Climate Stack

Show NotesWe hear a lot about electrification as quickly as possible, but most of that takes the electric grid for granted that it can handle the demand. But our guest today, Apoorv Bhargava, points out that the demand posed by electric cars is akin to putting industrial levels of demand in every residential neighborhood. With ⅓ of new cars in the US being electric by next year, the capacity just isn't there.WeaveGrid, which Apoorv co-founded, partners with utilities and automakers to make EV charging more reliable and flexible to deal with this rapidly increasing demand - and to take advantage of new opportunities, like the fact that many families will have giant batteries on wheels sitting unused at night.In This Episode:⚡ Why a single EV can have the same power demand as 2-3 homes⚡ The surprising bottleneck to decarbonization (Hint: It's not generation)⚡ Why charging at 10PM may not be the best solution for the grid⚡ The role of AI and automation in optimizing EV charging⚡ The lack of standardized protocols for EV-grid communication⚡ How WeaveGrid is building the most sophisticated EV Virtual Power Plant (VPP)⚡ The real challenge: Consumers don't want decarbonization, they want reliability!Apoorv also shares hot takes on EV adoption, public charging infrastructure, and the future of AI in energy, plus why software engineers should consider working in climate tech.About Our Guest:Apoorv Bhargavahttps://www.linkedin.com/in/apoorv-bhargava/Apoorv has spent over 12 years at the intersection of climate, energy, and technology, driving innovation in utility software, grid management, and electrification. Before founding WeaveGrid, he worked on demand-side management at Opower (now Oracle), energy storage strategy at NRG Energy, and clean tech investments in venture capital. Apoorv holds an MBA and MS in Energy & Resources from Stanford University and degrees in Chemical Engineering and Energy Economics from Rice University.Under his leadership, WeaveGrid has grown to nearly 90 employees and serves 15+ utility customers across the U.S.. In 2024, the company expanded partnerships with multiple major automakers, solidifying its position as a key player in EV-grid integration.Apoorv at RE:Invent: Apoorv Bhargava on “How Can AI Accelerate Progress in ...www.youtube.com › watchApoorv on Climate Tech Cocktails - talking, and singing: https://www.instagram.com/climatetechcocktails/reel/C-gDpLdpknc/Your Hosts Mansi Shah - Joshua Marker ClimateStack

ClimateStack is all about showing that the technology skills you already have can be applied readily to real problems that matter. In this episode, we have two guests who are parade examples of this fact. They run a climate technology consultancy, and have seen great variety of climate tech projects from several angles. About Our GuestsJaime and Jason Curtis have worked at software companies from small to large, and made the leap into climate - and working together - to put their energy toward climate resilience. We talked with them about their history in software, how they made the move to climate, and got some war stories from their experiences developing systems and advising climate startups in technology.Key Resource Mentioned In the show OptionZero - https://www.optionzero.co/Software for Climate Course - https://www.terra.do/climate-change-courses/software-for-climate/Climate Communitieshttps://workonclimate.org/https://mcj.vc/membersYour Hosts Mansi Shah - Joshua Marker ClimateStack

In this episode of The Climate Stack, we dive into the cutting-edge applications of machine learning in renewable energy with Tyler McCandless, Head of Data Sciences at Equilibrium Energy. Tyler shares how data science is driving innovation in the sector, from improving risk modeling and ROI forecasting to predicting failures and optimizing energy generation.This episode offers a comprehensive look at how technology is enabling the clean energy transition.About Our GuestTyler is an accomplished and forward-thinking data science leader renowned for spearheading strategic initiatives and delivering high-impact products at the convergence of weather, energy, and AI. Highly experienced at several companies in leading distributed cross-functional teams to conceptualize, develop, and deploy machine learning models, while balancing cost minimization with revenue maximization. Tyler is the Head of ML Sciences at Equilibrium Energy, a company focused on being energy innovators to combat climate change. Outside of work, Tyler balances a career in the sport of long distance running, having competed in four USA Olympic Marathon Trials.  Learn more about Tyler on LinkedIn.Your Hosts Mansi Shah - Joshua Marker ClimateStack

In this episode of The Climate Stack,  Mansi sits down with Vish Ganti, a visionary product leader at the nexus of clean energy, AI, and fintech. Together, they explore the transformative potential of virtual power plants (VPPs) in reducing reliance on carbon-intensive peaker plants and stabilizing the grid. Vish sheds light on how VPPs integrate distributed energy resources (DERs) and the critical need for open standards to create a frictionless ecosystem.VPPs are a prime example of how smart software optimizations can revolutionize the deployment and utilization of physical energy assets. By leveraging software to orchestrate diverse DERs, VPPs enable more efficient energy management, reducing waste and enhancing grid reliability. Discover why simplifying communication between DERs is essential for advancing sustainable energy solutions and making power management through VPP aggregators a reality.Our Guest Vish GantiVish is currently serving as Senior Director of Product Management for Virtual Power Plants (VPPs) at Qcells, Vish specializes in developing scalable energy platforms that integrate first-party hardware with industry-leading solutions from partners like Enphase, Tesla, and SolarEdge. His expertise lies in transforming distributed energy resources into bankable grid assets through advanced edge AI technologies, enabling smarter energy systems and sustainable revenue models.Previously, Vish held leadership roles at AutoGrid, where he spearheaded the development of AutoGrid Flex, a SaaS platform for VPPs and 24x7 Carbon-Free Energy solutions, and contributed to the company's acquisition by Schneider Electric. He also worked with renowned organizations such as CPower Energy, Johnson Controls, and Lawrence Berkeley National Laboratory, focusing on demand response, DER optimization, and energy system innovation.Vish holds a Master of Science in Electrical & Energy Systems from San Francisco State University and a Bachelor of Science in Electronics & Communications from Jawaharlal Nehru Technological University. A thought leader in the clean energy space, Vish has co-authored industry-recognized publications and continues to drive impactful solutions that advance the global transition to renewable energy.https://www.linkedin.com/in/vishganti/Your Hosts Mansi Shah - Joshua Marker ClimateStack

