平衡人工智能、商业效率与环境责任
随着人工智能在各行业的广泛应用，一个关键议题日益凸显：AI如何与企业的气候承诺及环境、社会和治理（ESG）目标相协调。尽管早期关于AI能耗高、耗水量大的担忧曾让一些企业望而却步，但如今的技术进展和战略优化正推动AI成为实现可持续发展的重要工具。
本文基于对星巴克企业架构负责人Ashutosh Ahuja的深度访谈，提炼出企业在推进AI部署的同时兼顾气候目标的实用路径，涵盖技术选型、运营优化与行业应用等多个维度。
重新审视AI的碳足迹：从担忧到可控优化
过去，诸如“一次ChatGPT提问消耗500毫升水”之类的流行研究引发了公众对AI环境影响的广泛关注。这类信息一度使部分中小企业质疑AI是否值得投入。然而，随着技术演进，我们应以更全面、动态的视角看待这一问题。
AI确实带来了更高的数据中心负荷、GPU算力需求以及冷却系统的能源消耗。但这并不意味着企业应当回避AI。相反，关键在于有意识地控制和优化其使用方式。
例如，机器学习驱动的智能恒温器（如Nest）能够根据用户习惯自动调节室内温度，显著降低能源消耗和碳排放。这表明，AI本身既可以是资源消耗者，也可以是节能减排的推动者。
此外，云计算成本失控的教训提醒我们：无节制的计算负载不仅造成财务浪费，也带来环境代价。因此，企业应定期审查计算资源利用率，并采用自动扩缩容（auto-scaling） 技术，避免资源闲置或过度配置——这一举措直接关联于更小的碳足迹。
用数据驱动业务效率：将ESG融入日常运营
衡量AI的环境影响，不能仅停留在能源消耗或直接排放层面。真正的价值在于通过数据洞察提升整体运营效率，从而自然契合ESG目标。
AI正在被广泛用于分析客流量模式。例如，一家小型蛋糕店可通过AI识别顾客到店高峰时段，据此调整营业时间、人员排班和电力使用，从而减少不必要的资源开销。这种精细化管理不仅能降低成本，还能量化其实现的环保贡献。
同时，企业可结合周末与工作日的需求差异，利用AI预测分析优化库存管理，减少食品浪费或其他物料损耗。这些措施不仅提升盈利能力，也为ESG合规提供了可追踪、可报告的实际成果。
AI在环保领域的高潜力应用场景
多个传统上依赖人工干预的行业，正因AI的引入迎来变革性突破。
1. 预测性维护：从“坏了再修”到“提前预防”
在可再生能源、制造业等设备密集型行业中，维护通常采取被动响应模式。而通过在关键设备上安装物联网（IoT）传感器并接入AI模型，企业可以实现预测性维护。
AI能提前识别设备异常，预判故障发生时间，安排主动维修，避免突发停机带来的高能耗修复过程。虽然初期需投入数据基础设施和建模能力，但从长期看，投资回报清晰可见——许多案例显示，三年内即可收回成本。
2. 智能垃圾分类：提升回收效率
计算机视觉与传感技术的结合，在废物管理领域展现出巨大潜力。AI摄像头可实时识别垃圾种类，并结合重量数据判断应归入可回收物还是普通垃圾。
这项技术减少了人工分拣的需求，提高了处理中心的运作效率，间接降低了运输车辆的燃油消耗与碳排放。更重要的是，它为大规模实现循环经济提供了技术支持。
迈向更高效的AI模型：轻量化与边缘计算
近年来，AI模型设计趋势正从“参数越多越好”转向更精简、更高效的架构。越来越多的小型化大语言模型（LLM）可在本地设备或边缘节点运行，大幅减少对云端算力的依赖。
这不仅提升了响应速度和数据隐私保护水平，也显著降低了跨网络传输所产生的能耗。尽管AI能力仍在持续增强，但“效率优先”的设计理念已成为主流。
企业可通过以下方式减轻AI使用的碳影响：
– 选用参数规模适中的模型
– 优化计算资源配置
– 利用本地部署（on-premises）或边缘AI方案
这些实践有助于企业在不牺牲数字化转型进度的前提下，走在气候责任的前沿。
企业落地AI+气候协同的五大行动建议
为在推进AI应用的同时有效支持气候目标，组织可参考以下策略：
1. 谨慎但积极地试点
从小范围项目入手，聚焦那些能直接提升效率、带来明确环境效益的应用场景，如能耗监控、供应链优化等。
2. 坚持“人在环路”原则
确保关键决策中保留人类判断。算法输出应作为辅助依据，而非唯一指令。当直觉提示结果不合理时，务必重新评估上下文。
3. 追踪正确的指标
超越单纯的能耗统计，纳入更多业务运营维度的数据：
– 客流与工作负载模式
– 预测分析的准确率与执行效果
– ESG对齐的关键绩效仪表盘
4. 关注行业特定机会
不同行业存在独特的高影响力切入点：
– 制造业：预测性维护
– 零售业：智能排班与库存管理
– 废弃物处理：自动化分类系统
5. 回归业务本质做决策
避免盲目追逐技术热点。深入理解自身商业模式，选择真正匹配需求的投资方向。信任身边有实战经验的人，而非泛泛之谈。
结语：AI不是气候问题的对立面，而是解决方案的一部分
将AI与气候战略相结合，需要具体、务实的方法论：精准测量业务环节、优化计算资源、审慎选择部署场景，确保AI的长期收益足以抵消初期的环境成本。
那些愿意采取理性、渐进式实践的企业，将在财务回报与生态贡献两方面获得远超预期的成果。未来，随着AI模型越来越高效、应用场景越来越深入，这场关于“发展 vs 可持续”的讨论终将走向融合——因为AI本身，正逐渐成为可持续未来的引擎。
—英文原文—
原标题: Ep 667: Aligning AI With Climate And Business Goals
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Aligning Artificial Intelligence with Climate and Business Goals: Actionable Strategies from Everyday AI
As the adoption of artificial intelligence intensifies across industries, a critical conversation has emerged regarding its impact on climate goals and Environmental, Social, and Governance (ESG) priorities. Despite early concerns—such as water usage per chat query and massive energy demands in data centers—current strategies offer nuanced solutions that harmonize business growth with sustainability. The insights below, drawn directly from the Everyday AI podcast, outline concrete approaches for organizations seeking to balance AI investments with climate objectives.
