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GigaOm CEO Ben Book describes how larger companies are embracing the digital transformation and deploying new technologies within their enterprise, as well as several developing trends that are unlocking substantial opportunities for investors.
May 31, 2022– By Simon Erickson
The digital transformation is certainly underway.
Technology is changing and companies of all sizes are embracing it. Small businesses are deploying lightweight applications to grow revenue and gain share. Larger enterprises are replacing legacy systems with more efficient solutions from cloud-native vendors. And even massive and notoriously slow-moving industries like health care, banking, and insurance are keen to innovate more quickly and become more efficient in today’s digital world.
All of this is great news for investors. More efficient companies are more profitable and are able to hand out those rising profits to their shareholders.
But where do the greatest opportunities in the digital transformation lie? Should investors look to the smaller, pure-play companies who are enabling bigger-trend movements such as data-centric AI or DevOps? Or is there a greater opportunity to invest in larger companies who are capitalizing on these movements to re-align their existing businesses? And are all of the things taking place in the world — from the lingering impacts of COVID, supply chain disruptions, or fears of a global recession — having any impact on how these exciting new technologies are actually getting deployed?
To help us answer those questions, we’ve brought in an expert. Ben Book is the co-founder and CEO of GigaOm, who is helping IT decision makers tackle their most complex technical challenges. GigaOm is bringing the executives of progressive companies up-to-speed about emerging technologies and then helping to implement them across their organizations.
In an exclusive interview with 7investing CEO Simon Erickson, Ben describes what’s really driving the digital transformation. He focuses on the fundamental changes taking place in artificial intelligence and how organizations are building entire programs to benefit from them. He describes the DevOps movement and explains how data scientists are monitoring new data formats to make better decisions. He also shares what the process typically looks like for a large enterprise to shift from its legacy vendors and adopt a cloud-based solution.
In the second segment, Ben chats a bit about the progress being made in health care. He explains that while hospital budgets are constrained, there is a ton of interest in using medical information to improve patient outcomes. He also discusses companies who have done a great job of embracing digital innovation, and points to Disney (NYSE: DIS) as one example.
In the final segment, Simon spots Ben up with the ‘lightning round’ and asks for his thoughts on quantum computing, the Metaverse, and DNA used for storage.
Publicly-traded companies mentioned in this interview include Alphabet, Amazon, Apple, C3.AI, Cloudera, Disney, GitLab, Johnson & Johnson, Meta Platforms, Microsoft, The New York Times, Oracle, SAP, Snowflake, Splunk, and Teradata. 7investing’s advisors or its guests may have positions in the companies mentioned.
0:00 – Introduction: The digital transformation is underway
1:07 – Changes underway in artificial intelligence
14:39 – The process enterprises go through when deciding to deploy new technologies
17:55 – Using technology in banking, insurance, and health care
24:13 – Are macroeconomic challenges slowing down the pace of innovation?
28:33 – How small businesses respond to technology changes
31:07 – “The Lightning Round” – Quantum computing, the Metaverse, and DNA storage
Simon Erickson 00:00
Hello, everyone, and welcome to this edition of our 7investing podcast where it’s our mission to empower you to invest in your future. I’m 7investing founder and CEO, Simon Erickson, we’ve been chatting a lot about the digital transformation in recent years, a lot of exciting new technologies that are out there. And it’s getting a lot of great media coverage. But even more important than that is actually how these technologies are being used and deployed by actual corporations. We don’t want any investors to fall in the landmines to ride the hype cycle up, we want, we want to be seen where the rubber hits the road. And these are actually being applied. On that note, I’m really excited to welcome my guest today, Ben book is the co founder and CEO of Diego out in Santa Barbara, California. This company is actually engaging with large enterprises and executives and helping them deploy new technologies for their innovations and their own organizations. Ben we’ve had you on the 7investing podcast before. I’m really excited to welcome you back here this morning.
Ben Book 00:54
Thanks for having us back. Simon, good to see you. And looking forward to our chat. Last time we were talking about digital transformation and being in the third inning. And today we’ll be talking about AI which is like the first out of the first inning.
