Year: 2020

“The Mandalorian” just wrapped up its second season on Disney+, with an action-packed and surprise-filled finale.

In many ways, it feels like a seamless continuation of the first season’s storylines, with the titular bounty hunter searching for a Jedi who can take responsibility for the alien moppet known to the internet as Baby Yoda, while the pair is pursued by the sinister Moff Gideon.

But as we explain on the latest episode of the Original Content podcast, where the first season of “The Mandalorian” felt accessible to anyone, regardless of their level of Star Wars fandom, season two deepens its ties to the rest of the fictional universe.

That includes bringing in live action versions of characters from the animated “Clone Wars” series, as well as setting up the many other Star Wars shows that are in the works for Disney+. This approach prompted very different responses from your podcast hosts — Darrell was delighted since he understood all the Ester Eggs, Jordan was exhausted trying to keep up and Anthony was happy to let many of the references go over his head.

At least the show’s other virtues remain intact, with enjoyably grungy and tactile space opera settings, spectacular big budget battles and an adorable baby Jedi.

In addition to reviewing “The Mandalorian,” we also discuss HBO Max’s arrival on Roku (which somehow prompts Anthony to explain his disappointment in the new Christopher Nolan movie “Tenet”), and Darrell and Jordan offer their latest thoughts on “The Bachelorette.”

You can listen to our review in the player below, subscribe using Apple Podcasts or find us in your podcast player of choice. If you like the show, please let us know by leaving a review on Apple. You can also follow us on Twitter or send us feedback directly. (Or suggest shows and movies for us to review!)

And if you’d like to skip ahead, here’s how the episode breaks down:
0:00 Intro
4:14 HBO Max/”Tenet” discussion
13:35 “The Bachelorette” discussion
28:42 “The Mandalorian” Season 2 review
50:40 “The Mandalorian” spoiler discussion

“Are you okay?”

I don’t have a good answer to the question. Knowing full well that I’m talking back to an algorithm — even one asking the same question of everyone with a different band mad-libbed in — doesn’t soften the blow. Am I? Are we? Is anyone, really?

In this case, it’s referring to Waxahatchee. I mean, yeah, I totally listened to a lot of Waxahatchee this year. Waxahatchee is good. Saint Cloud was one of my favorite albums of the year. Katie Crutchfield’s music doesn’t exist in the Elliott Smith, Leonard Cohen bin for me. It’s not time to send up the signal flares when you see the band all over my Spotify social feed.

The Spotify roasting AI that’s been making the rounds this week is a fun exercise in music snobbery. It also may be brushing against some larger truth here. Something I think we all considered at least in passing this year when Spotify offered its annual “Wrapped” year in review.

What’s the soundtrack to the worst year, ever? What do we listen to while the world burns? In 2009, a former CNN intern stumbled across a video tape in the archives labeled with the title, “Turner Doomsday Video.” The minute-long video features a band playing, “Nearer My God To Thee,” believed to be the final song played by the band on the Titanic. It carried the explicit instructions, “HFR [Hold for Release] till end of the world confirmed.”

Barring any sort of last-minute surprise, it seems likely we’ll make it through 2020 shy of a full-on apocalypse (in spite of, perhaps, the best efforts of some). But for me, Spotify’s year in review was a testament to hell year, just as my Apple Watch exercise bars saw a zeroing out in late-March and April, as the pandemic bore down on my home of Queens, New York and I dealt with some personal health issues.

What was pitched as a celebratory aggregation of my listening habits over the previous 12 months exited the machine as a testament to the long stretches of time where engaging with music felt like an impossibility. Ambient music and post-rock got me listening again when lyrics seemed like too much to process. And I’m sure I’m not alone in having listened to some comfort tracks with an alarming frequency.

Looking back is a useful reminder of the role music played in what undoubtedly qualifies as the worst year to date for many. It would be an overstatement to suggest that music saved my life in 2020, but it certainly cushioned the blow of one too many emotional gut punches.