AI Climate Impact: Navigating Carbon Footprint and Data Center Optimization
Early viral studies cautioning about the environmental toll of AI—like equating ChatGPT queries to high water consumption—caused hesitation among firms pursuing climate pledges. Now, with trillion-dollar investments into data infrastructure, scrutiny is sharper than ever on energy usage, cooling methods, and emissions linked to AI development.
Instead of avoiding AI entirely, companies should assess how its deployment can be controlled and optimized. For example, machine learning-driven technologies such as smart thermostats can decrease energy bills and reduce carbon emissions through automated adjustment to user patterns. Moreover, cloud computing costs have become a cautionary tale; unchecked workloads can result in not only financial, but also environmental excess. Businesses are advised to prioritize regular reviews of compute utilization and leverage auto-scaling to curtail resource waste—a measure directly correlated with a smaller carbon footprint.
Business Efficiency Metrics: Foot Traffic Patterns and ESG Compliance
Measuring the environmental impact of AI calls for more than just tracking energy consumption or direct emissions. Data-driven analysis of business operations can unlock new efficiencies that align with ESG targets. AI is increasingly used to detect foot traffic patterns, enabling small enterprises to tailor hours of operation and resource utilization. For example, a cake shop analyzing customer arrival times can adjust staffing and utilities, thereby shrinking their environmental and financial overhead.
Companies should also apply metrics like weekend versus weekday demand and employ AI-powered predictive analytics to refine inventory and minimize waste. These actions not only drive revenue but provide measurable compliance with climate goals, propelling business operations towards genuine sustainability.
Environmental Sector AI Applications: Predictive Maintenance and Waste Management
Industries as varied as renewable energy and waste management are only beginning to tap the strategic potential of AI. In sectors where machine maintenance is primarily reactive, the adoption of machine learning for predictive analytics offers substantial returns. By installing IoT sensors and connecting them to AI models, organizations can anticipate equipment failure, scheduling maintenance proactively and avoiding energy-intensive breakdowns. While initial investments in data infrastructure and modeling capabilities are required, the long-term benefits—down to specific cost offsets within three years—are quantifiable.
