Unless you have been under a rock for a few months, you know that yet another California fire season started months early. The volume and ferocity of these fires seems to grow every year. And recent data that less than 20,000 forest firefighters are working in the state shows that we do not have enough manpower to address these infernos head-on. Rather, we need to use next-gen technology solutions to amplify our efforts in prevention, detection, and immediate response to these fires.

There are a number of companies trying to help solve this problem either through observation, prediction, and response and I wanted to highlight them here today:

Overstory: (Link) Combines AI and satellite data to support vegetation management at utilities. Vegetation encroaching upon poles & wires is a leading ignition cause.

Descartes Labs: (Link) Applies ML to data sources like satellite imagery for better forecasting, monitoring and historical analysis, enabling clients to collect data daily from public and commercial imagery providers and calibrate it for scientific analysis. This satellite imagery can help spot fires at their infancy.

Jupiter Intelligence: (Link) Uses AI and proprietary climate modeling techniques to deliver asset level predictions on peril impact: fire, wind, heat and water perils, from 1 hour ahead to 50 years. (Note: Energize is an investor here!) The company also received a Moore grant to build fire predictions for California public entities.

Zonehaven: (Link) Combines critical data and modeling capabilities to help first responders and communities understand, minimize and respond to an emergency. As fires become more commonplace around our towns and cities, we will need to have better response and evac plans.

Of course, a top long-term priority for addressing climate change is to continue to invest in new tech that is decarbonizing our power and mobility sectors. In the meantime, though, we must invest in adaptation technologies to live alongside changes in our environment.

Who else is leveraging new technology to address the fire problem?

** ADDING NAMES SHARED FROM TWITTER NETWORK **

Terrafuse.ai (Link) Actionable climate intelligence for a resilient earth

FirePerimiter.com (Link) Situational intelligence for public safety based on collaborative, real-time disaster visualization

GeoSite Inc: (Link) Geosite is a cloud-based geospatial data marketplace with integrated spatial data management and collaboration.

BuzzSolutions: (Link) Safeguarding the world’s energy infrastructure through: “Artificial Intelligence, Actionable Insights and Predictive Analytics for Power Line and Grid Inspections”

NearSpace Labs: (Link) Near Space Labs provides timely wide-scale imagery from the stratosphere at down-to-earth prices.

(Note: this post was co-authored with Kevin Stevens, Partner at Intelis Capital)

Much like its industrial counterpart, energy technology is broad, but in a different way. Energy has the usual two categories, enterprise, and consumer, but also contains “hard” technologies like solar and battery storage. This bifurcation along two segments creates varying outcomes.

Since the energy transition is an emerging trend, data points for exits are limited. The companies that have exited are the success stories of “cleantech 1.0”. The average Series A date in our dataset is Q2 2008 which is the heart of this era and the majority of exits occurred before the end of 2017. 

Capital for energy technology largely dried up between 2009-2016, so it’s unsurprising that exits became less common as fewer companies were funded. Additionally, it’s likely the outcomes in our dataset are smaller than future outcomes as we haven’t seen many exits in today’s market which values technology even more favorably than just a few years ago. Link to analysis can be found here.

CONSUMER TECHNOLOGIES

Using the median data here’s the narrative for consumer technologies in energy (note: Tesla and Sunrun are outliers):

1. A startup raises $9.6M on a ~$24M post-money valuation
2. Over the next 6 years, the company raises an additional $165M in capital prior to exit
3. At the time of exit, the startup has an approximate revenue/bookings of $108M for a cash efficiency of 65%.
4. The company exits at $972M, meaning the average multiple is 6.4x Trailing Twelve Month revenue. And the exit price is a 2.9x multiple on total invested capital, a 57.9x return on the Series A, and a 1.2x return on the pre-exit round.

ENTERPRISE TECHNOLOGIES

Using the median data here is the narrative for enterprise technologies selling to energy companies:

1. A startup raises $5.4 on a ~$16M post-money valuation
2. Over the next 5-6 years, the company raises an additional $40M in capital prior to exit
3. At the time of exit, the startup has an approximate revenue/bookings of $157M 
4. The company exits at $557M, meaning the average multiple is 4.1x Trailing Twelve Month revenue. And the exit price is a 10.5x multiple on total invested capital, a 21.6x return on the Series A, and a 1.5x return on the pre-exit round.

What are the comparative takeaways for each of these segments?

1. The data points are limited, we only have 5 true exits in the consumer data set with two big outliers – Tesla and Sunrun – and only 8 documented exits in the enterprise segment. As with the industrial analysis, the larger narrative is how fell exits there are- and that the graveyard doesn’t necessarily speak the truth.
2. In these companies, early-stage investors did extremely well. This makes sense since most of the Series A investors in this dataset financed product development in a category that was unproven. 
3. Consumer businesses require more capital to grow but also reach larger markets. As a result, they earn higher multiples and exit prices than their enterprise counterparts. 
4. At 16x trailing twelve-month revenue, software companies earn the highest multiple. hardware companies earn just over 5x. And a surprising narrative is that the “tweener” companies that merge hardware and software actually have the lowest multiple at 3x.
5. There has been a lull in exits which makes intuitive sense. From 2007-2012, clean energy-related startups received $20B annually in new funding – that number plummeted to $3B from 2013-2017. Fewer companies funded = fewer companies to exit. 

Given the average time to exit post a Series A is 5-6 years this means that the renewed funding levels in 2018 and 2019 will likely start to produce headline exits in 2023. Grab your popcorn!