“Music can lift us out of depression or move us to tears – it is a remedy, a tonic, orange juice for the ear,” the late-neurologist, Oliver Sacks wrote. “But for many of my neurological patients, music is even more – it can provide access, even when no medication can, to movement, to speech, to life. For them, music is not a luxury, but a necessity.”

Louis Armstrong put it even more succinctly: “music is life itself.”

It’s a cruel irony that, in a year when music has meant so much to so many, most musicians have struggled to make ends meet. The musical field certainly isn’t unique in that respect this year, but their struggles have been pronounced in an era when streaming revenues offer fractions of cents what musicians make in record sales, and touring has become the most important revenue stream for all but the biggest names. For the past 10 months, that all but dried up.

“The pandemic utterly decimated the live-music industry,” Wilco frontman Jeff Tweedy noted in a recent interview. “There’s been almost an entire year now of absolutely zero revenue.”

In May, a survey from the Musician’s Union noted that 19% of musicians said they might end up giving up their careers due to the impact of COVID-19. Seven months later, one wonders whether that figure might have been optimistic.

Tweedy adds, “There will be places to play. But the landscape won’t ever look the same. I imagine that a lot of the more intimate music venues will be gone, just like a lot of small businesses and restaurants.”

Bandcamp has been a beacon for many. The service’s “Bandcamp Fridays,” which waive its revenue cut, have raised $40 million to date. The site has promised to continue offering the feature at least through May of next year.

This year’s struggles have served to highlight concerns over streaming royalties. Spotify has understandably been the focal point for this conversation, all while the company has spent hundreds of millions to bolster its podcast programming. CEO Daniel Ek didn’t do himself any favors in July when he noted, “Some artists that used to do well in the past may not do well in this future landscape, where you can’t record music once every three to four years and think that’s going to be enough.”

In October, Justice at Spotify rep (and Galaxie 500 member) Damon Kurkowski told me “[R]esponse from certain corners of the industry has been as cold as we expected: ‘You’re just musicians and don’t understand business,’ is the basic gist of it. To which I would say: The problem we are calling attention to is precisely that musicians have been left out of the conversation! We always come last in payment and in consultation — even though our work is what the streaming business is built on.”

The struggle to survive on music is nothing new, of course. Jazz genius Thelonious Monk famously had a benefactor in Baroness Pannonica de Koenigswarter. But just because we’ve failed musicians in the past doesn’t mean we can’t and shouldn’t do better.

Am I okay? I’m still not sure, but listening to music seems to help.

Chinese authorities investigate an e-commerce giant, Google may be tightening its grip on research and VCs weigh in on the year’s biggest surprises. This is your (briefer than usual) Daily Crunch for December 24, 2020.

The big story: Alibaba faces antitrust probe

China’s State Administration for Market Regulation said that it’s investigating the e-commerce giant over a policy that forces merchants to sell exclusively with Alibaba and skip rival platforms JD.com and Pinduoduo.

“Alibaba will actively cooperate with the regulators on the investigation,” the company said in a statement. “Company business operations remain normal.”

Meanwhile, Chinese authorities have already called off the initial public offering of Alibaba affiliate Ant Group, and the company has now received another “meeting notice” from regulators.

Holiday grab bag

Google reportedly tightens grip on research into ‘sensitive topics’ — Reuters, citing researchers at the company and internal documents, reports that Google has implemented new controls in the last year, including an extra round of inspection for papers on certain topics.

Five VCs discuss what surprised them the most in 2020 — The latest episode of Equity reflects on a year that no one could have predicted.

Gift Guide: Last-minute subscriptions to keep the gifts going all year — They’re easy to order at the very last minute, easy to give from afar and they’ll spread the gifting fun out over weeks and months.

Advice and analysis from Extra Crunch

The built environment will be one of tech’s next big platforms — An in-depth look at Sidewalk Labs’ abandoned Toronto waterfront project.

US seed-stage investing flourished during pandemic — According to a TechCrunch analysis of PitchBook data and a survey of venture capitalists, a few trends became clear.