To beat fossil fuels for energy density and portability, EV charging has to be fast and ubiquitous. But the infrastructure to deliver charging is expensive, both in the chargers themselves and the power infrastructure to deliver the electricity. Companies delivering charging infrastructure are thus cautious, wanting to be sure they'll make back their investment. But this isn't somewhere we can afford to be timid. Stable Auto employs machine learning algorithms to build a cloud based SaaS product that can run deep analytics to determine optimal locations for EV charging. This lowers the risk for infrastructure investment, making it easier for businesses to justify capital investment and hastening the EV revolution. Higher profitability will inevitably also lead to more entrants in the market driving prices down for consumers and accelerating the adoption curve.Rising energy costs and thoughtful legislation have created a landscape where work like Stable's can enhance ROI for the companies capable of delivering the infrastructure while simultaneously benefiting consumers by increasing the density of EV charging options. It's a fantastic example of capitalism driving climate impact. Jamie spoke with us about Stable's machine learning approach, the state of the US energy industry, and the truly shocking amount of electricity that'll be necessary to fully switch to EVs.And, you can work for Stable! They're hiring for all roles.Our guest, Jamie SchielJamie Schiel is the Co-Founder and CTO of Stable. Before co-founding Stable, Jamie helped pioneer the use of drones by electrical utilities in New Zealand before landing at MIT Media Lab, where he built a billion frames-per-second camera for automotive and medical sensing. His contributions to clean transportation and energy earned him a spot on the Forbes 30 Under 30 list.https://www.linkedin.com/in/jamieschiel/Stable Auto: https://stable.auto/Your Hosts Mansi Shah - Joshua Marker ClimateStack

In this episode we speak with Dr Soudip Roy Chowdhury, CEO of Eugenie.ai. Eugenie is building an AI platform that is helping to reduce carbon emissions from asset-heavy manufacturing operations. Many industrial processes, such as steel and cement production, require large amounts of energy to generate high temperature process heat. The current reliance on fossil fuels for this energy makes it difficult and economically prohibitive to switch to low-carbon alternatives and effectively decarbonize.These industries are the epitome of what we think about when we think of carbon emissions. They are vast, generate a tremendous amount of carbon impact, and aren't going anywhere. Eugenie.ai is tackling the problem by bringing accuracy to emission measurement using an ensemble of methods and then deep learning their way to the most optimal operational practices. In our conversation we learned just how medieval some of this tech is, which is great news - there's a tremendous amount of slack in the system to take up. Eugenie's platform is able to improve production, reduce wastage, and improve product quality all while reducing ~30% of emissions – and because of rising costs, do it at a net financial gain to companies.Though a complete overhaul might be long ways out, the smallest efficiency gain brought about in the industrial sector by machine learning adds up to carbon saving comparable to fixing the entire transportation industry!Our Guest, Soudip Roy ChowdhurySoudip Roy Chowdhury is the founder and CEO of Eugenie.ai. He has successfully taken Eugenie from the conceptualization stage to a merger, transforming it from 0 to 1. Eugenie has been featured by Google I/O, CNBC, Forbes, and major analyst reports as an innovative solution for industrial decarbonization.With over 18 years of professional experience, Soudip has held various technical leadership roles at IBM Research, Rakuten, Tata Elxsi, Fractal AI Inc., and other AI and data startups across three continents. He holds a PhD in machine learning and is passionate about applying AI to solve large-scale problems such as climate change.At Eugenie, Soudip and his team are building an AI platform to decarbonize asset-heavy manufacturing operations. A nature lover and avid traveler, he shares his environmental passion with his daughter. In addition to his work at Eugenie, Soudip advises several startups in sustainability, AI, and healthcare domains. He is an advisor on the Forbes Tech Council, serves as an AI/ML advisor for DevNetwork, and holds an adjunct faculty position at IIT Kharagpur in India.Eugenie: https://www.linkedin.com/company/eugenie-ai/Soudip: https://www.linkedin.com/in/soudiprc/Your Hosts Mansi Shah - Joshua Marker ClimateStack