Simon Erickson 01:07
It’s super early, right, we are so familiar with artificial intelligence and machine learning and how this has evolved in a couple of years. But maybe to set the scene on this just a little bit. It’s changing. It’s not the same AI today than it was just a couple of years ago. And what I mean from that is, you know, there used to be kind of these competitions, ImageNet and others where you’d feed a whole bunch of unstructured data and let the algorithms do the things right. Here’s a zillion pictures figured out which ones are cats look for the ears, look for the whiskers. Now 99.8% confident this is a picture of a kitty cat. Unstructured data, though, is kind of hard for commercial applications that need to be sure about things, if you’re dealing with stuff that’s really regulated, or safety is a real concern, whether it’s self driving cars, whether it’s hospitals, I mean, you’ve got to make sure the AI is tuned correctly. And so it seems like Ben did to kind of set this tone for this conversation, we’ve seen a shift in AI to be more data centric, where a lot of this labeling a lot of stuff that was manual processes to kind of define the inputs for the algorithms is becoming more and more interesting and important. That’s just my take on it, though. You’re the one that’s actually working with the customers and enterprises out there. How do you see them adapting to AI? And what approaches are most interesting to the customers you work with?
Ben Book 02:19
Yeah, so you know, if you break down AI, there’s a lot of different components to AI, right? Ai, you know, tends to be the out like a derivative of an outcome. So you know, you need to start with the data to your point, either unstructured or structured, which lives in some type of data warehouse or data lake, then you have to apply an application, and then AI tools in a model, then you have to have the tooling around it like DevOps and ml ops. So there’s a huge stack, you have to build with AI. And you know, in the past, it’s been a bunch of science projects, you have a couple of data scientists, or maybe one data scientist working with some data on the laptop. But that’s not production only, you know, relevance for the customer or for the enterprise, because they want to do things at scale, which drive optimization, right. So when you think about AI, you know, some of the some of most of the use cases are, you know, how do I get 1% optimization? How do I get, you know, less risk on my analysis? How do I get predictive analytics, and it doesn’t need to be a 600% return 100,000,000% return that we hear in the market, it’s like, how do I get this incremental thing and build on that? Right, so how do I get 1% 2% 3% when you’re working at a $50 billion company 1% a lot of money, right?
But the challenge is, it starts in one little business group, it starts with one little project. And then you need to get to that scale. And that’s kind of the been the biggest challenge. We’ve seen a lot of, you know, large enterprises from New York Times, to companies like Microsoft, are trying to go up that maturity model. And you know, the challenge is how do you get how do you build the technology and operationalize it. So we’ve now built the technology, technology been built over the last 10 years, it’s now accessible, it’s democratized. And companies like Microsoft, and Amazon and Google are providing that they’ve also brought down the barrier for price. So it’s not as expensive now used to have to have data scientists building their own products, building their own tools. And so now you can do AI at scale, which is exciting. Going back to the point you made earlier about, you know, kind of different industries and how different industries are doing this and how they’re attacking it around ethics around, you know, challenges with governance and security. We worked with a really large pharmaceutical company, and they were trying to track adverse reactions. So think COVID How do I track adverse reactions for a vaccine, pharmaceutical, a pill? And what they had to do is they have to take in tons of different data from different sources have to take in PDFs, taken hospital images, doctor’s notes, Word files, unstructured data from social media, all of this data, put it in somewhere, then build a stack on top of it, and then apply the AI, right. And so it is a multi year project, to do all of that at scale. So a lot of customers have been doing POCs takes three, three to six months to roll that out. And then you know, you can actually productionize it. But again, it takes, you know, one to two to three years, because there’s so many people involved, so much data involved.
So the last five to 10 years has been a lot of science projects. Now we’re at the point where customers have figured out, you know, how to optimize how to drive analytics off of it. And now, how do I take this from one drug at a pharmaceutical company to all of my drugs? How do I do this for, you know, one brand, I have Johnson and Johnson to all of the brands, and that’s where it’s gonna get exciting, that’s where investors are gonna see really, you know, the returns, because it will bring down the cost efficiency, not just on the Johnson and Johnson, you know, you know, shampoo for babies, but every, you know, 50 of their brands are 100 of their brands. So if you’re doing optimization 1% across all the brands, that’s a lot more money than 1% for one brand. So, you know, that’s, that’s pretty exciting. And then the compliance, you know, is a big issue, which is why a lot of these regulated industries haven’t really rolled it out yet. One because they don’t want to deal with the compliance issues. And legal gets involved. But now, I think those those organizations and verticals are ready for primetime, they’re ready to take on the risk. And to investors are and board members are asking to be more efficient to drive more revenue. And really AI is one of those areas that they can make a big impact.