Last month, Hiro Mizuno was on the Capital Allocator’s podcast with Ted Seides. Ted runs and is the host of the Capital Allocators podcast, and I am a regular listener. I encourage you to add his podcast to your library.

Hiro Mizuno is the recently departed Executive Managing Director and Chief Investment Officer of GPIF, Japan’s $1.5 trillion Government Pension Investment Fund. During the conversation with Ted, Hiro spent time covering how to align ESG metrics to investment performance. Near the end of the conversation Hiro goes on a great monologue about investor responsibility and how active investors can take on the responsibility of addressing ESG metrics through governance influence- and he specifically covers carbon-generating industries..

“I refuse to actually compete for less carbon footprint of the global portfolio. We could do that by divesting a carbon-heavy industry. But given our universal ownership approach, it doesn’t make any difference. Maybe we divest coal, tobacco…. But from our universal ownership perspective, it sounds like we are passing ownership of those problematic businesses to the people that don’t care about it. We don’t want to divest the carbon footprint of our portfolio by divesting the carbon-heavy industry as that doesn’t reduce the carbon footprint of the world.”

I really appreciate how Hiro talks about the responsibility to address problematic businesses. He wants to own the problem so that he can improve the outcome. What a powerful statement.

At Energize, most of our digital investments address the renewable and/or digital side of the energy and industrial transition. However, as shown yesterday in our petrochemical review, our goal is to be a student of the entire energy ecosystem and understand how digital technologies can provide value across different energy types. The fact is that transition fuels like natural gas and their byproducts will be around for a long time and digital technologies that advance renewables (like drones, or cybersecurity) can also be leveraged into providing a more secure and environmentally friendly transition for these carbon-based fuels.

We embrace this responsibility, are tracking our ESG metrics, and seek to advance the entire transition. Here is to a responsible, digital, energy transition. And thank you, Ted & Hiro, for a great discussion.

In the power landscape, one particular graph has been a key area of focus: the Lazard Levelized Cost of Energy chart. An Energize-equivalent summary of the chart is below:

As the chart highlights, renewable energy is now more cost effective than the ongoing operating costs of conventional generation. Wind and solar are the cheapest form of new energy generation. And batteries will be paired with these intermittent generation sources to further decelerate carbon-based generation.

Despite rooftop solar’s breakneck growth, big swaths of the US will continue to be served by a utility-scale generation source. And renewable energy’s greatest inputs (wind, solar) are generally most abundant outside of key metropolis areas – see the graph below and the wind speeds in the middle of the country!

Many power developers are placing big investments in utility scale transmission lines to move renewable power from the midwest to the eastern seaboard. Just like a startup needs an easy way to get its’ product into customer’s hands, our energy industry needs better mechanisms to deliver our renewable energy to the end-consumer. And unfortunately, digital investments in transmission are mostly nonexistent.

Great platforms, like Arcadia and Level10, are attaining incredible growth by delivering a renewable experience to renewable-inclined consumers and corporations. And firms like Amperon are making congestion and demand data more readily available. Ultimately, we are going to need structural changes to our transmission system. And we suspect that digital solutions will play a big role in that change, just as these digital solutions positively impacted the cost curves for solar & wind costs. If you are working on a digital technology that advances the transmission system, reach out.

The definition of industrial technology is very broad. And the best industrial technologies ultimately get used across a number of manufacturing, energy, real estate, healthcare, logistics and OT industries.

Due to the nebulous nature of this space, the M&A narrative is not well-covered. Last week I asked IndustrialTech twitter to highlight successful exits in our space. Special kudos to Ty Findley for sharing more history on the space. Here is a list, with some data points and takeaways:

You can click on the picture to get a bigger version. And here is access to the Google Doc that contains this file. If you have more information, you want to add, please do. Link to the Google Doc Here: Industrial Tech M&A

Using the median figure, here is the narrative;

1- An industrial technology company raises a $11.8M Series A at a $41M post-money valuation.

2- Over the course of the next 4 years the start-up raises another 2 rounds and raises nearly $50M in new capital for $66M in total capital received prior to an exit.

3- By the time of exit, 4 years after the Series A, the start-up achieves $30M in revenue/bookings for a cash efficiency of 45%.

4- The company exits at $775M, meaning the average multiple is 16.5x Trailing Twelve Month revenue. And the exit price isa 9.7x multiple on total invested capital, an 9.1x return on the Series A, and a 2.5x return on the pre-exit round.

What are some takeaways

The graveyard doesn’t speak and these are the best of the best exits for the space. So recognize that the average deal is far less performant than this outcome!

When upside exits rarely exceed $500M, entry valuations and knowing the true TAM and market dynamics is key. The most rewarding exits from this list are the ones where capital efficiency remains a focus for the management team and little excess capital is invested to try to force the acceleration of a deliberately slow purchasing cycle.

Ty Findley, Santosh Sankar, Chris Stallman, and Julian Counihan all recently contributed to an excellent post on “Why VCs Must Specialize” and I agree with the theme: smarter knowledge of your vertical dynamics drives better evaluation and price awareness so that when a start-up is going adrift, alarm bells are raised to realign for upside success. And to keep valuations within a reasonable range to allow for upside returns.

Finally, the high revenue multiples and potentially abbreviated time to exit for the most successful companies (other than OSIsoft!) shows how acquirors pay premiums for access to the industrial customer, but need to see non-trivial revenue levels (almost $30M+) before paying up.

I hope we can add many more to this list in the coming years.!