模型；通过将车辆导至低收入社区来改善交通流量的算法。这些不是 AI 的失败——它们是人在问题建构上的失败。

第三，或许最重要的是：批判性思维与验证。AI 模型是黑箱，可能自信地出错。它们可能延续偏见、遗漏边缘情况，产出看似合理却根本有缺陷的结果。必须有人审视这些输出、对照现实验证，并理解其局限。

最近我看到一项研究用 AI 模型预测城市增长模式。预测表面合理，直到你意识到它们完全忽略了现有分区法规、基础设施约束与政治现实。模型从数据中学到了模式，却对实际治理城市发展的制度框架毫无理解。没有人类专业知识捕捉这些问题，这类研究可能毫无用处——或更糟，具有误导性。

我们应如何不同地行动（以及钱在哪里）

那么 AI 时代对城市科学家意味着什么？我不认为应抵制 AI 或假装它没有改变我们的领域——那艘船已经启航。相反，我认为我们需要演进角色——并理解何处能创造真实、可货币化的价值。

我们需要更擅长提问，而非仅擅长回答。 城市研究的瓶颈 increasingly 不是算力或数据可得性——而是知道哪些问题重要、如何恰当地框定它们。这需要深厚的领域知识、理论根基，以及将技术能力与真实城市挑战连接的能力。

而这一点是有价值的。雇佣城市科学家的组织——无论是政府机构、房地产开发商还是研究机构——不仅想要数据分析。他们想要能告诉他们首先该分析什么的人。市场重视能框定正确问题的人，而非仅运行模型的人。我现在花在问题建构上的时间多于方法论，我相信真正价值在此。

我们需要成为翻译者与整合者。 城市是复杂系统，需要社会学、经济学、环境科学、公共卫生、设计、政策等多学科洞见。AI 可以处理所有这些领域的数据，但没有人类引导无法有意义地整合。必须有人 bridging 技术能力与实践应用、模型输出与政策建议、数据模式与人类需求之间的鸿沟。

在研究中，我不断在学科与社群之间 navigate：思考如何向计算机科学家传达城市洞见，如何将 AI 发现 translate 到城市规划语境，如何将技术研究与更广泛的政策含义连接。这种翻译工作 increasingly valuable 且 uniquely human——在就业市场中薪酬优厚。组织为能讲多种”语言”——技术、领域、政策——的人支付溢价。关键不是最好的程序员或最有经验的规划师，而是桥梁。

我们需要聚焦 AI 无法回答的问题。 城市研究 entire 领域 fundamentally human：理解人们如何体验城市、探索场所的文化意义、调查城市发展中的权力动态、审视城市技术的伦理含义、想象 alternative 城市未来。

我相信最有影响力的研究 increasingly 结合 AI 驱动分析与 fundamentally human 的方法：理解人们如何体验城市、探索场所文化意义、调查权力动态。这些定性方法并未被 AI 取代——它们作为纯数据驱动方法的 counterbalance 变得更重要。经济现实是：组织 increasingly 认识到纯数据分析缺乏 human context 会导致 costly mistakes。他们 willing to pay for 结合 AI 分析能力与对数据对 real people 意味着什么的 human insight 的研究。

一种不同的专业能力（以及它如何带来回报）

那么 AI 时代考虑城市科学 career 意味着什么？我认为 field 比以往更需要人，但需要 different kind of expertise。是的，仍有 money——但可能不在你以为的地方。

你需要技术素养。不必是 AI 专家，但需要理解这些工具能做什么、如何工作、何处失败。需要批判性评估 AI 驱动研究并在自己工作中有效使用这些工具。这种 baseline 技术能力正成为入场券，而非差异化因素。

但你也需要深厚的领域专长。AI 能做的越多，human expertise 越 valuable——不是作为 AI 的 replacement，而是 complement。你需要 deeply know 城市：历史、政治、社会动态、物质形态、生态语境。这种 knowledge 让你 ask good questions、interpret results critically、connect technical analysis 与 real-world impact。crucially，这是 market 中 command premium rates 的东西。

我从就业市场与同行交流观察到：主要涉及运行模型的初级岗位 increasingly 商品化。但 require contextualising AI outputs、spotting when wrong、translating into strategic recommendations 的 roles in high demand。”can use AI tools” 与 “can critically evaluate and contextualise AI outputs” 之间的 salary gap 似乎在 widening，而非 shrinking。

