As investors with a long-term, bullish outlook on generative AI, we’re excited to back early-stage companies which are scaling and innovating, often outside the limelight, as market urgency grows and companies continue to move from AI experimentation into production. Indeed, our recent “State of Enterprise Tech Spending” report, which surveyed 100 enterprise CXOs about their tech-spending plans, identified an increasing number of generative AI deployments within enterprises, but found the majority of new applications hadn’t yet been put to use.

That makes the enterprise traction of early-stage companies in our portfolio—including two which announced new, Series B funding rounds this week—even more impressive, in our view.

Take Galileo*, which this week announced it has raised $45 million in new funding. When we first spoke with Galileo more than two years ago, the company had an early product geared towards developers building applications with language models (circa May 2022!). But we fell in love with the bold vision of solving the quality-measurement problem for AI with unstructured data, and the talented technical founders who previously led AI teams at Google and Uber.

Today, Galileo helps its fast-moving, enterprise customer base, including customers like Twilio, Chegg, HP, Procter & Gamble and other Fortune 50 enterprises move from experimentation to production with gen AI products. The company’s “evaluation intelligence” platform helps customers monitor, debug and evaluate increasingly complex generative AI systems at scale.

As we do with many of our early-stage investments, we helped Galileo get its first products out the door and also assisted the company with customer introductions, marketing and making key hires.  This is an early-stage playbook we continue to execute as we search for standout generative-AI companies that are just getting started, often by former executives and developers from big-tech players. We continue to believe in the power of innovation at the infrastructure and application layers, where we think there’s a huge opportunity for value creation

Along those lines, three years ago, we helped lead the seed round for Neuron7*, a generative AI startup that that was little more than a concept at the time. Niken Patel, the CEO, had what we considered a bold and innovative thesis: Customer service in industries with complex, high-stakes products—like medical devices, industrial equipment and telecommunications—often faces significant bottlenecks. This is because the necessary knowledge to resolve customer issues is often scattered across disparate sources, such as lengthy product manuals, troubleshooting databases and the minds of expert technicians. Neuron7 aimed to solve this by leveraging large language models (LLMs) and cutting-edge generative AI to create a single system of intelligence. This approach consolidated vast, fragmented knowledge across thousands of interactions, people and data points, offering, in our view, a new level of insight and speed to resolve customer queries.

To help accelerate Neuron7’s journey, we augmented the expertise of the company’s product team with Bill Binch, a Battery operating partner with over three decades of enterprise sales experience. Within just 12 months, Neuron7 made impressive strides, securing top tier customers, including publicly listed companies that are high-profile players in sectors like healthcare and high-tech, where customer service is mission critical.

Some customers also have since expanded their usage of Neuron7 products, purchasing additional product modules to improve the efficiency and effectiveness of their support teams. Recognizing the company’s momentum, we doubled down on our investment, leading a $10M Series A round within a year of our seed investment. Today, Neuron7 continues to grow its presence across service-desk, call-center, and field-service operations and boasts an expanding global footprint. Most notably, Neuron7’s success caught the attention of ServiceNow, which through its ServiceNow Ventures arm made a strategic investment in the company this past March, further validating the company’s transformative potential.

And today, Neuron7 announced a $44 million Series B fundraising round led by Keith Block, the former Salesforce co-CEO, now at Smith Point Capital.

Another early-stage AI company we’re excited about is Orkes*, a microservice orchestration engine whose founders previously worked at Netflix, for developers to create durable workflows across distributed systems. We seed-funded the company and then doubled down to participate in the company’s $20 million Series A fundraising round earlier this year alongside Nexus Venture Partners and Vertex Ventures. This round, in our view, is a testament to Orkes’ significant growth and the continued importance of the Conductor open-source community, upon which Orkes’ technology is based. Orkes has become an important part of the critical application infrastructure underpinning digital companies in many industries.

As AI applications and services mature and more enterprises adopt them, we’re excited to keep backing the next generation of AI startups leading this charge. These companies span sectors from databases to software development to customer service to government contracting and more. We’d love to talk to you if you’re building in this area.

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But today, exciting new technologies are emerging that promise to improve the accuracy, timeliness and cost of weather forecasting, potentially at a lower cost. These include new weather data sources; forecasting approaches grounded in machine learning; and new applications for end users. We’re already seeing startups emerge to leverage many of these new technologies.

In our view, these innovations could not come soon enough. In the U.S. alone, natural disasters costing over $1 billion have increased in frequency from 13.1 per year in the 2010s to over 20 per year over the last 5 years. We see broad consensus among climatologists and insurers that the frequency, severity and impact of these events will increase in the years to come, making next-generation forecasting technologies even more critical.

The highest value opportunities we have identified through our market research include disaster mitigation and preparation for state and local governments; mission planning for defense; route planning and ground operations for airlines; route optimization for logistics companies; energy-demand forecasting for utilities; and conditions-based marketing for the travel and leisure industry.