Use Git data to optimize your developers’ annual reviews — Three metrics can help you understand true performance quality.

(Extra Crunch is our membership program, which aims to democratize information about startups. You can sign up here.)

The Daily Crunch is TechCrunch’s roundup of our biggest and most important stories. If you’d like to get this delivered to your inbox every day at around 3pm Pacific, you can subscribe here.

This was going to be the year of 5G. It was going to be the year the next-generation wireless technology helped reverse some troubling macro trends for the industry — or at the very least helped stem the bleeding some.

But the best laid plans, and all that. With about a week left in the year, I think it’s pretty safe to say that 2020 didn’t wind up the way the vast majority of us had hoped. It’s a list that certainly includes the lion’s share of smartphone makers. Look no further than a recent report published by Gartner to answer the question of just how bad 2020 was for smartphone sales.

It was so bad that a 5.7% global decline year-over-year for the third quarter constituted good news. In a normal year, that wouldn’t qualify as good news for too many industries outside of wax cylinder and asbestos sales. But there are few standards by which 2020 was a normal year, so now we’ll take some respite in the fact that a 5.7% drop was a considerably less pronounced drop than the ~20% we saw in Qs 1 and 2.

Some context before we get into the whys here. A thing that’s important to note up front is that mobile wasn’t one of those industries where everything was smooth sailing before everything got upended by a pandemic. In 2019 I wrote a not insignificant number of stories with headlines like “Smartphone sales expected to drop 2.5% globally this year” and “Smartphone sales declined again in Q2, surprising no one.” And even those stories were a continuation of trends from a year prior.

The reasons for the decline should be pretty familiar by now. For one thing, premium handsets got expensive, routinely topping out over $1,000. Related to that, phones have gotten good. Good news for consumers doesn’t necessarily translate to good news for manufacturers here, as upgrade cycles have slowed significantly from their traditional every two years (also an artifact of the carrier subscription model). Couple that with economic hardships, and you’ve got a recipe for slowed growth.

This March, I wrote an article titled “5G devices were less than 1% of US smartphone purchases in 2019.” There was, perhaps, a certain level of cognitive dissonance there, after many years of 5G hype. There are myriad factors at play here. First, there just weren’t a ton of different 5G models available in the States by year’s end. Second, network rollout was far from complete. And, of course, there was no 5G iPhone.

I concluded that piece by noting:

Of course, it remains to be seen how COVID-19 will impact sales. It seems safe to assume that, like every aspect of our lives, there will be a notable impact on the number of people buying expensive smartphones. Certainly things like smartphone purchases tend to lessen in importance in the face of something like a global pandemic.

In hindsight, the answer is “a lot.” I’ll be the first to admit that when I wrote those words on March 12, I had absolutely no notion of how bad it was about to get and how long it would last (hello month nine of lockdown). In the earliest days, the big issue globally was on the supply side. Asia (China specifically) was the first place to get hit and the epicenter of manufacturing buckled accordingly. Both China and its manufacturing were remarkably fast to get back online.

In the intervening months, demand has taken a massive hit. Once again, there are a number of reasons for this. For starters, people aren’t leaving their homes as much — and for that reason, the money they’ve allotted to electronics purchases has gone toward things like PCs, as they’ve shifted to a remote work set-up. The other big issue here is simple economics. So many people are out of work and so much has become uncertain that smartphones have once again been elevated to a kind of luxury status.

There are, however, reasons to be hopeful. It seems likely that 5G will eventually help right things — though it’s hard to say when. Likely much of that depends on how soon we’re able to return to “normal” in 2021. But for now, there’s some positive to be seen in early iPhone sales. After Apple went all in on 5G this year, the new handset (perhaps unsurprisingly) topped sales for all other 5G handsets for the month of October, according to analysts.

The company will offer a more complete picture (including the ever-important holiday sales) as part of its earnings report next month. For now, at least, it seems that thing are finally heading in the right direction. That trend will, hopefully, continue as the new year sees a number of Android launches.