你需要伦理根基。随着 AI 在塑造城市环境中更 powerful，必须有人 ask 关于 equity、justice、privacy、power 的 hard questions。谁从这些 technologies benefit？谁被 left behind？我们在 algorithms 中 encoding 什么 values？这些不是 technical 问题——fundamentally 关于我们想创造什么样的 cities。organisations increasingly willing to pay for 这种 expertise——无论是 avoid costly mistakes、manage reputational risks，还是 genuinely pursue equitable outcomes。

我看到的未来（与经济现实）

我对 AI 时代城市科学的 future actually optimistic。不是因为 AI 不会 transformative——它会。而是因为 human expertise 与 AI capabilities 的结合能 achieve 两者 alone 无法 achieve 的东西。crucially，因为对能 navigate 这一 landscape 的城市科学家存在 sustainable economic model。

我 envision 能 fluidly move 于 computational analysis 与 ethnographic research、between big data patterns 与 individual stories、between technical optimisation 与 ethical reasoning 的城市科学家。use AI 作为 powerful tool 同时 maintain critical distance from outputs 的研究者。bridge technical possibility 与 social desirability 的 professionals。

economic landscape 以 interesting ways shifting。涉及 data processing 与 basic analysis 的传统 “research assistant” roles 可能 face downward salary pressure——AI 能做 much of that work。但 require judgment、contextualisation、strategic thinking 的 roles becoming more valuable。career ladder 并未 disappearing；它 becoming steeper。entry-level 与 senior positions 之间的 gap widening，但对能 master full stack——technical skills、domain expertise、strategic thinking——的人 ceiling 也在 rising。

我也看到 new revenue streams emerging：consulting on responsible AI implementation in cities；validating 与 stress-testing AI-driven urban plans；training urban professionals effectively work with AI tools；developing hybrid methodologies 结合 AI analysis 与 human insight。这些 services command good rates，因为它们 address pure technology companies 无法 meet 的 real needs。

future cities 将由 AI shaped，但需要 human wisdom guided。需要理解这些 technologies 的 power 与 limitations、ask right questions 并 critically interpret answers、ensure technical capabilities serve human needs 而非反过来的人。他们 willing to pay for it。

那么 AI 时代城市科学家还能做什么？Everything that matters。值得问的问题。值得理解的 contexts。值得 defend 的 values。值得 imagine 的 futures。是的，make good living doing meaningful work 的能力。

AI 在 changing 我们的 tools，但未 changing 我们的 purpose：better understand cities 以便 make them work better for people who live in them。这一 mission 仍 as important as ever——perhaps more so in age where technology reshaping urban life at unprecedented pace。market increasingly recognising 能在 AI-augmented world effectively pursue 这一 mission 的 professionals 的价值。

real question 不是 AI 时代是否有 urban scientists 的位置，或我们能否 make living。而是我们是否 ready evolve practice 以 meet 这一 moment，并 position ourselves where 能 create 与 capture value。I think we can be。I think we must be。

---

你在城市研究中与 AI 的经验如何？你如何看待城市科学家角色的演变？欢迎在评论中分享你的想法。

AI 尚不能（至少目前不能）做的事

让我从一个故事说起。几个月前，我在做一个分析新加坡街景热舒适的项目。我们拥有全部数据——街景图像与行人调查。我们训练了复杂模型，能基于街景影像以令人印象深刻的精度预测舒适水平。AI 漂亮地完成了它的工作。

但当我思考其含义时，触动我的是：技术精度只是故事的一部分。真正重要的问题是：”为什么这个社区在变化？”“这对居住于此的人意味着什么？”“我们应该如何干预，如果有的话？”“我们的分析在服务谁的利益？”

AI 看到了模式。我们看到了人。

这是第一点仍 uniquely 属于我们的：情境理解。城市不仅是优化问题或模式识别任务。它们是人类需求、文化实践、历史遗产与社会动态的鲜活生态系统。AI 可以处理信息，却无法理解为何某个街角对社区重要，或为何技术上”最优”的方案可能因忽视地方语境而失败。

我并非在浪漫化人类直觉。我在谈更根本的东西：提出”为何重要？”与”为谁？”的能力。这些判断需要来自生活经验、文化意识与伦理推理——AI 尽管强大，却不具备。

第二点是问题建构。AI 极擅长解决我们交给它的问题。但谁决定哪些问题值得解决？谁确定我们应优化交通流量而非行人安全、碳排放或社会公平？这些不是技术问题——它们是价值判断，塑造整个城市研究与政策的方向。