The science of weather—a look back

Taking a step back, it’s clear that atmospheric science has made enormous strides since the first “numerical weather prediction” (NWP) models were conceived in the 1950s. Though weather is stochastic to the core, the well-understood physical laws of hydrodynamics long have been applied to predict the weather with useful near-to-midterm accuracy. Instantiating these laws in predictive models requires simulating the earth’s atmosphere as a three-dimensional grid of cells exchanging energy and moisture from observed starting conditions. This requires some of the most complex and demanding computational workloads in existence.

Consequently, the forecasts we all rely upon today are generally provided by large national weather services—most importantly, the National Oceanic and Atmospheric Administration (NOAA) and National Weather Service (NWS) in North America and the European Centre for Medium-Range Weather Forecasts (ECMWF) in Europe, supplemented by several country-specific models such as the Unified Model (UM) in the U.K. and the ICON Model in Germany.

Overview of NWP from Google Research.

But there are new possibilities to innovate on this foundation, including through the same forces spurring innovation in so many other industries: novel sources of data complementing large data sets for training, declining costs of computation and machine-learning (ML) techniques capable of discerning patterns beyond human inference.

In the case of weather, the theoretical promise of ML-weather prediction is to “abstract away” a considerable portion of the traditional, physics-based modeling in order to generate accurate predictions with far lower computational demand—i.e., to do it both faster and cheaper. Think of this as analogous to the way LLMs can generate highly useful text output without (discernibly) learning the rules of language semantics and syntax.

A figure from Forecasting Global Weather with Graph Neural Networks (Ryan Keisler, PhD) showing a Coder → Processor → Decoder approach for weather prediction using neural networks. 

Early indications for new, ML-based forecasting approaches are encouraging. DeepMind reports its GraphCast model produces more accurate predictions than the ECMWF’s leading HRES model on over 90% of 1,380 test variables and forecast lead times, and Nvidia claims its FourCastNet model can compute forecasts orders of magnitude faster (and thus cheaper) than ECMWF models with comparable or superior accuracy.

Newer vendors like Atmo, Zeus AI, and WindBorne Systems similarly claim their ML models offer greater accuracy and granularity than incumbent NWP models. Other companies are providing ML weather forecasting that is fine-tuned to serve specific use cases, such as Jua (for energy trading) and Excarta (for insurance and solar).

While there is near-consensus among industry and academic experts that deep-learning approaches will play a role in improving forecasts, questions remain. Explainability, the unclear incorporation of physical constraints, the potential for hallucination and the ability to account for rare but extreme weather events are all hurdles that ML models still have to clear.

Most importantly, ML models have not yet been rigorously tested over a meaningful time period across mission-critical use cases. The prevailing view among experts we surveyed is that the likely end-state will be hybrid approaches that combine deep learning with hard-earned insights of physics-based NWP.

“The progress of the pure machine-learning models has been quite astonishing during [1H 2023] and many scientists in the field have been taken by surprise regarding the quality of predictions.”

– Peter Deuben, head of Earth system modeling at ECMWF

“The results of [ML-based forecasting systems] are amazing.”

– Hendrik Tolman, National Weather Services senior advisor for modeling

DeepMind’s Graphcast model shows meaningfully less error than HRES, the leading ECMWF model. 

As ML models grow in sophistication, a key challenge will be data assimilation—i.e., the incorporation of novel data sources that are structured differently in time and granularity.  Companies such as Spire, DTN and Tomorrow.io collect advanced weather data through weather ground stations, satellites (for imagery, radio occultation, etc.), sensors and space-based radar, which could potentially expand coverage of the atmosphere to large portions of the world that are poorly surveyed today.

WindBorne, for example, collects atmospheric data from its weather balloons, which can navigate over oceans and rainforests to obtain hard-to-reach data. This type of new data holds the promise of improving the accuracy of models, though the ability to fully incorporate novel data appears to be still in its infancy.

Overview of WindBorne’s innovative global sounding balloons (GSBs). 

Beyond forecasts and data, another key area for value creation in weather is business applications that translate weather forecasts into actionable insights, such as planning the most efficient flight or trucking route, deciding whether or not to cancel an outdoor concert due to thunderstorms or launching a weather-driven ad campaign.

Consumer-facing weather applications represent another intriguing opportunity: Weather forecasts are checked over 300 billion times per year in the U.S., and close to nine out of 10 adult Americans obtain weather forecasts three or more times each day.

Overall, we are excited about the promise of ML in weather forecasting, powered by new sources of high-resolution data and translated into useful business and consumer applications.

Not only could these improvements create significant fundamental value for thousands of public-sector, military and commercial customers, but they could potentially unlock new use cases and expand coverage to constituencies underserved by governmental incumbents.

If you are building or investing in ML for weather forecasting, we would love to hear from you.

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