Computer vision and sensor integration, especially in waste sorting, empower faster, more accurate recycling decisions. These technologies facilitate less manual sorting, streamline facility operations, and directly reduce fuel usage and emissions—all while contributing to scalable ESG compliance.
AI Model Efficiency: The Currency of Smaller, Leaner Large Language Models
Recent advances in AI model design demonstrate a shift from parameter-heavy systems to more streamlined architectures. With the proliferation of edge AI and locally deployable models, businesses are able to considerably reduce cloud dependencies and related environmental impacts. Yet, growth in AI capabilities shows no signs of slowing; the quest remains to balance increasing sophistication with operational efficiency.
Efforts to minimize carbon impacts within AI usage—through adopting smaller models, optimizing compute, or leveraging on-premises resources—can position companies at the forefront of the climate conversation without sacrificing digital transformation initiatives.
Actionable Strategies for AI and Climate Alignment
To extract meaningful value from AI while advancing climate ambitions, organizations should:
Adopt cautiously, but actively : Begin with pilot projects, focusing on solutions directly linked to efficiency and clear environmental payoff.
Prioritize human judgement : Maintain “human-in-the-loop” protocols to ensure business decisions are context-aware and not driven solely by algorithmic outputs.
Track the right metrics : Move beyond energy usage alone, incorporating business operational patterns, predictive analytics output, and ESG-aligned dashboards.
Consider sector-specific opportunities : Explore predictive maintenance and waste management for high-impact wins in sustainability.
In sum, integrating AI with climate strategy requires specificity—measuring exact business operations, optimizing compute, and carefully selecting deployment scenarios where AI’s long-term benefits can offset initial environmental costs. Companies willing to implement pragmatic, measured practices will find the returns on their investments—both financial and ecological—to far outweigh the initial hurdles.
Topics Covered in This Episode:
AI’s Environmental Impact and Climate Concerns
Companies Aligning AI with ESG Goals
AI Adoption Versus Carbon Footprint Tradeoffs
Metrics for Measuring AI’s Environmental Impact
Business Efficiency Gains from AI Adoption
Real-World Examples: AI Offsetting Carbon Footprint
Industry Opportunities for Sustainable AI Integration
Future Trends: Efficient AI Models and Edge Computing
Episode Transcript
Jordan Wilson [00:00:17]: One thing that we don’t talk maybe enough on this show about is AI’s impact on the climate. I think this is maybe a hotter button issue earlier on when there was all these kind of viral studies that compared, you know, the number of questions you ask chat g p t with a certain amount of water. And then you had businesses maybe saying, hey, AI isn’t for us. It’s bad for the environment. It’s bad for your company’s climate goals. But here we are many years later since the chat GPT moment, now more than three years. And I think it’s time to have maybe a more in-depth conversation about how companies can align their AI goals with their climate goals as well. So whether you work in a field related to the environment or maybe you’re in charge of ESG at your company, I think today’s show is going to be for you, and I’m excited to talk about it here on Everyday AI. Jordan Wilson [00:01:18]: What’s going on y’all? My name is Jordan Wilson, and welcome to Everyday AI. This is an unedited, unscripted daily livestream podcast and free daily newsletter helping everyday business leaders like you and me not just keep up with what’s happening in the world of AI because it’s nonstop, but how we can make sense of it and grow our companies and our careers. So if that’s what you’re trying to do, it starts here. But to take it in the next level, make sure to go to our website at youreverydayai.com. Go sign up for the free daily newsletter. We’re gonna be recapping the highlights from today’s interview as well as keeping you up to date with all of the other AI news that you need to be the smartest person in AI at your company or in your department. So without further ado, I’m excited for today’s guest, and for this conversation, and I know it’s gonna be a very helpful one. So, livestream audience, if you could, please help me welcome to the show our guests for today, Ashu Haja, who is the enterprise architecture lead at Starbucks. Jordan Wilson [00:02:14]: Ashu, thank you so much for joining the Everyday AI Show. Ashutosh Ahuja [00:02:17]: Absolutely, Jordan. Thanks for having me. It’s great to be here. And this this already reminds me of the good conversations I’ve had in the past, so I’m thrilled. Jordan Wilson [00:02:24]: Alright. Yeah. This is gonna be a good one for sure. But, Shu, before we get started, just let our audience know a little bit, about your background and also kind of, like, what your role, at Starbucks