我见过太多技术出色却根本方向错误的 AI 驱动城市项目，因为它们优化了错误的目标。最大化住房密度却不考虑社区凝聚力的模型；通过将车辆导至低收入社区来改善交通流量的算法。这些不是 AI 的失败——它们是人在问题建构上的失败。

第三，或许最重要的是：批判性思维与验证。AI 模型是黑箱，可能自信地出错。它们可能延续偏见、遗漏边缘情况，产出看似合理却根本有缺陷的结果。必须有人审视这些输出、对照现实验证，并理解其局限。

最近我看到一项研究用 AI 模型预测城市增长模式。预测表面合理，直到你意识到它们完全忽略了现有 zoning 法规、基础设施约束与政治现实。模型从数据中学到了模式，却对实际治理城市发展的制度框架毫无理解。没有人类专业知识捕捉这些问题，这类研究可能毫无用处——或更糟，具有误导性。

我们应如何不同地行动（以及钱在哪里）

那么 AI 时代对城市科学家意味着什么？我不认为应抵制 AI 或假装它没有改变我们的领域——那艘船已经启航。相反，我认为我们需要演进角色——并理解何处能创造真实、可货币化的价值。

我们需要更擅长提问，而非仅擅长回答。 城市研究的瓶颈 increasingly 不是算力或数据可得性——而是知道哪些问题重要、如何恰当地框定它们。这需要深厚的领域知识、理论根基，以及将技术能力与真实城市挑战连接的能力。

而这一点是有价值的。雇佣城市科学家的组织——无论是政府机构、房地产开发商还是研究机构——不仅想要数据分析。他们想要能告诉他们首先该分析什么的人。市场重视能框定正确问题的人，而非仅运行模型的人。我现在花在问题建构上的时间多于方法论，我相信真正价值在此。

我们需要成为翻译者与整合者。 城市是复杂系统，需要社会学、经济学、环境科学、公共卫生、设计、政策等多学科洞见。AI 可以处理所有这些领域的数据，但没有人类引导无法有意义地整合。必须有人 bridging 技术能力与实践应用、模型输出与政策建议、数据模式与人类需求之间的鸿沟。

在研究中，我不断在学科与社群之间 navigate：思考如何向计算机科学家传达城市洞见，如何将 AI 发现 translate 到城市规划语境，如何将技术研究与更广泛的政策含义连接。这种翻译工作 increasingly valuable 且 uniquely human——在就业市场中薪酬优厚。组织为能讲多种”语言”——技术、领域、政策——的人支付溢价。关键不是最好的程序员或最有经验的规划师，而是桥梁。

我们需要聚焦 AI 无法回答的问题。 城市研究 entire 领域 fundamentally human：理解人们如何体验城市、探索场所的文化意义、调查城市发展中的权力动态、审视城市技术的伦理含义、想象 alternative 城市未来。

我相信最有影响力的研究 increasingly 结合 AI 驱动分析与 fundamentally human 的方法：理解人们如何体验城市、探索场所文化意义、调查权力动态。这些定性方法并未被 AI 取代——它们作为纯数据驱动方法的 counterbalance 变得更重要。经济现实是：组织 increasingly 认识到纯数据分析缺乏 human context 会导致 costly mistakes。他们 willing to pay for 结合 AI 分析能力与对数据对 real people 意味着什么的 human insight 的研究。

一种不同的专业能力（以及它如何带来回报）

那么 AI 时代考虑城市科学 career 意味着什么？我认为 field 比以往更需要人，但需要 different kind of expertise。是的，仍有 money——但可能不在你以为的地方。

你需要技术素养。不必是 AI 专家，但需要理解这些工具能做什么、如何工作、何处失败。需要批判性评估 AI 驱动研究并在自己工作中有效使用这些工具。这种 baseline 技术能力正成为入场券，而非差异化因素。

但你也需要深厚的领域专长。AI 能做的越多，human expertise 越 valuable——不是作为 AI 的 replacement，而是 complement。你需要 deeply know 城市：历史、政治、社会动态、物质形态、生态语境。这种 knowledge 让你 ask good questions、interpret results critically、connect technical analysis 与 real-world impact。crucially，这是 market 中 command premium rates 的东西。

我从就业市场与同行交流观察到：主要涉及运行模型的初级岗位 increasingly 商品化。但 require contextualising AI outputs、spotting when wrong、translating into strategic recommendations 的 roles in high demand。”can use AI tools” 与 “can critically evaluate and contextualise AI outputs” 之间的 salary gap 似乎在 widening，而非 shrinking。

