State of the market update - players, funding, jobs and more

If you are working in the high-tech sector or are at a startup, the first half of 2023 was definitely a roller coaster. We saw venture funding dry up for many sectors; saw the collapse of the Silicon Valley Bank - the bank where many startups and venture capital funds kept their money; and observed layoffs happen in the technology giants - Google, Facebook, Apple and others. In fact, one of the VC firms, Cervin, did an analysis showing that venture funding has been at an all-time low this year, with only 19 mega deals being signed in Q1 2023 compared to 98 the previous year; funding fell by 53% for angel and seed funding and by 30% for Series A rounds. In fact, except for companies focusing on generative AI and AI adjacent technology in the high-tech sector, it has been an exceptionally difficult year financially.


How did the clean-tech sector fare? Unsurprisingly, while there were some impacts on startups and their ability to raise additional funds from venture capital firms, the sector as a whole has been pretty robust. With funding from the Inflation Reduction Act in the US, and other government sources in Europe, China and India being readily available for clean technology, startups and companies that are willing to build for the environment have found opportunities beyond what the traditional avenues. 


As GreenBiz noted, leading companies around the world have committed to $1 billion in funds that are committed to early carbon removal technologies. These include Microsoft, Apple, Shopify, Stripe in the tech sector and Autodesk, JP Morgan Chase, H&M Group and Workday in the energy, finance, and fashion sectors. As regulations on environmental sustainability and carbon removals come into play in Europe and the US in order to meet the requirements from the Paris Accords to keep the Earth’s temperature at 1.5C, more and more companies are focusing on methods that use data and machine learning to monitor their environmental footprint and support companies and organizations that can help them reduce it.


And it’s not just carbon! Water utilities have started building out their strategies for a digital world. Whether these involve simpler solutions focusing on improving their data, mapping and analysis capabilities to building out digital twins of their systems for predictive maintenance and training through virtual reality - companies in the water sector have begun expanding the envelope of what is possible.  


This in turn has led to more jobs in the sector. These jobs are beginning to look different from what were posted a couple of years ago - there’s been greater focus in each industry on looking for professionals who have great experience and understanding of the sector, be it water, agriculture or climate, and who can wield the tools of our digital age - Python, ChatGPT and deep learning.


So, for professionals in clean tech who have been thinking about acquiring digital skills - this is probably the best time to get started!




What our community are reading

Moonshots, Models, IoT and Machine Learning in Agriculture

  What do Google, Climate Corporation, early stage startups in farm robotics, and researchers trying to figure out how to feed the world sustainably have in common? They’re all grappling with one of the toughest challenges of working with natural systems - how do you work with data that is sparse, unevenly distributed and with systems that have so many connections and interactions with other systems?     Before the advent of cheap sensors that are connected to phones, easily accessible satellite data and drones that can fly over fields quickly and inexpensively -     scientists in companies and academia worked on developing plant and crop models that incorporated as many aspects of the farm and as much data as was available so that they could understand and predict what was likely to happen on the field. Understandably, the forecasts took some time to produce and as the models grew more complex, issues about how to estimate model parameters and the uncertainty associated with the resul
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Our online community space is now live!

 Our online community space is now open to anyone who has signed up for a free or paid course on our website! In addition to everyone who signed up for our cohort-based courses, we're now expanding it to all the members of our community. If you've already signed up for any of our courses, check your email for the invitation for the space. It's where we'll get together to talk about all things data science and clean technology related, discuss the latest research, network and make connections with other professionals in the sector. It's an invitation only , no bots and no trolls allowed space - so come on over! Here's where you can check out our courses and join our community !
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How much water should an email consume? Data centers and water use

  A mid-sized data center consumes around 300,000 gallons of water a day, or about as much as 1,000 U.S. households; About 20% of data centers in the United States already rely on watersheds that are under moderate to high stress from drought and other factors; Operating a data center often requires a tradeoff between water use and energy use; And in a survey of 122 data centers in the United States, only 16% or 20 utilities reported plans for managing water-related risks. As professionals working in the field, what can we do to solve this issue? One aspect is developing and using water models that can identify water risks at different scales - so that we can predict the risk to water supplies under a changing climate. A second is using machine learning to identify and optimize water use between all the stakeholders in the watershed - data centers, farmers, cities, other industries - so that biases and needs are brought out into the open and the key issues identified. A third, of cours
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