entails. Ashutosh Ahuja [00:02:35]: Absolutely. I’m enterprise architect lead at Starbucks. I focus on, sustainable coffee house solutions. My work looks at the full store built, life cycle, essentially, from everything the technologies we use, choose, to build and design our stones with sustainability and the ESG compliance across our operations. That’s, that’s the gist of it. Jordan Wilson [00:03:01]: No. That’s great. And and, you you know, maybe for, some of our audience that isn’t, you know, super in tune with the ESG. Right? So environmental, social, governance. Right? I remember, like, ten years ago having to look that up. Right? Coming from a smaller company, I’m like, what the heck? But, you you know, maybe can you talk a little bit about, kind of why, you know, AI has become over the last couple of years, maybe more highlighted than other recent technologies, especially when it comes to environmental concerns? Ashutosh Ahuja [00:03:31]: For sure. I think the biggest, the biggest, highlight of the AI has been how rapidly it has evolved and touched everyday users. Many times back when ten, fifteen, twenty years ago when cloud, excuse me, cloud was hot. It everyday user could not really relate to it. It was still at a business level, or the end users weren’t really feeling the kick of it. With AI, the end users were the first one who who adopted to it. The businesses , started coming into it. In a way, this is the other way around conversation. Ashutosh Ahuja [00:04:16]: So so when when end users, they come in in a mass, it it creates, suddenly the positive. At the same time, it actually adds more of a carbon footprint. They get more data centers, more GPUs, and everything. So, it they are very related. And I think the reason why this got a bigger hype out of everything else, in the last hundred years or so, in my opinion, based on, I’m not that old. So I’ve been, you know, reading about things, primarily around how quickly users adapted to it. And then, and then all the negatives came along with it. Right? What what impact it has in the environment? Jordan Wilson [00:05:00]: Yeah. And I think that, potential negative side or the potential downside or the environmental impact has really been thrust in the spotlight a little more recently. Right? When you see these, the the big tech companies making these, $100,000,000,000 investments in these data centers. Right? And now all of a sudden you have everyday people like me reading about, you know, water cooling for GPUs. Right? And the amount of energy that’s needed to run these AI data centers, should. Right? And and and maybe this is more for our our enterprise audience that, you know, have companies that have to make climate pledges and things like that. But how should companies be looking at the trade off, right, between investing in AI and maybe being more efficient with the environmental toll said AI may ultimately have? Ashutosh Ahuja [00:05:52]: Yeah. I I I think we certainly, we have to be cognizant of, how much footprint we leave for our kids and then generations to come. And I think at the same time, I I certainly feel in the long run, it it is gonna offset, whatever carbon footprint we’re doing, but maybe the good of him the good for the humanity. I was reading, something on LinkedIn maybe about a couple of weeks ago. We could be we it could detect breast cancer five years before, it even happens. So imagine evolving to I know we’re not there yet. Imagine evolving to a to a level where you could detect something that bad disease, which which helps you save lives. And I think if if you start to compare it, and there are many examples comes like this, then, then then I think it speaks on its own. Ashutosh Ahuja [00:06:51]: So, carbon footprint, though it is very, very, very important, but, the amount of value it adds or it could add in the long run suddenly offsets everything else. Jordan Wilson [00:07:03]: Yeah. And that’s that’s a great point. And, you you you know, I think some of this conversation goes back to that original that I kind of alluded to, in the opening there. I think, what it was is there was a 2023 study that said, you know, for every 20 to 50 questions you ask CHAT gpt, it was 500 milliliters of water. And I think that maybe that study very early on, caused a lot of maybe smaller companies to say, hey, maybe AI isn’t for us. Right? Look at this impact. Do you think now that companies should still be, you know, looking at their AI usage, maybe on the smaller and medium size side to studies like that and saying, hey, We shouldn’t use AI at our company. Is that something company should actually be asking themselves? Ashutosh Ahuja [00:07:55]: They can ask, but I think they should certainly look to adopt AI. It has way, way more advantages than not using it. I’ll give you something super simple, example, Nest thermostat. Yes. It’s owned by Google now, but back when it was not acquired by Google, it was a smallish company. Yes. It was big valued. It was a smallish company. Ashutosh Ahuja [00:08:19]: It started very, very small. It it uses machine