你需要伦理根基。随着 AI 在塑造城市环境中更 powerful，必须有人 ask 关于 equity、justice、privacy、power 的 hard questions。谁从这些 technologies benefit？谁被 left behind？我们在 algorithms 中 encoding 什么 values？这些不是 technical 问题——fundamentally 关于我们想创造什么样的 cities。organisations increasingly willing to pay for 这种 expertise——无论是 avoid costly mistakes、manage reputational risks，还是 genuinely pursue equitable outcomes。

我看到的未来（与经济现实）

我对 AI 时代城市科学的 future actually optimistic。不是因为 AI 不会 transformative——它会。而是因为 human expertise 与 AI capabilities 的结合能 achieve 两者 alone 无法 achieve 的东西。crucially，因为对能 navigate 这一 landscape 的城市科学家存在 sustainable economic model。

我 envision 能 fluidly move 于 computational analysis 与 ethnographic research、between big data patterns 与 individual stories、between technical optimisation 与 ethical reasoning 的城市科学家。use AI 作为 powerful tool 同时 maintain critical distance from outputs 的研究者。bridge technical possibility 与 social desirability 的 professionals。

economic landscape 以 interesting ways shifting。涉及 data processing 与 basic analysis 的传统 “research assistant” roles 可能 face downward salary pressure——AI 能做 much of that work。但 require judgment、contextualisation、strategic thinking 的 roles becoming more valuable。career ladder 并未 disappearing；它 becoming steeper。entry-level 与 senior positions 之间的 gap widening，但对能 master full stack——technical skills、domain expertise、strategic thinking——的人 ceiling 也在 rising。

我也看到 new revenue streams emerging：consulting on responsible AI implementation in cities；validating 与 stress-testing AI-driven urban plans；training urban professionals effectively work with AI tools；developing hybrid methodologies 结合 AI analysis 与 human insight。这些 services command good rates，因为它们 address pure technology companies 无法 meet 的 real needs。

future cities 将由 AI shaped，但需要 human wisdom guided。需要理解这些 technologies 的 power 与 limitations、ask right questions 并 critically interpret answers、ensure technical capabilities serve human needs 而非反过来的人。他们 willing to pay for it。

那么 AI 时代城市科学家还能做什么？Everything that matters。值得问的问题。值得理解的 contexts。值得 defend 的 values。值得 imagine 的 futures。是的，make good living doing meaningful work 的能力。

AI 在 changing 我们的 tools，但未 changing 我们的 purpose：better understand cities 以便 make them work better for people who live in them。这一 mission 仍 as important as ever——perhaps more so in age where technology reshaping urban life at unprecedented pace。market increasingly recognising 能在 AI-augmented world effectively pursue 这一 mission 的 professionals 的价值。

real question 不是 AI 时代是否有 urban scientists 的位置，或我们能否 make living。而是我们是否 ready evolve practice 以 meet 这一 moment，并 position ourselves where 能 create 与 capture value。I think we can be。I think we must be。

---

你在城市研究中与 AI 的经验如何？你如何看待城市科学家角色的演变？欢迎在评论中分享你的想法。

I’ve been thinking about this question a lot lately. As someone who works at the intersection of urban studies and AI, I keep hearing variations of the same concern: “Will AI replace urban scientists?” “What’s the point of doing all the research when AI can do it all?” “Should I even pursue a PhD in urban science anymore?”

These are valid questions. We’re living through a moment where AI models can generate city images, predict urban patterns, analyse satellite imagery, and even write research papers. Just last week, I tested a foundation model to produce a comprehensive urban analysis based on urban maps, urban data, and urban images that would have taken a researcher days to complete. It was both impressive and, honestly, a bit unsettling.

I know some top researchers are working hard to develop more sophisticated urban analysis models for different scenarios and applications. But it’s foreseeable that much of this work—the technical modelling, the pattern recognition, the data processing—will likely be accomplished by AI within the next decade. This raises an uncomfortable question: will traditional urban scientists be reduced to organising materials, giving lectures, and conducting field surveys while AI handles the analytical heavy lifting?

And let’s be honest about the elephant in the room: if AI can do much of what we do, faster and cheaper, why would anyone pay us? How do we justify our salaries when a foundation model can produce similar analyses at a fraction of the cost? This isn’t just an existential question—it’s an economic one.