So healthcare data, for example, right, you know, I worked in healthcare as a consultant, a long time ago work with a lot of healthcare organizations. And the problem with healthcare is you have data everywhere, right? Just like I gave that in that example, how do you bring that data together? And then how do you apply AI to that, with models and tooling, again, to drive better outcomes. So you know, 1%, better outcomes for patients or driving costs down 1% for $50 billion companies a lot of money. So we have one analyst here, who comes from the insurance business, his full time job is to just bring down the cost for claims. And if he brings down the cost for claims for 1%. Internally, that’s going to be billions of dollars, right? So it’s a huge impact. And again, you know, we’re just at the beginning of this, you see it across IoT, now the infrastructures in place with things like LP win and low power Wan, edge edge AI is out there, we all hear about, you know, self driving cars, that’s still early days. But we actually have real proof points to that. And then that wouldn’t be that wouldn’t be possible without the cloud, that wouldn’t be possible. Without, you know, a lot of these more hyper scalar tooling that we have today.
Simon Erickson 07:51
Ben, that was amazing. You’ve described yourself as a systems thinker, I know that you live in San Francisco, kind of the South side of town, working more on the healthcare side of it, and kind of moving up north as you get into more of the programming and the the IT side of it. But it’s perfect, like we have described it, it’s the first out of the first inning here, we’re still so early on, there’s so much opportunity for this. But just to unpack a lot of what you just said, you know, which is very large in scope, there’s different kinds of components to that full stack, right, you mentioned that there’s data warehouses, and now we’ve got publicly traded companies.
And every one of those layers of the stack, the data warehouse is a service. If you think of a company like a Snowflake, that’s got, you know, now the data on top of the cloud service providers, like the Amazons, and the Microsoft’s, and you’ve got a way to go out there and get it. The DevOps, you know, continuous monitoring, you’ve got to be able to look at all this data, you start to see companies like DataDog, they’re out there looking at everything. There’s others, like you said, the tooling, and you know, the AI stuff, and how do you get this AI at scale, I can think of a company like C3.ai, just some publicly traded opportunities for retail investors, which are our audiences. Those are just some examples, though.
I would like to ask you at any point of that stack, or even if you want to mention about all of them, what’s gaining traction out there, what are enterprises finding is really working really well for this solution, or is it all dependent on who the customer is and what the application is?
Ben Book 09:05
Yeah, good question. So, you know, for large enterprise, they’ve all had these technologies before, they’ve had data warehouses. Many of them have had data lakes, you know, so they had some Teradata, they had, you know, Microsoft had Oracle, SAP. They had Cloudera. You know, these these companies. But you know, it was really a reporting thing. It was really like a tooling, and not really driving business outcomes for the most part. Or if they weren’t getting business outcomes, they weren’t getting the business outcomes they wanted, because they couldn’t move the data in real time. They couldn’t apply AI to it, etc. Right? So it’s kind of like the second second or third version of data now, which is okay, how do we actually take action on this? We have all this data. So yeah, the area that we see a lot of modernization to your point is at the data infrastructure layer, because every enterprise mid market company is saying how do we have the right infrastructure? But how do we apply it? You know, I think the big challenge is a lot of legacy technologies wasn’t easy to use, there was only the the data DBA database architect who could actually use it. Now we have technologies like Snowflake, and you know, Microsoft, and some of the other ones are coming out to make it easier to use. So it democratizes access, that’s been the biggest challenge. Otherwise, it’s just locked away with the data team, right? You can say the same thing about things like auto ml, or data science, you know, there’s a lot of tooling now that are accessible to anybody, anyone can go to Google you, me, you know, your friend, your neighbor, they can go to Google and download and download some AI technology and actually use it, it’s pretty incredible. So we’re democratizing both both the kind of data infrastructure layer all the way up to the application layer. But, you know, it does all come down to the foundational infrastructure. So again, a lot of companies are going to Snowflake, or go into Microsoft is their Microsoft shop, or Amazon, if they’re more of a dev shop, you know, I think a lot of that applies.