Perhaps 2021 will be the year of 5G — because 2020 sure wasn’t.

SpaceX is set to significantly ramp up its Starship development program in the new year, in more ways than one. SpaceX CEO and founder Elon Musk noted on Twitter on Thursday that the company will seek to make use of both of its two launch pads at its development facility in Boca Chica, Texas with prototype rockets set up on each, and that it will begin flight testing its Super Heavy booster (starting with low-altitude ‘hops’) in as few as “a few months” from now.

Recently, SpaceX has set up its SN9 prototype of Starship (the ninth in the current series) at Pad B at its Texas testing facility, which is on the Gulf of Mexico. SN9 will be next to undergo active testing, after SpaceX successfully flew its predecessor SN8 to an altitude of around 40,000 feet, and then executed a crucial belly flop maneuver that will be used to help control the powered landing of the production version. SN8 was destroyed when it touched down harder than expected, but SpaceX still achieved all its testing goals with the flight – and more.

SN9 will now undergo ground tests before hopefully doing its own flight test later on. That’ll provide the team with even more valuable data to carry on to further tests – with the ultimate goal of eventually achieving orbit with a Starship prototype vehicle. Musk’s tweet that two prototypes will be stood up next to each other on both Pad A and Pad B at the Boca Chica site could indicate the pace of these test flights might speed up, to match the fast clip at which SpaceX is constructing new rocket iterations.

Meanwhile, news that Super Heavy could be undergoing testing soon is also reason to get excited about 2021 for SpaceX and Starship. Super Heavy is the booster that SpaceX will eventually use to fly Starship for orbital launches, and to eventually help propel it to deep space – for destinations including Mars. Super Heavy will be around 240-feet tall, and will include 28 Raptor engines to provide it with the lift capacity needed to break Earth’s gravity well when it’s stacked with a Starship loaded down with cargo.

The end of the year is looming and with it one of your most important tasks as a manager. Summarizing the performance of 10, 20 or 50 developers over the past 12 months, offering personalized advice and having the facts to back it up — is no small task.

We believe that the only unbiased, accurate and insightful way to understand how your developers are working, progressing and — last but definitely not least — how they’re feeling, is with data. Data can provide more objective insights into employee activity than could ever be gathered by a human.

Consider this: Over two-thirds of employees say they would put more effort into their work if they felt more appreciated, and 90% want a manager who’s fair to all employees.

Let’s be honest. It’s hard to judge all of your employees fairly if you’re (1) unable to work physically side-by-side with them, meaning you’ll inevitably have more contact with the some over others (e.g., those you’re more friendly with); and (2) you’re relying on manual trackers to keep on top of everyone’s work, which can get lost and take a lot of effort to process and analyze; (3) you expect engineers to self-report their progress, which is far from objective.

It’s also unlikely, especially with the quieter ones, that on top of all that you’ll have identified areas for them to expand their talents by upskilling or reskilling. But it’s that kind of personal attention that will make employees feel appreciated and able to progress professionally with you. Absent that, they’re likely to take the next best job opportunity that shows up.

So here’s a run down of why you need data to set up a fair annual review process; if not this year, then you can kick-start it for 2021.

1. Use data to set next year’s goals

The best way to track your developers’ progress automatically is by using Git Analytics tools, which track the performance of individuals by aggregating historical Git data and then feeding that information back to managers in minute detail.

This data will clearly show you if one of your engineers is over capacity or underworked and the types of projects they excel in. If you’re assessing an engineering manager and the team members they’re responsible for have been taking longer to push their code to the shared repository, causing a backlog of tasks, it may mean that they’re not delegating tasks properly. An appropriate goal here would be to track and divide their team’s responsibilities more efficiently, which can be tracked using the same metrics, or cross-training members of other teams to assist with their tasks.