learning to detect your patterns, how much temperature you like it, what what at what time of the day, and it actually adjusts as that. And it also have an impact on your, on your, carbon footprint. Low energy bills, less the temperature, less electricity usage, and less less carbon footprint. But from a smaller but from a smaller enterprises or smaller companies, they certainly should. At the same time, I think we need to be cautious. It’s a very tempting thing to do. I remember back when cloud was hot and everybody hopped on and there were stories and, posts on LinkedIn and everywhere that there there was a bill from somebody about $204,100,000 dollars because compute was running. Ashutosh Ahuja [00:09:13]: So I think we need to be cognizant. Yes. Everybody should try. It has so much advantages, from productivity saving to bringing more people in, maybe bringing more businesses in, and improving your, improving your overall efficiency, the way you do it, the way you operate, but you got to be cautious. Mhmm. It it is certainly very helpful in day to day life. Jordan Wilson [00:09:38]: Yeah. And maybe what metrics, that companies, if they still want to, you know, have climate goals, if they still want to, you know, have that philanthropic side of being, you know, putting the environment first, but they still do want to use AI, right? Are there certain metrics that they should be looking at, in in terms of I again, this isn’t my space, right? The amount of, energy use, you know, measuring carbon footprint, like, how can companies still do both, but then say on the on the back end, like, yes, we are still being, you you know, environmentally friendly even though we are still using, large language models. So maybe what are some of those metrics or, things for those companies to focus on? Ashutosh Ahuja [00:10:24]: Yeah. So carbon footprint is certainly a big thing, but the I I think the metrics that are more important, are more on identifying the patterns that you have, in some in terms of in how your business operates. Looking at foot traffic, a small business, a mom and pop cake shop, they open, let’s say, seven days a week, and they can detect the pattern, you know, the on when the most customers are coming in. Do they come in between eight to ten in the morning, or do they come after eleven? Things like that. Is there more footfall? That’s the one, that’s the one pattern and, and one metric. Footfall traffic, is that more on weekends or or more on Fridays? The patterns like this that one a small businesses essentially can look at it, and then, and then go from there from whether their investments perspective, whether, whether they’re more whether they’re more compliant with the ESG goals or not. Jordan Wilson [00:11:30]: You know, when you, are looking at the future of AI, right, and it’s it’s hard for anyone to predict what might be happening in, you know, five years, let alone five weeks. But in terms of AI’s impact, with company’s climate goals, what maybe what are some things that you’re most excited about, for the future of AI and its, you know, potential impact on what might be possible that we aren’t even thinking about today? Ashutosh Ahuja [00:12:02]: That’s a good one. I I think it’s it’s mainly about how value you’re able to drive out of it, especially when you when you look at your business goals and, how well you’re adapting it. And I think it it is it is important in terms of bringing more revenue. It is less talked about and especially, small businesses , those who rely on word-of-mouth sort of marketing . The sentiment matters a lot. Five years of from now, I I think, many companies should and will be using a lot of sentiment analysis if they aren’t doing it anyway, to to look at how the brand is brand is progressing. I remember back when I, worked for Subway, before before I go into any of that, I just wanna frame the discussion, you know, that I’m speaking from my own, personal experience, not at the professional capacity, from Starbucks. Back when I worked for Subway, and Subway was going over a big, a big, shift in brand narrative and the marketing perspective. Ashutosh Ahuja [00:13:23]: Around five years ago, they did a whole menu revamp, and they and we were we were looking at the social media , how the sentiment is. And, and Subway, the billion dollar company. And, the smaller companies, smaller brands should also look at, investments like this. So five five years from now five I think it’s still too far ahead. Everybody should look at this using the tool that the disposal to do analysis like this to see where the market is heading, where customer sentiment is heading, Because that would drive a lot of business and that will allow you to serve your customers more effectively and tweak your business model, and look and, you know, hear the customer’s feedback. Jordan Wilson [00:14:11]: So you kind of already gave, you know, one good example on, you know, kind of the, the other side of AI. Right? Like, you can look at AI as, oh, it’s you know, it consumes a lot of power in all these data centers, but, yeah, it