And then to the next level, you know, the next level is the DevOps, the orchestration tools. So companies, like a DataDog, for example, would be there, but also companies like GitLab, GitHub, right, those those companies are, are starting to see traction. And that’s kind of in the DevOps space. Again, DevOps was kind of something that’s taken a while to get here, data infrastructure has been here for many years, right. So people are modernizing their data infrastructure, doing it in new ways, DevOps is a new thing. So that’s a huge opportunity for a lot of companies who want to be more agile. And so there’s a huge runway for that. There are pockets of DevOps happening within large enterprise, but still not really rolled out. We work with a company called Pluralsight, who is the largest e learning engineering company in the world. And they work with a lot of customers who are trying to adopt DevOps. And the challenge with that is, you know, people, how do you have enough people? How do you train the people, because it’s still a new thing. So there’s a huge runway for that if companies can prioritize it, and focus on it. And that will drive the attraction of DevOps and, you know, CI/CD and all those technologies. And then you start to get into the security component, right, which is, you know, GitHub and GitLab are also focusing on data dog is now focusing on that all those DevOps tools are really focusing on the security piece, because enterprises are prioritizing that. So security is starting to get baked in to everything, data infrastructure, CI/CD, DevOps, agile. And so, you know, some companies that we work with, like I said, GitLab, GitHub, they’re, they’re growing like wildfire. Companies, like Snowflake clearly, are growing.
A lot of them start with growth companies, though, right? They all started with these kind of high flying, you know, Silicon Valley unicorns, and that’s where they can get the most traction, they can move the fastest, they can drive the most revenue. Now they’re starting to get into the enterprise, right, and they’re getting into compete with the legacy players who, you know, haven’t transitioned their customers to agile, or waterfall, or agile or DevOps, they’re still stuck in waterfall.
So, you know, I think that’s the huge opportunity like Snowflake, for example, you know, it’s gonna be a long slog for them to get in the enterprise, it just takes a long time, they have long planning cycles, they have big budgets, and they have a lot of legal and political things you have to deal with when you’re selling in there. So you know, they will be taking out Teradata. But Teradata will also stick around with some customers, right? Microsoft will be taken out Amazon and Snowflake will be taking out Microsoft, it’s gonna be a fun competition. But a huge opportunity, I think for investors, because it’s gonna be a long, long road, five to 10 years, I’d say. Same thing on DevOps, and then at the AI kind of stack, where you have companies like, you know, data IQ and data bricks, and you know, a lot of these other companies who are innovating, again, very early days, which is, you know, I’ll say maybe 15, or 20 year runway. So as you kind of go up the stack, it’s like, you know, five to 10, for data, modernization, you know, 10 to 15, for agile and DevOps and then 20, for AI, it’s gonna be a long, long runway and a ton of opportunity.
And there just aren’t enough people, right? That is the biggest challenge we have right now, my organization, every organization we work with, they don’t have enough people, they can’t train the people the fastest to do these things. And so, you know, people are gonna look to technology to solve that problem by being easier to use, right? By by enabling customers to do things faster. So, you know, that’s, that’s aligned with the great resignation and everything else that we’re talking about in the market. You know, so DevOps, automation, data, automation, AI automation, a lot of that will help solve some of the problems. But that problem is going to persist for a long time.
Simon Erickson 14:39
Ben, can you talk a little bit about that process that a large organization would go through when they’re deciding to change vendors for something like this? I’ve worked personally, to have the fortune 10 You work with large organizations, like you just said, it’s hard to get the ship to change directions. When you’re that large of a company. You’re established with something you train yourself on how it works, you’ve got budgets, you’ve got people you talk to all the time, how do they go about change? into something that’s cloud native versus a legacy provider like they’ve worked with for decades. Obviously, it takes time.