Show NotesIn this episode we speak with Charles Humble, climate polymath and author, about why we as software engineers should think about energy consumption of our workloads. We also talk about how nascent legislation and standards are turning externalities into real costs, forcing  innovation that gives us more control over software energy usage. Charles has encyclopedic knowledge of the history of the industry and is a former CTO himself, so his judgments are practical, realistic,  and applicable. It's easy to become cynical when you learn that our industry uses about 300TWh per year, about the consumption of Brazil. What I admired about Charles is that despite his knowledge, he was optimistic. He used a comparison to acid rain, to bring home “We can fix this problem,”  and goes on to tell us how we can do the same for tech industry as well. Listen to learn that there may actually be useful things you can do, now and in the future, to shape your system's energy usage and energy policy of the large hyperscalers. Our Guest, Charles HumbleCharles Humble is a freelance consultant, author and podcaster. A former software engineer, architect, and CTO he has worked as a senior leader and executive of both technology and content groups. He was InfoQ's editor-in-chief from 2014-2020, and was chief editor for Container Solutions from 2020-2023.He writes regularly for The New Stack and other publications, is a highly experienced content strategist, and has spoken at multiple international conferences including Devoxx, GOTO, WTF is SRE and QCon. His primary areas of interest are how we build software better, including sustainability and ethics, cloud computing, remote working, diversity and inclusion, and inspiring the next generation of developers.Charles is also a keyboard player, and half of the ambient techno band Twofish.You can find him on linkedIn at https://www.linkedin.com/in/charleshumble/Links to Charles' Mentions The BBC 4 program, “Costing The Earth” https://www.bbc.co.uk/programmes/b006r4wnWATTime, information and APIs for optimizing energy usage: https://watttime.org/Green Software Foundation: We are building a trusted ecosystem of people, standards, tooling and best practices for green software:  https://greensoftware.foundation/. They make an API that is a wrapper around WATTime and other carbon intensity data for real-time optimization: https://sci-guide.greensoftware.foundation/I/APIBased/Holly Cummins, senior principal software engineer at Quarkus for Red Hat: https://www.linkedin.com/in/holly-k-cummins/Amazon is making sustainability datasets more readily available: https://aws.amazon.com/blogs/publicsector/aws-announces-simpler-access-sustainability-data-launches-hackathon-accelerate-innovation-sustainability/Books“Building Green Software” by by Anne Currie, Sarah Hsu, Sara Bergman: Your Hosts Mansi Shah - Joshua Marker ClimateStack website - https://climatestack.podcastpage.io/

In this episode we speak with Dr Peetak Mitra, veteran of countless climate change projects, on the founding team of Excarta, core member of ClimateChange.AI, and gracious human being. He illuminates the role AI/ML can play in adapting to a warming planet, describes the ML techniques his company employs in their breakthrough tools, and gives advice for engineers looking to move into the climate space - in short, ‘just do it'. We also discuss growth in the climate sector, and he shares that despite a widespread economic slowdown, investment in climate technology continues to increase. We were delighted to have him on the show. About Dr Peetak MitraPeetak is a San Francisco-based technologist passionate about leveraging AI to combat climate change. He's on the Founding team of Excarta, a venture-backed startup building a breakthrough AI-powered weather intelligence platform for businesses. Prior to Excarta, he was a Member of Research Staff at the Xerox PARC (now SRI-PARC), where he co-led projects for AI climate forecasting funded in part by DARPA, and NASA. He has been part of Climate Change AI, organizing impactful workshops at major ML conferences including ICLR, AAAI, and NeurIPS with Turing Laureate Prof. Yoshua Bengio. He has been a featured speaker on Climate and AI at MIT, SF Climate Week, OpenAI, NSF among others. He holds a PhD in Scientific Machine Learning from the University of Massachusetts Amherst and a Bachelor's degree from BIT Mesra.https://www.linkedin.com/in/peetak/PapersThe paper Peetak mentioned: Tackling Climate Change with Machine Learning - https://dl.acm.org/doi/10.1145/3485128A milestone paper summarizing the application of ML to climate problems. Abstract: “Climate change is one of the greatest challenges facing humanity, and we, as machine learning (ML) experts, may wonder how we can help. Here we describe how ML can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by ML, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the ML community to join the global effort against climate change.”Companies and OrganizationsClimate Change AIClimate Change AI (CCAI) is an organization composed of volunteers from academia and industry who believe that tackling climate change requires concerted societal action, in which machine learning can play an impactful role. Since it was founded in June 2019 (and established as a US domestic non-profit on June 14, 2021), CCAI has led the creation of a global movement in climate change and machine learning, encompassing researchers, engineers, entrepreneurs, investors, policymakers, companies, and NGOs.9zero Climate Co-working Space.  Launched during San Francisco Climate Week 2024, 9Zero is the hub for all things climate. Starting with coworking and events, we're uniting the entire ecosystem. Startups, investors, corporations, service providers, policymakers, academics: if you're working toward a healthier, more resilient world, you belong at 9Zero. Expanding to Seattle and LA this year. Sign up at www.9ZeYour Hosts Mansi Shah - Joshua Marker ClimateStack website - https://climatestack.podcastpage.io/