But here’s what I’ve learned from working in this space: the question isn’t really about what’s left for us—it’s about what’s uniquely ours to contribute, and crucially, what value we can create that AI cannot.

The things AI can’t (yet) do

Let me start with a story. A few months ago, I was working on a project analysing thermal comfort in Singapore’s streetscapes. We had all the data—street view images and pedestrian surveys. We trained sophisticated models that could predict comfort levels with impressive accuracy based on SVIs. The AI did its job beautifully.

But here’s what struck me when thinking about the implications: technical accuracy is only part of the story. The questions that really matter are: “Why is this neighbourhood changing?” “What does this mean for the people who live there?” “How should we intervene, if at all?” “Whose interests are we serving with this analysis?”

The AI saw patterns. We saw people.

This is the first thing that remains uniquely ours: contextual understanding. Cities aren’t just optimisation problems or pattern recognition tasks. They’re living, breathing ecosystems of human needs, cultural practices, historical legacies, and social dynamics. AI can process information, but it can’t understand why a particular street corner matters to a community, or why a technically “optimal” solution might fail because it ignores local context.

I’m not romanticising human intuition here. I’m talking about something more fundamental: the ability to ask “why does this matter?” and “for whom?” These questions require judgment that comes from lived experience, cultural awareness, and ethical reasoning—things that AI, for all its power, doesn’t possess.

The second thing is problem formulation. AI is incredibly good at solving problems we give it. But who decides what problems are worth solving? Who determines that we should optimise for traffic flow versus pedestrian safety versus carbon emissions versus social equity? These aren’t technical questions—they’re value judgements that shape the entire direction of urban research and policy.

I’ve seen too many AI-driven urban projects that are technically brilliant but fundamentally misguided because they optimised for the wrong things. A model that maximises housing density without considering community cohesion. An algorithm that improves traffic flow by routing cars through low-income neighbourhoods. These aren’t AI failures—they’re human failures in problem formulation.

The third thing, and perhaps the most important: critical thinking and validation. AI models are black boxes that can be confidently wrong. They can perpetuate biases, miss edge cases, and produce results that look plausible but are fundamentally flawed. Someone needs to interrogate these outputs, validate them against reality, and understand their limitations.

Recently, I came across a study that used an AI model to predict urban growth patterns. The predictions looked reasonable on the surface until you realised they completely ignored existing zoning laws, infrastructure constraints, and political realities. The model had learnt patterns from data but had no understanding of the institutional frameworks that actually govern urban development. Without human expertise to catch these issues, such research could be useless—or worse, misleading.

What we should be doing differently (and where the money is)

So what does this mean for urban scientists in the age of AI? I don’t think it means we should resist AI or pretend it’s not transforming our field. That ship has sailed. Instead, I think we need to evolve our role—and understand where we can create real, monetisable value.

We need to become better at asking questions, not just answering them. The bottleneck in urban research is increasingly not computational power or data availability—it’s knowing what questions matter and how to frame them properly. This requires deep domain knowledge, theoretical grounding, and the ability to connect technical capabilities with real urban challenges.

And here’s the thing: this skill is valuable. The organisations hiring urban scientists—whether government agencies, property developers, or research institutions—don’t just want data analysis. They want people who can tell them what to analyse in the first place. The market values those who can frame the right questions, not just run the models. I spend more time now thinking about problem formulation than I do about methodology, and I believe this is where the real value lies.

We need to become translators and integrators. Cities are complex systems that require insights from multiple disciplines—sociology, economics, environmental science, public health, design, policy. AI can process data from all these domains, but it can’t integrate them meaningfully without human guidance. Someone needs to bridge the gap between technical capabilities and practical applications, between model outputs and policy recommendations, between data patterns and human needs.

In research, I find myself constantly navigating between different disciplines and communities: thinking about how to communicate urban insights to computer scientists, how AI findings translate to urban planning contexts, how technical research connects to broader policy implications. This translation work is increasingly valuable and uniquely human—and well-compensated in the job market. Organisations pay premium rates for people who can speak multiple “languages”: technical, domain-specific, and policy-oriented. It’s not about being the best coder or the most experienced planner; it’s about being the bridge.

We need to focus on the questions AI can’t answer. There are entire domains of urban research that remain fundamentally human: understanding how people experience cities, exploring the cultural meaning of places, investigating power dynamics in urban development, examining the ethical implications of urban technologies, imagining alternative urban futures.