But does this start with kind of, you know, a smaller group that kind of pushes it up the organization and says, Hey, we should be making this change? Was it coming top down, we’ve got a CISO, or a CTO or somebody that’s saying, Hey, we’re going to embrace this, what’s the process look like for big company?
Ben Book 15:18
Yeah, good point, it is, to your point based on the the company and the leadership. So you know, we work with one company, very large aviation company, he was a data analyst, and he wanted to bring in a lot of the modern data tools, he got, you know, kind of, you know, some run away from his leadership to go find some new tooling. And then he went and built an entire data practice, he went and bought, you know, elation, which is a data catalog, he went out and bought data, IQ, you know, all of these new tools. But you know, along the way, he was proving the KPIs, and he got the buy in of the organization. You know, that’s really how a lot of these modern projects start, they don’t start from the top, right, because the top is not as worried about the ease of use the time to value until their, their staff can prove it to them, and then prove the money equation. So the the legacy players can kind of go top top down. But then when it gets down to the top saying, Hey, do you want to use these tools in the bottom saying, No, I don’t. And, you know, you need to retain those people. And if you give them the tools that they don’t want, they’re gonna go to the next company, it’s starting to create a lot of friction, right. And so I think that the combination of, you know, this great resignation, people wanting to be empowered, people wanting to do things in more modern ways, a lot of a lot of people retiring, who have those legacy technologies and the new player coming up, who’s going to be leading the organization?
So you know, a lot of it starts from the bottom. And then, you know, from the top, there certainly are some great leaders who have some vision, who want to enable their organizations. And but they don’t start at like, how easy is this thing to use? How is it going to democratize access, it’s typically started with what is the financial calculation? And if it’s not big enough financial calculation, then they kind of just put it on ice, right? I think you know, more recently, though, leaders are put under more pressure to not put stuff on ice. Like, you need to start, you need to keep innovating, and trying new things, which again, enable the people, you know, who were going to be driving these projects and trying new things out. So I think from a modernization perspective, we see it a lot from the bottom up. You know, in our organization, we focus on helping customers do POCs to prove it out. Because that’s what executives like they like things that are proven, right? So that’s, that’s gonna be the tried and trued. Way. And then also, there’s just more developers, there’s more data architects, then there are CIOs and CTOs and CEOs See, they just don’t have the time to investigate this stuff. So you know, it’s pretty exciting to see what the leadership is doing it at the actionable levels, right? To bring it up to the top.
Simon Erickson 17:55
Makes a lot of sense. So we’ve recently seen companies like Splunk, or Snowflake, relentlessly, quantifying the ROI of the solution, sending out teams to help developers figure out what’s the financial incentive of changing your vendors out there, let me spell it with three highly regulated, maybe legacy industries that are chattering about embracing AI embracing new digital infrastructure in for them, but between banking, healthcare and insurance, you can kind of put tech at the end of any one of those. And it’s kind of a new industry that’s being talked about out there.
But between banking, healthcare and insurance, which of the three of those do you think is most likely to adopt AI most rapidly right now?
Ben Book 18:37
Well, a lot of the banks insurance have been doing for a long time, just been very bespoke. So they’ve built their own models. They built their own platforms over the past 20-30 years, right. I think healthcare is the laggard in that one. Because they typically don’t have enough resources, and enough money to spend on this stuff, unless they’re big enough. A lot of the other banking insurance companies, you know, have entire departments that do this, right. Think auditors. Think actuaries right, they’ve been doing it in a way for a long time. The again, but it’s, it’s, it’s something now that you know, to do this, and change is difficult, right? So, you know, they might have been doing something bespoke or doing on legacy technology. Now, they need to change to go to snowflake, or to go to Microsoft Azure, or AWS, or Google, you know, to go do the POC to implement it, to get the buy in to get the budget, it can take, you know, one to three years to do that. Right.