Another example is that of an engineer who is dipping their toe into multiple projects. Indicators of where they’ve performed best include churn (we’ll get to that later), coworkers repeatedly asking that same employee to assist them in new tasks and of course positive feedback for senior staff, which can easily be integrated into Git analytics tools. These are clear signs that next year, your engineer could be maximizing their talents in these alternative areas, and you could diversify their tasks accordingly.

Once you know what targets to set, you can use analytics tools to create automatic targets for each engineer. That means that after you’ve set it up, it will be updated regularly on the engineer’s progress using indicators directly from the code repository. It won’t need time-consuming input from either you or your employee, allowing you both to focus on more important tasks. As a manager you’ll receive full reports once the deadline of the task is reached and get notified whenever metrics start dropping or the goal has been met.

This is important — you’ll be able to keep on top of those goals yourself, without having to delegate that responsibility or depend on self-reporting by the engineer. It will keep employee monitoring honest and transparent.

2. Three Git metrics can help you understand true performance quality

The easiest way for managers to “conclude” how an engineer has performed is by looking at superficial output: the number of completed pull requests submitted per week, the number of commits per day, etc. Especially for nontechnical managers, this is a grave but common error. When something is done, it doesn’t mean it’s been done well or that it is even productive or usable.

Instead, look at these data points to determine the actual quality of your engineer’s work:

1. Churn is your number-one red flag, telling you how many times someone has modified their code in the first 21 days after it has been checked in. The more churn, the less of an engineer’s code is actually productive, with good longevity. Churn is a natural and healthy part of the software development process, but we’ve identified that any churn level above the normal 15%-30% indicates that an engineer is struggling with assignments.
   
   

As the United States entered its first wave of COVID-19 lockdowns, there were wide expectations in startup land that a reckoning had arrived. But the expected comeuppance of high-burn, high-growth startups fueled by cheap capital provided by venture capitalists raising ever-larger funds, failed to arrive.

Instead, the very opposite came to pass.

Layoffs happened swiftly and aggressively during the early months of the pandemic era. But by the middle of Q2, venture activity had warmed and third quarter dealmaking felt swift and competitive, with some investors describing it as the hottest summer in recent years.

But somewhat lost amongst the splashy megarounds and high-interest IPOs that can dominate the news cycle were seed-stage startups. The raw little companies that represent the grist that will shape itself into the next set of giants.

TechCrunch explored what happened in seed investing to uncover what was missed amidst the storm and fury of late-stage startup activity. According to a TechCrunch analysis of PitchBook data and a survey of venture capitalists, a few trends became clear.

First, the pattern of rising seed-check sizes seen in prior years continued despite the tumultuous business climate. Second, more expensive and larger seed deals were not only caused by excessive capital present in the private markets. Instead, COVID-19 shook up which startups were considered attractive by private investors. And the changeup did not necessarily raise their number.

Let’s dig into the data and see what it can teach us about this wild year. Then we’ll hear from Eniac Ventures’ Nihal Mehta, Freestyle’s Jenny Lefcourt, Pear VC’s Mar Hershenson and Contrary Capital’s Eric Tarczynski about what they saw in 2020 while writing a chunk of the checks that our data encompasses.

The American seed market in 2020

If you didn’t think much about seed in 2020, you’re not alone. Late, huge rounds consumed most of the media’s oxygen, leaving smaller startups to compete for scraps of attention. There was so much late-stage activity — around 90 $100 million or larger rounds in Q3, for example — it was difficult for smaller investments to command attention.

But despite living in the background, the dollars invested into seed-stage startups in the United States had an up-and-down year that was fascinating:

Image Credits: PitchBook

Seed dollar volume fell as Q1 progressed, reaching a 2020 nadir in April, the start of Q2. But as May arrived, the pace at which investors put money into seed-stage startups accelerated, recovering to January levels — which is to say, pre-pandemic — by June. The COVID dip, for seed, then, was a short-term affair.

As the United States entered its first wave of COVID-19 lockdowns, there were wide expectations in startup land that a reckoning had arrived. But the expected comeuppance of high-burn, high-growth startups fueled by cheap capital provided by venture capitalists raising ever-larger funds, failed to arrive.

