As in previous quarters, we polled 100 CXOs who collectively represent over $35B in annual technology spending; our goal was, as always, to gauge the budget planning and overall sentiment of large enterprise technology buyers. To catch up on previous reports, check out what we published in March 2023, September 2023 and April 2024.

A few highlights from the survey:

• Buyer sentiment rose significantly relative to prior quarters, indicating a rebound in technology spending from buyers across industries.
• Overall budgets are trending upward with 74% this quarter vs. 55% in Q1’24 expecting an increase over the next year. With 59% of those increasing budgets seeking to invest in experimental initiatives and new tech, we see strong opportunities for startups in 2025
• Tech employment is leveling out. ​​The number of organizations looking to slow down or freeze hiring has dropped 19 percentage points from 46% in Q1 2023 to 27% in Q3 2024. Moreover, 42% of respondents expected to increase tech hiring over the next year.
• The AI wave is still building, but the future has been slower than anticipated. Today only 5.5% of identified AI use cases are in production, a sobering reality check on respondents’ Q1’24 projection that 52% of identified use cases would be in production over the next 24 months.
• That said, enthusiasm for, and continued investment in, Generative AI remains high. Our survey reveals some interesting trends on which use cases will deploy next, preferred AI model deployments, and what other tech priorities are emerging downstream in light of more AI production rollouts.

We see a lot of fresh opportunities for early-stage technology startups in these survey findings. Check out the full report here to learn more:

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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.

The post The Future of Weather Forecasting, and Why It Matters appeared first on Battery Ventures.

Our practice is diverse and focuses on critical enabling technologies for research, quality control, automation and scientific workflows, spanning instrumentation and sensor technologies, consumables (the picks and shovels enabling life-science research, analytical testing workflows and R&D) — even training and service providers.

Much like our software-investing colleagues, we prioritize what we call high-quality revenue: predictable and recurring revenue from consumables and services, over one-time product sales.

For our companies, which often have a number of revenue streams, from product sales and service contracts to consumable sales and software/data subscriptions, it can be more challenging to define value, especially on a comparative basis as the revenue mix varies across companies. But we are seeing more and more technology companies implement software-enabled products and services, as artificial intelligence becomes more accessible and as investors and management teams place a greater emphasis on revenue stability, especially in the wake of the pandemic.

Within our portfolio, we’ve always prioritized improving revenue mix as a key value creation lever, in addition to other key factors such as scale, organic growth rate, profitability and customer/market diversification to name a few.

So, we sought to quantify what we knew intuitively: that for non-software technology businesses, revenue quality mix is a meaningful indicator of value, and moreover, should serve as a key lever to help companies increase in value over time.

Based on a quantitative analysis of publicly-traded ITLST companies and our own portfolio, we created a new framework — the Revenue-Quality Podium — to help management teams, investors and industry stakeholders track value creation as companies transition to a higher share of high-quality revenue.

Download the Revenue-Quality Podium Whitepaper here to see our full analysis. 

The post Introducing the Revenue-Quality Podium: How Revenue Mix Drives Value for Industrial Tech and Life-Science Tools Companies appeared first on Battery Ventures.

When we recently attended Legalweek in New York, we saw firsthand how excited the legal community is becoming about AI, too. Nearly all the event programming centered on AI implementation, with attendees eager to learn best practices on how to benefit from AI tools. And for good reason: according to LexisNexis, 90% of law firms plan to increase their investment in generative AI over the next 5 years.

So, what comes next? Through our analysis and conversations with over 70 legal AI startups, we’ve identified three key areas where AI is poised to revolutionize legal workflows:

1. Legal Research and Review: AI can streamline discovery and search, quickly identifying relevant data and automating document reviews.
2. Contract Drafting and Negotiation: From implementing playbooks to automated hyperlinking, AI can expedite document creation and assist in negotiations.
3. Patents and Intellectual Property: We anticipate the emergence of tools that can parse complex documents and identify prior art, completely transforming the patent landscape.

It’s crucial to recognize that the legal AI market is diverse, with distinct buyer personas possessing unique needs, challenges and decision-making processes. Startups targeting large law firms should be prepared to collaborate closely with IT departments and tailor features to each client’s specific workflows. Conversely, startups serving small law firms may find success with a product-led growth strategy that minimizes onboarding requirements.

While data security, change management and the reduction of billable hours present significant challenges, the momentum behind AI adoption in the legal industry is robust. Firms and practitioners who embrace this technology will gain a competitive edge, enhancing their ability to serve clients effectively and efficiently.

Legal Research and Review

Litigation workflows are a perfect example of a process rife with inefficiencies that are neatly addressable by AI. As casework begins, legal teams upload vast collections of documents into repositories and then spend countless hours meticulously sorting through each document to uncover relevant sources and exhibits. Only then can lawyers begin constructing their arguments. This knowledge bank must be revisited multiple times throughout a case’s lifespan, which can span years, necessitating a refresh of the team’s case knowledge every few months. This triggers a repetitive cycle of legal document search-and-review each time the case is revisited.

Tedious legal workflows, such as timeline reconstruction, demand detailed oversight of massive document volumes. Huge amounts of data and proprietary knowledge languish in various document repositories, preventing teams from extracting full potential value.

We see a prime opportunity for AI-powered startups to enable legal teams to parse their data more efficiently, automatically synthesize documents, quickly refresh case knowledge and surface relevant content. By leveraging AI assistance, lawyers can focus more on case strategy and better serve their clients’ needs.

Contract Drafting and Negotiation

We’ve seen many startups target contract drafting and negotiation as a main focus, and for good reason: the technology is clearly ready for primetime in this domain. AI excels at reading contracts and detecting inconsistencies compared to historical documents and internal playbooks. It can also draft red lines and provide negotiation guidelines.

Ontra*, a Battery portfolio company in the AI-contracting space, uses AI to handle the initial 90% of the contract process, with their Ontra Legal Network serving as the human-in-the-loop to finalize contracts for private market clients. We see significant opportunities in this segment for startups to differentiate by focusing on different buyer personas (e.g., biglaw, midlaw, solo) and contract types (from bespoke, highly complex negotiations to high-volume, simple agreements).

Patents and Intellectual Property

Another compelling application of AI within the legal domain is in the realm of patents and intellectual property (IP). Patents are extremely dense, long filings with very technical language. Patents in the U.S. alone are valued at over $3T. The process of getting a patent approved can take up to two years, but the lifetime of the patent is 20 years (as long as the maintenance payments are made on time). Large corporations will protect their patent portfolio with armies of lawyers serving to defend the firm against IP lawsuits and to catch infringements.

And it makes sense why: Patent litigation costs $2.3-$4M in fees on average with patent damages costing the US market over $4.6B annually. The intricate nature of patent language, coupled with the high stakes involved in IP litigation, positions AI as a strong potential ally in this space. We anticipate the development of tools capable of parsing complex documents and identifying prior art, transforming the patent landscape and providing users with unparalleled insight and efficiency.

Conclusion and Market Opportunity

With over 1.3 million active lawyers in the U.S. alone, the potential for AI to reshape the very nature of legal practice is immense. As the industry navigates this transformative period, LLMs are elegantly addressing major pain points in legal workflows that involve vast amounts of unstructured data. Buyer appetite has surged in the past year, and we believe the legal AI reference stack is currently being established.

If you are building in this space, we’d love to talk!

The post The New Code of Law: How AI Will Revolutionize the Legal Sector appeared first on Battery Ventures.