So the banks and insurance companies are well on their journey to doing that, but they’re still doing it, you know, they’re putting they’re still putting in the data infrastructure, right? There’s still not, you know, to deploy AI across every single business, across fraud across credit cards, you know, they’re still early days. So they might have one or two of those fully deployed in hyperscale, or one of the modern AI data stacks and In the healthcare again, you know, it’s kind of a laggard, they’re not a laggard, because they can’t do it. It’s because one, they don’t have the resources to, they also can’t recruit a lot of the talent, right? That they can’t pay to pay the talent. And then three, it’s governmental, right, the government moves at a glacial pace with health care. And it takes a long time to implement technology based on that. So we had that we had the whole convergence and the whole kind of centralization of healthcare, which helped a lot. The data, you know, initiative to actually help companies and organizations collect data and share data, which is great. But, you know, we’re just kind of an early, early days for healthcare, banking, insurance, you know, we’ll continue doing and have been doing it for a long time. And they have a lot of experience and a lot of money to throw at it.
Simon Erickson 20:47
That’s true. So speaking of that, and getting back to the healthcare center, you have a background in healthcare has to also been, you know, how are we progressing there? I know, like you said, it’s hard, it’s regulated, you know, it’s government, everything else like that. It’s hard to get talent. There’s just so much data, everyone always sees the opportunity for correlating and connecting the dots, who had electronic healthcare records, it was kind of a move to get everything digital. We’ve heard Apple and Google chat about this for years. But it’s certainly got to be frustrating for them. Just seeing the concerns, you know, the safety concerns, data, privacy concerns, all this stuff?
Where do we stand with healthcare? Are we getting anywhere with this? Or is it still gonna be something that’s perpetually three to five years out?
Ben Book 21:23
Yeah, I think the main issue with the healthcare data is, you know, I don’t know if any, any consumers will trust these consumer companies, right, that’s the biggest challenge. And then to that, they really don’t interact with the industry, the way that the industry, you know, works. So they need to change the way that they work, and work more like a payer insurance company, like a doctor. And if they can build an ecosystem around it, versus wanting to be a player, that’s really what these technologies companies are really good at right is building an ecosystem of partners, an ecosystem of digital providers, an ecosystem where you can share the data. That’s what they do is their business model, right? Google, Microsoft, Amazon, they go to market, and they bring all of these other companies with them to help them make money, or they help those companies make money to get into the game. And that’s something that I think they just haven’t cracked it. And then the trust, and the whole thing around explainability is a whole another topic. Right around AI. You know, that’s been an issue, how do you explain how you get to this result, a lot of tooling didn’t have that. So they actually blocked a lot of the ability to use AI. Now, the technology and the tooling has it because customers like health care have asked for it, now that the AI companies have built it. So now they’re actually able to prove explainability down to the code level down to the documentation level. And, or allowing, you know, humans to intervene. So again, the technology is now there for healthcare organizations to use. But you know, it’s, you have your providers, you have insurance companies, and then you have hospitals, all of them have different initiatives, it’s almost a yet add a whole another branch of government here are a whole another branch of business, which is tech, right, and techniques to be at the table along with those other parties, or they need to find a way, you know, to work with one of those parties to get into the table, the easiest way is insurance, because that’s probably the closest to what their business is around data.
Insurance companies want to make money, they can leverage technology to make money. So you know, I think it’d be interesting to see those companies cozy up to the insurance companies and work more closely with them on the data piece that will then drive the revenue down from the healthcare companies, which they’re trying to get better optimization. So, you know, it’s it’s tricky, because the insurance companies and payers and providers are are constantly discussing, you know, how to how to handle patients and outcomes. And the technology companies just don’t really have a lot to offer there. Right, they can just offer the data and the outcomes along with along with the tech. So, you know, it’s exciting, I think, you know, we’ll eventually get there, but it’s going to take a lot longer. And the consumer, this consumer trust thing is just getting worse. So unless these companies can prove that they can be trusted with their privacy, you know, I think most people would trust their health care provider versus Google or Amazon or, or Facebook. Right.
Simon Erickson 24:13
I second the notion of putting a tech representative at the table to chat with the government about pushing change. Good idea for this one.