can detect, you know, maybe certain illnesses or diseases earlier, than if you weren’t using AI. Do you have any other examples specifically on, on the environmental side on how AI can actually maybe more than offset, right, the the the carbon footprint in terms of what it can help, on the environmental or the climate side. Ashutosh Ahuja [00:14:49]: Yeah. I’ll give it two in fact. So I about a year ago, I was in Toronto, and, I was looking at some projects for, some colleges or affiliated with, University of Toronto. Students , they’re they they developed a camera vision to detect the waste the kind of waste. So, there’s a big problem when you, you know, throw plastics. I’m glad, you know, states are banning now, when you throw plastic and it had a lot of impacts to it. They developed a a camera version and a sensor, which actually detects what and along with and verifies along with the weight of a product to see where it should be discarded. Should it be recycled or should be discarded? So it’s, yes, it is using a lot of resources in which gets talked about. Ashutosh Ahuja [00:15:43]: And I think, we have already started seeing some impact in a day to day life where, you have benefits in terms of less sorting of waste act waste facilities. That means less diesel, less petrol, less gas. Lot of many of the things which goes unnoticed and they are behind the scene. So, it it helps offset in those ways. So, yes, it may get talked about, you know, it uses, you know, 500 metric ton of water to run a data center. I’m just making up the numbers for now. But but but the benefits are far more than, impact it has. Another one I I remember, this was again ten, twelve years ago, ten years ago, I think. Ashutosh Ahuja [00:16:34]: I was working for Cigna, the the the consultant, and, we were, Cigna was doing some sort of an acquisition with Express Scripts. Mhmm. And, we needed to do the contact center modernization. And there was a lot about, AWS compute. We were from on on prem to AWS. We needed to go there, and, we even looked at auto scaling options. And when we did that, it it brought our compute down, resources down. It it I think it didn’t saves thousands of dollars per week. Ashutosh Ahuja [00:17:07]: And, and, again, less likely to get noticed in a day to day life, but it has a bigger impact because less compute means less energy, less energy means less carbon footprint, and better, ESG metrics. If you’re a public company, which is even a big thing, because we have a lot of SEC filings and everything, but even for a smaller company, it it helps. The the only thing with with this, with this is it’s, it’s more like this. When when something goes wrong, it goes in the deepest pocket. And, when, when the positives are less likely to come out in front of everyday user because this is a behind the scene metric. But, but I can assure you someone who works in this field, it it has a big impact, whether you whether it gets gets in front of us or not. Jordan Wilson [00:18:04]: Yeah. And and I I love those two examples that you gave, and even one I’ve been seeing a lot personally. Right? I’m I’m in Chicago, so, you know, it seems like it’s one of those cities where they’re trying out these kind of, like, delivery robots. Right? So, delivering food and, you know, at first, I’m like, okay. I, you know, don’t really understand this, and it’s obviously using a lot of, you know, AI to optimize delivery, computer vision, you know, to make sure that, little delivery robots don’t run into little kids or get hit by cars. And at first, I’m like, okay. I don’t really see the point of this. But then when you read about it a little bit more, it’s like, okay. Jordan Wilson [00:18:39]: This is probably really cutting down on just the pollutants and, you know, more getting, more cars maybe, you know, off the road and, you know, cleaner energy and all those things. You know, maybe is like, what are some of the industries, that you might see maybe adopting AI a little bit more quickly, in order to maybe have a greener footprint? You know? Delivery is one I’ve just, you know, kind of seen off offhand. But, yeah, are there are there certain sectors, that you really see a lot of potential in that maybe, you you know, we’re not there yet, but there’s a lot of potential? Ashutosh Ahuja [00:19:16]: Yeah. So let me touch on this delivering, and then I’ll I’ll speak on a sector too. I I used to think exactly like you. How is this, like, it’s gonna cut jobs. People are depending on it. Yes. It it it is gonna do it at some at some extent. But cities like Manhattan, Chicago, they’re already very crowded. Ashutosh Ahuja [00:19:40]: And having someone on on a scooter or a car delivering food, it is it adds more congestion, at the same time. And using the robots, I I think it is it is fascinating. So let me speak on on the industry side now. I’m a board member for one of our company in India. It’s it’s in the renewable energy space, and, it’s very hardcore run industry in a way that, they did develop a lot of big plants, which, which actually, uses alternative