Instead, the very opposite came to pass.

Layoffs happened swiftly and aggressively during the early months of the pandemic era. But by the middle of Q2, venture activity had warmed and third quarter dealmaking felt swift and competitive, with some investors describing it as the hottest summer in recent years.

But somewhat lost amongst the splashy megarounds and high-interest IPOs that can dominate the news cycle were seed-stage startups. The raw little companies that represent the grist that will shape itself into the next set of giants.

TechCrunch explored what happened in seed investing to uncover what was missed amidst the storm and fury of late-stage startup activity. According to a TechCrunch analysis of PitchBook data and a survey of venture capitalists, a few trends became clear.

First, the pattern of rising seed-check sizes seen in prior years continued despite the tumultuous business climate. Second, more expensive and larger seed deals were not only caused by excessive capital present in the private markets. Instead, COVID-19 shook up which startups were considered attractive by private investors. And the changeup did not necessarily raise their number.

Let’s dig into the data and see what it can teach us about this wild year. Then we’ll hear from Eniac Ventures’ Nihal Mehta, Freestyle’s Jenny Lefcourt, Pear VC’s Mar Hershenson and Contrary Capital’s Eric Tarczynski about what they saw in 2020 while writing a chunk of the checks that our data encompasses.

The American seed market in 2020

If you didn’t think much about seed in 2020, you’re not alone. Late, huge rounds consumed most of the media’s oxygen, leaving smaller startups to compete for scraps of attention. There was so much late-stage activity — around 90 $100 million or larger rounds in Q3, for example — it was difficult for smaller investments to command attention.

But despite living in the background, the dollars invested into seed-stage startups in the United States had an up-and-down year that was fascinating:

Image Credits: PitchBook

Seed dollar volume fell as Q1 progressed, reaching a 2020 nadir in April, the start of Q2. But as May arrived, the pace at which investors put money into seed-stage startups accelerated, recovering to January levels — which is to say, pre-pandemic — by June. The COVID dip, for seed, then, was a short-term affair.

If the portfolio of a corporate venture capital firm can be taken as a signal for the strategic priorities of their parent companies, then National Grid has high hopes for automation as the future of the utility industry.

The heavy emphasis on automation and machine learning from one of the nation’s largest privately held utilities with a customer base numbering around 20 million people is significant. And a sign of where the industry could be going.

Since its launch, National Grid’s venture firm, National Grid Partners, has invested in 16 startups that featured machine learning at the core of their pitch. Most recently, the company backed AI Dash, which uses machine learning algorithm to analyze satellite images and infer the encroachment of vegetation on National Grid power lines to avoid outages.

Another recent investment, Aperio uses data from sensors monitoring critical infrastructure to predice loss of data quality from degradation or cyberattacks.

Indeed, of the $175 million in investments the firm has made roughly $135 million has been committed to companies leveraging machine learning for their services.

“AI will be critical for the energy industry to achieve aggressive decarbonization and decentralization goals,” Lisa Lambert, the chief technology and innovation officer at National Grid and the founder and president of National Grid Partners.

National Grid started the year off slowly because of the COVID-19 epidemic, but the pace of its investments picked up and the company is on track to hit its investment targets for the year, Lambert said.

Modernization is critical for an industry that still mostly runs on spreadsheets and collective knowledge that’s locked in an aging employee base, with no contingency plans in the event of retirement, Lambert said. It’s that situation that’s compelling National Grid and other utilities to automate more of their business.

“Most companies in the utility sector are trying to automate now for efficiency reasons and cost reasons. Today, most companies have everything written down in manuals; as an industry, we basically still run our networks off spreadsheets, and the skills and experience of the people who run the networks. So we’ve got serious issues if those people retire. Automating [and] digitizing is top of mind for all the utilities we’ve talked to in the Next Grid Alliance.

To date, a lot of the automation work that’s been done has been around basic automation of business processes. But there are new capabilities on the horizon that will push the automation of different activities up the value chain, Lambert said.