Changing gears a little bit here. You know, in our world, we look at the guidance that’s been given by tech companies. A lot of it has been very conservative, especially this quarter is is saying, hey, the economy is digesting a lot of things going on right now. Notably, rising inflation rates, you know, the Fed is increasing Fed Funds rates. And so we’ve got higher interest rates on things. We’ve got still kind of COVID lingering out there. We’ve got challenges with the chip shortage, especially in semiconductors. Right now, a lot of companies are kind of saying, Hey, we’re all going to collectively say it’s going to be a tough year for 2022. Are you seeing the same thing with your customers? Are they slowing down on the deployments that they’re doing are backing off on things that they previously said? Or is it not as conservative as publicly traded companies are making it out to be out there.
Ben Book 25:03
Yeah, I think there’s there’s a little waiting right now everyone is like waiting to see what happens. So I think I think CFOs and, and executives are kind of pulling back a little bit, but not all the way yet. So that’s good. I think the the CFOs, and CEOs and you know, people are leading strategy teams, also understand that when you’re in this type of environment, this is the time to grab market share, right. And the issue that a lot of these larger companies have had is getting market share in digital and doing digital, right. So you know, they also know that if they can do digital, right, that will offset, you know, the cost, and revenue they have another line is like.
Look at Disney, right? Disney is always a great example. I hate to bring it up, because everyone talks about it, but it’s just a great example. You know, they figured out a way to make money with digital, right. And you know, they’re gonna continue to invest in that. And they’re going to continue to try to find new ways, because that offsets a lot of the other issues that they have with their other businesses. So, you know, I think, you know, companies are still doing the modernization projects, they may slow it down a little bit 10%, less 20%, less, I don’t think we’re gonna see everyone just stopping their projects. And then to, again, back to the talent, like, talent wants to be at a place where the company is going to provide vision, they’re going to modernize. And if they want to continue to retain their talent, they have to keep doing these things. So you know, that’s another piece that I just keep bringing up, but it’s just a big challenge now with the talent shortage, as has the shortage for everything. But you know, I think that that’s the big companies, the mid market in the s&p, they get hit a little harder, right, I think then the larger companies, you have the resources and the relationships with banks to give them whatever they need to continue to operate. And also the government’s who are providing the soft landing, or whatever we have coming up coming our way. So I think that’s some BS in in markets, you know, they, they really need to be prepared.
And they really need to be thinking about, you know, cost reduction, and being able to manage costs if we get into this period of sustained decrease in spending. But again, you know, this is where they can kind of turn to technology and do things, to modernize themselves to create more efficiency. So, you know, enabling your staff to do more with less, that’s really what every company is always asking, but especially the SMBs, mid markets, they constantly live in that environment. So if they can leverage, you know, new types of technologies, like data technologies, we talked about who are now accessible to the mainstream, like auto ml or things like that, that will help them you know, you know, get get around any issues that we currently have.
But, yeah, it’s certainly like a wait and see, we’ve been talking to customers. Some customers say, yeah, yeah, we were gonna spend, you know, a couple 100 grand, now we’re gonna spend 100 grand, some customers say, oh, yeah, we have a little bit of a spending freeze for now for the next month, to wait and see, other ones are saying, Hey, this is the time to grab market share. And we’re going to do that. And to be honest, you know, whenever companies do that, you know, they also set the stage for their, for their partners and customers, and they typically go along with it. So it’s going to be the time for the bull to take it on. I know his organization. You know, we’re continuing to grow, we’re growing at 50% 80% a year. And we want to continue doing that. And we have the kind of headwinds that are back. And if we start to slow down, then that slows down on momentum, that slows down our partner activity that slows down, you know, our employee activity, and also what our employees are excited about with our purpose. So you know, we’re gonna go full, full steam ahead. But you know, what we’ll see, we’ll see what happens in the next couple of months.
Simon Erickson 28:33
Yeah, for sure. Especially if the s&p is a small and medium businesses, like you mentioned. I mean, it seems like the larger organizations, once you’ve worked with these, they’ve already basically embrace cloud. They’re embracing AI, they have teams, they have resources to do that. How are smaller customers who aren’t so far along in that digital transformation? How are they picking cloud providers? Do they just go with Amazon first, and then kind of expand from there, or Microsoft, you know, Oracle, everybody else has got, you know, a Google now has got a very comprehensive offering, how are they picking the CSP? And what influences those decisions for them?