energy to, alternate waste to generate energy, be it your home food waste or something else. They don’t use, that industry, don’t rely on AI a lot. And I think there’s a huge there’s a huge opportunity. Primarily around those machines, they cost 200,000, half 1,000,000. Ashutosh Ahuja [00:20:46]: They’re big machines. And, right now, the maintenance is, especially in that industry, is like, we’ll we’ll we’ll just look at it when something breaks. Imagine, leveraging machine learning and, predictive analytics on it to see when I can do a predictive maintenance versus something of reactive maintenance. So that’s a big, that’s a big opportunity. It it yes. It will add a cost to begin with. You need IoT data to feedback to you, and you you need some models to run on it to analyze where to do it. But in the long run, that that cost is is gonna offset in, I think, in less than three years depending on what’s your investments are, obviously. Ashutosh Ahuja [00:21:34]: But, that industry is very underutilizing AI in my opinion. Jordan Wilson [00:21:41]: Yeah. And, you know, speaking of the future and and and being more efficient, it’s something I’m always, trying to keep up with and make sense of. But it seems like, you know, the, AI frontier labs are, getting more and more efficient in terms of their next and newest models. It seems like models maybe that could have been trillions of parameters, you know, two years ago are now getting into hundreds of billions. And and maybe, you know, more will run locally, right, which then would, you know, reduce, you know, some of that carbon footprint or some of the need to go to the cloud every single time. You know, might there be a point where in a couple of years, just the models are smaller, more efficient, more edge AI that this is less, of an of a conversation? Or do you think that just the the the capabilities are gonna keep increasing so it’s always gonna be, you know, climate and AI is always gonna be this kind of a balance? Ashutosh Ahuja [00:22:38]: I think models are gonna keep increasing, and the climate and AI, they’re always gonna be sort of at intersection at some point. But, I I I I personally feel this this overpowers the it it actually the value that you get out of it is way way bigger than in the long run. It it looks like this right now, like, when you start to have material results that impact mass population. Right now, the results that we have in front of us, they’re not mass produced. Right? ChargeGPT, Gemini, these models, yes, they’re mass used, but not in a layman perspective. When I say, detecting the breast cancer example I said. Right? Doing something which literally has an impact on everyday user. When when those things start to become more normal, that’s when, these conversations start to subside down. Ashutosh Ahuja [00:23:48]: So, I think they would these will continue to mass produce, and I think companies would or the even the smaller users would continue to leverage it, because it it actually brings, it can bring a lot of revenue to you if you if you do it right. Jordan Wilson [00:24:06]: So we’ve covered a lot in today’s conversation. But as we wrap up, you know, what is your one most important takeaway, for business leaders that are, you know, wanting to, maybe see bigger and better returns on AI yet at the same time? You know, ESG is a top priority, and and climate goals are still a top priority. What’s your one most important takeaway message? Ashutosh Ahuja [00:24:33]: Go go slow. At the same time, don’t hesitate to try something new. It has more advantages. Listen to someone who has direct experience in it, not me in general. Right? Those who those who you trust around you. At the same time, look be be cognizant. Don’t try to ride the hype train. Look at your business. Ashutosh Ahuja [00:25:04]: Look at look at the model you are in, and then then look for the right investments. Because at the same, I trust you. I think, this is going to be the next next big thing, or it is the next big thing in my opinion, for for generations to come. One big thing in my opinion that that often gets overlooked, human in the loop. Those those were not in the tech. This is very techy term, human in the loop. It it’s, you always have to have a human instincts. If you think something isn’t right, trust it, and and and trust your judgment to to kinda look at it again. Ashutosh Ahuja [00:25:52]: So if you keep these sort of goals in mind, I think, the results would far outweigh the hesitations of the investments that we have today. Jordan Wilson [00:26:04]: Great words of advice. And, Ashu, thank you so much for taking time out of your day to join the Everyday AI Show. We really appreciate it. Ashutosh Ahuja [00:26:12]: Absolutely. Thank you. Thanks for having me. Take care. Jordan Wilson [00:26:14]: Alright. And if you miss anything, y’all, it’s all gonna be in our newsletter. So if you haven’t already, please go to youreverydayai.com. Sign up for the free daily newsletter. Thanks for tuning in. We’ll see you back tomorrow and every day for more Everyday AI. Thanks y’all.