“ ML is the next level — predictive maintenance of your assets, delivering for the customer. Uniphore, for example: you’re learning from every interaction you have with your customer, incorporating that into the algorithm, and the next time you meet a customer, you’re going to do better. So that’s the next generation,” Lambert said. “Once everything is digital, you’re learning from those engagements – whether engaging an asset or a human being.”

Lambert sees another source of demand for new machine learning tech in the need for utilities to rapidly decarbonize. The move away from fossil fuels will necessitate entirely new ways of operating and managing a power grid. One where humans are less likely to be in the loop.

“In the next five years, utilities have to get automation and analytics right if they’re going to have any chance at a net-zero world – you’re going to need to run those assets differently,” said Lambert. “Windmills and solar panels are not [part of] traditional distribution networks. A lot of traditional engineers probably don’t think about the need to innovate, because they’re building out the engineering technology that was relevant when assets were built decades ago – whereas all these renewable assets have been built in the era of OT/IT.”

If the portfolio of a corporate venture capital firm can be taken as a signal for the strategic priorities of their parent companies, then National Grid has high hopes for automation as the future of the utility industry.

The heavy emphasis on automation and machine learning from one of the nation’s largest privately held utilities with a customer base numbering around 20 million people is significant. And a sign of where the industry could be going.

Since its launch, National Grid’s venture firm, National Grid Partners, has invested in 16 startups that featured machine learning at the core of their pitch. Most recently, the company backed AI Dash, which uses machine learning algorithm to analyze satellite images and infer the encroachment of vegetation on National Grid power lines to avoid outages.

Another recent investment, Aperio uses data from sensors monitoring critical infrastructure to predice loss of data quality from degradation or cyberattacks.

Indeed, of the $175 million in investments the firm has made roughly $135 million has been committed to companies leveraging machine learning for their services.

“AI will be critical for the energy industry to achieve aggressive decarbonization and decentralization goals,” Lisa Lambert, the chief technology and innovation officer at National Grid and the founder and president of National Grid Partners.

National Grid started the year off slowly because of the COVID-19 epidemic, but the pace of its investments picked up and the company is on track to hit its investment targets for the year, Lambert said.

Modernization is critical for an industry that still mostly runs on spreadsheets and collective knowledge that’s locked in an aging employee base, with no contingency plans in the event of retirement, Lambert said. It’s that situation that’s compelling National Grid and other utilities to automate more of their business.

“Most companies in the utility sector are trying to automate now for efficiency reasons and cost reasons. Today, most companies have everything written down in manuals; as an industry, we basically still run our networks off spreadsheets, and the skills and experience of the people who run the networks. So we’ve got serious issues if those people retire. Automating [and] digitizing is top of mind for all the utilities we’ve talked to in the Next Grid Alliance.

To date, a lot of the automation work that’s been done has been around basic automation of business processes. But there are new capabilities on the horizon that will push the automation of different activities up the value chain, Lambert said.

“ ML is the next level — predictive maintenance of your assets, delivering for the customer. Uniphore, for example: you’re learning from every interaction you have with your customer, incorporating that into the algorithm, and the next time you meet a customer, you’re going to do better. So that’s the next generation,” Lambert said. “Once everything is digital, you’re learning from those engagements – whether engaging an asset or a human being.”

Lambert sees another source of demand for new machine learning tech in the need for utilities to rapidly decarbonize. The move away from fossil fuels will necessitate entirely new ways of operating and managing a power grid. One where humans are less likely to be in the loop.

“In the next five years, utilities have to get automation and analytics right if they’re going to have any chance at a net-zero world – you’re going to need to run those assets differently,” said Lambert. “Windmills and solar panels are not [part of] traditional distribution networks. A lot of traditional engineers probably don’t think about the need to innovate, because they’re building out the engineering technology that was relevant when assets were built decades ago – whereas all these renewable assets have been built in the era of OT/IT.”