I believe the most impactful research increasingly combines AI-driven analysis with approaches that remain fundamentally human: understanding how people experience cities, exploring the cultural meaning of places, investigating power dynamics in urban development. These qualitative methods aren’t being replaced by AI—they’re becoming more important as counterbalances to purely data-driven approaches. And here’s the economic reality: organisations increasingly recognise that pure data analysis without human context leads to costly mistakes. They’re willing to pay for research that combines AI’s analytical power with human insight into what the data actually means for real people.

A different kind of expertise (and how it pays off)

So what does this mean for anyone considering a career in urban science in the age of AI? I think the field needs people more than ever, but it needs a different kind of expertise. And yes, there’s still money in it—but not where you might think.

You need technical literacy. You don’t necessarily need to be an AI expert, but you need to understand what these tools can do, how they work, and where they fail. You need to be able to critically evaluate AI-driven research and use these tools effectively in your own work. This baseline technical competency is becoming the entry ticket, not the differentiator.

But you also need deep domain expertise. The more AI can do, the more valuable human expertise becomes—not as a replacement for AI, but as a complement to it. You need to know cities deeply: their history, their politics, their social dynamics, their physical form, their ecological context. This knowledge is what allows you to ask good questions, interpret results critically, and connect technical analysis to real-world impact. And crucially, this is what commands premium rates in the market.

Here’s something I’ve observed from the job market and talking to peers: junior positions that mainly involve running models are increasingly commoditised. But roles that require contextualising AI outputs, spotting when they’re wrong, and translating them into strategic recommendations are in high demand. The salary gap between “can use AI tools” and “can critically evaluate and contextualise AI outputs” seems to be widening, not shrinking.

And you need ethical grounding. As AI becomes more powerful in shaping urban environments, someone needs to ask the hard questions about equity, justice, privacy, and power. Who benefits from these technologies? Who gets left behind? What values are we encoding in our algorithms? These aren’t technical questions—they’re fundamentally about what kind of cities we want to create. And increasingly, organisations are willing to pay for this expertise—whether it’s to avoid costly mistakes, manage reputational risks, or genuinely pursue equitable outcomes.

The future I see (and the economic reality)

I’m actually optimistic about the future of urban science in the age of AI. Not because AI won’t be transformative—it will be. But because the combination of human expertise and AI capabilities can achieve things neither could do alone. And critically, because there’s a sustainable economic model for urban scientists who can navigate this new landscape.

I envision urban scientists who can fluidly move between computational analysis and ethnographic research, between big data patterns and individual stories, between technical optimisation and ethical reasoning. Researchers who use AI as a powerful tool while maintaining critical distance from its outputs. Professionals who can bridge the gap between technical possibility and social desirability.

The economic landscape is shifting in interesting ways. Traditional “research assistant” roles that involve data processing and basic analysis will likely face downward pressure on salaries—AI can do much of that work. But roles that require judgment, contextualisation, and strategic thinking are becoming more valuable. The career ladder isn’t disappearing; it’s becoming steeper. The gap between entry-level and senior positions is widening, but the ceiling is also rising for those who can master the full stack: technical skills, domain expertise, and strategic thinking.

I see new revenue streams emerging too. Consulting on responsible AI implementation in cities. Validating and stress-testing AI-driven urban plans. Training urban professionals to work effectively with AI tools. Developing hybrid methodologies that combine AI analysis with human insight. These services command good rates because they address real needs that pure technology companies can’t meet.

The cities of the future will be shaped by AI, but they need to be guided by human wisdom. They need people who understand both the power and the limitations of these technologies, who can ask the right questions and interpret the answers critically, who can ensure that technical capabilities serve human needs rather than the other way around. And they’re willing to pay for it.

So what’s left for urban scientists in the age of AI? Everything that matters. The questions worth asking. The contexts worth understanding. The values worth defending. The futures worth imagining. And yes, the ability to make a good living doing meaningful work.

AI is changing our tools, but it’s not changing our purpose: to understand cities better so we can make them work better for the people who live in them. That mission remains as important as ever—perhaps more so in an age where technology is reshaping urban life at an unprecedented pace. And the market is increasingly recognising the value of professionals who can pursue that mission effectively in an AI-augmented world.

The real question isn’t whether there’s a place for urban scientists in the age of AI, or whether we can make a living. It’s whether we’re ready to evolve our practice to meet this moment, and position ourselves where we can create and capture value. I think we can be. I think we must be.

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What’s your experience with AI in urban research? How do you see the role of urban scientists evolving? I’d love to hear your thoughts in the comments.