Ben Book 29:05
Now, a lot of SMB start with things like, you know, right, so they know, Gmail, and then say, Okay, what else does Google have or I use, you know, Outlook. So the companies who have application type technology, you know, they have the kind of foothold with the consumers slash SMBs mid market, who are using those technologies. A lot of mid market companies use Microsoft, and they’ve been with them for a long time. So a lot of mid market companies just go with Microsoft, and they have the collaboration tools, like teams, they have the email, they have the basic things you need to do your job, right. And so does Google. Right, Amazon does, right, for example. Also ease of use, so how easy is this technology to use? Do I need to invest a lot of money in training my people do? Do I need to invest in time that it takes to train people to get this thing up and running? Again, you know, that’s where Google and Microsoft really come in. They make things really easy to use, and they make it accessible to everybody. And Amazon is kind of, you know, really focused on developers. So they’re not focused on really democratizing it for the every man or the every woman who wants to use technology. So, you know, the bet is, you know, typically on on Google and Microsoft, and you know, mid market is huge. It’s 50% of the technology spend. It’s also sold through the channel, Microsoft has the strongest channel. And they built this really strong channel, Google was just starting to build their channel a couple years ago, they’re starting to extend their channel. And, you know, there’s also when you think of SMB mid market, it’s also like local, local, state governments. Those are like, Yeah, I would think of those as SMB and mid market, they just don’t have the resources that big enterprises have. So how can you just give me the basics, the zoom, the, the teams, the things that I can just do to communicate and get work done? They’re not doing the AI stuff yet. They’re they’re just trying to make sure they have enough, you know, collaboration tools that they can communicate with customers, or teachers or students.
Simon Erickson 31:07
Yeah. Yeah. Perfect, Ben. And just one more question I have for you, since I have you here. And I always enjoy these conversations with you, I got to kind of do a lightning round here at the end and talk about some stuff that’s even farther out on the horizon. Right, we’re talking about where it’s getting implemented, we’re talking about where the rubber hits the road. Let’s change gears and talk about some topics that are kind of out there. How likely of the following you can say this on a one to 100 scale, or you can just kind of give me some some some comments about you thinking about but how likely are these actually of getting implemented in the next three years at a larger scale? Maybe not in the next three years? Just just kind of broadly thinking, How do you think about quantum computing?
Ben Book 31:42
Yeah, we’re, I think everyone is excited about it. I think we’re starting to see some early proof of concepts. And I will, I do think we will still see some great implementations. There’ll probably be a handful. But you know, we’re not gonna We’re not quite close to the early mainstream yet on that.
Simon Erickson 31:59
Okay. It’s still early, but we’re starting to see some signs of light for that. How about how about thoughts about the metaverse?
Ben Book 32:05
That’s a great one. Think, I don’t think anybody really knows what that is yet. And then he’s create a lot more awareness and prove it out a little bit more for customers to use it. And then again, they’re building their ecosystem around companies like Microsoft and Google. I don’t think so.
Simon Erickson 32:20
And then finally, how about DNA used for storage devices, data storage using DNA?
Ben Book 32:27
Yeah, I think we’ll see some early implementations in that like quantum computing. So yeah, I think I think those the quantum computing and then DNA I would bet on the metaverse, it’s gonna take a while.
Simon Erickson 32:38
To be determined. I don’t think anyone really understands the metaverse, maybe including Mark Zuckerberg himself. Thanks again, Ben Book, the co-founder and CEO of GigaOm out in California. Does some fantastic work in deploying some of the newest most emerging technologies to larger enterprises and even smaller businesses as well. It’s always a pleasure. Thanks for being part of the 7investing podcast.
Ben Book 32:56
Thanks for having us Simon. Good to see you.
Simon Erickson 32:58
And for anyone who subscribes to our podcasts or enjoys these conversations, we encourage you to also join our email list where we give key takeaways from each one of our conversations. You’ll find those at 7investing.com/email-signup that will hit your inbox with a written recap of every one of these podcasts that we record. So once again, I’m 7investing founder and CEO Simon Erickson. It is our mission to empower you to invest in your future. We are 7investing.
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