“Breaking Down the Path to Entrepreneurship”: An Interview with Gil Allouche, CEO, Metadata

  

Gil Allouche is a tech entrepreneur whose passion for artificial intelligence (AI), big data, and growth marketing led him to start Metadata in May 2015. As the Founder and CEO of Metadata, Gil prides himself on building a customer-first company. He is part of an ambitious team that is committed to solving a major problem that B2B marketing and sales professionals face -- generating qualified, opt-in leads. Prior to Metadata, he ran marketing for Karmasphere (now FICO), Qubole and Silver Spotfire (now TIBCO).

Could you explain a little bit more about what Metadata brings to the marketplace?

Metadata is a technology software company in San Francisco. We are disrupting the B2B marketing space. We automate the marketing operations role with automation and AI. There are many tools in the B2B marketing space for tag management and email marketing automation and advertising and data vendors, etc. What Metadata does is connect all of those tools together, learn what worked and what didn't work, and then orchestrate operations from within those technologies using an API, so that people don't have to log in every morning and manually operate those tools. That's the big vision.

Today we connect about 40 different tools in paid media and do anything from sourcing audiences to getting all of their PIIs and cookies, etc. Then, we execute campaigns on social media, retarget, and optimize all of those campaigns automatically using KPIs from Salesforce and market automation from the customers. We have about 60 customers. Some of them are enterprise and some of them are mid-market and startups.

Can you tell me a little more about your background before you started your startup?

I’m a software engineer. I’ve written code since I was a kid. I moved to the U.S. about 12 years ago to do my MBA at Babson College, an entrepreneurship school in western Massachusetts. After that, I spent eighteen months running product for B2B companies. Then I moved into the marketing realm. I was a very technical marketing manager, so I relied on 3rd party contractors to do my copy and communications. But, I had complete responsibility for on demand generation, making sure that my counterparts have a pipeline to go and sell. I did that in three companies. Two of them got acquired. At the third one, I was the first business hire. I was in a small team in a tiny room, and today Qubole is a few hundred people.

After doing that for about 7-8 years I had to choose whether I wanted to continue my career as a CMO (Chief Marketing Officer) and just work for bigger companies, or switch into the entrepreneurship realm, which is what I'm interested in, and build a product that will serve those non-technical CMOs and enable them to do what I did, but much easier. I started a consultancy with Qubole becoming my first customer. Three years later, this is where we are.

What previous experience do you feel best equipped you for your role right now?

Having the experience from both sides, as the software builder as well as the marketing software user. Building systems with AI, building Web products in the first part of my career and then switching up to doing an MBA led me to understand the business side of things and which software can solve for critical business KPIs. Then choosing the marketing space I wanted to innovate in, and then working as a CMO in a B2B company, gave me both points of view. All that prepared me for building the right software and serving the customers.

I started software companies before, and I'm always passionate and excelled in that type of culture, without everything set up, An unstable environment and high risk, that's where I thrive and every year we exist makes me better entrepreneur for the future.

Do you believe that having a technical background first and then understanding the business side of things is the best background to have? Or do you believe learning the business side of things first is more important?

It's hard for me to say because I'm already subjected to the way my career went and so it’s hard for me to roll back and say maybe there’s a better way. I think I'm well equipped to run Metadata because I have a technical background and then I had the customer experience. Would it be better the other way around? Maybe. I don't know many people who have done the other way around.

The vast majority of people that I know in my position have a technical background and they know what's possible to build to begin with. They build it in an amateur way using scripts, which is exactly what I did for eight years while I was running marketing. Then, after seeing it working, I built some of the tools myself, used them in my role and then built a generic solution for the rest of the market. It would not have been possible to do that the other way around because I wouldn't even know it's possible to fix using software.

If you could go back to the first day of your startup, when you were still building Metadata up, what advice would you give yourself?

Probably spend more time building the software. Maybe give delivery more time, building something small, focused, and that was more of an MVP (Minimal Viable Product). In my mind my MVP was more of a VP and not so minimal. It was more of a bunch of different tools put together. I think I would’ve focused on building something more holistic, but more minimized. It took us about a year to bridge that gap to what we have today.

I probably would have invested a little more in engineering and product earlier on. Today that’s our main focus I’d also recommend reaching out to all of your colleagues, former managers - those will be your first customers, advisors, investors and will give you friendly needed feedback. Finally - I think it’s critical you learn how to manage your own psychology. Starting a company can be a tough journey and you need to learn to forgive yourself, adapt quickly and include others in your journey.

What do you think is the most valuable thing you learned from being a part of Alchemist?

I learned a lot from Alchemist. I learned about how seed investors perceive companies. I think I'm good at it, thanks to Alchemist. I also learned how to pitch my company, self development and reaching my first paying customers using CAB.  That was very helpful and Alchemist definitely helped me do those. And finally the network, connections and practical 1:1 programming. Big shout out to Danielle and Ravi who are always there.

If you had to give some advice to someone in Alchemist right now, what kind of advice would you give them to make the most of the experience?

I think you have to come to Alchemist with something to offer already, meaning some technology or customers, and then take that raw material and build upon it. If you don't have much, I would recommend to wait a quarter or two until you do, because otherwise you're going to waste your time in the program.

Another piece of advice I have is to start the program before the program starts. Danielle and Ravi will attest that I was in touch with them maybe two or three months prior to the program starting, doing email campaigns to get investors, asking advice about evolution and about the rest of the things that I had challenges with before the program started. So, moving at your own pace with the leaders of Alchemist I think was the key success factor for me.

Finally, pick your battles in terms of what you want to participate in Alchemist and what you don’t. Running a business that already had some traction, I did not want to stop everything and attend every talk that Alchemist had. Rather, I wanted to keep running the business and use Alchemist whenever I saw fit, maybe 40 percent of the capacity or maybe 50 percent of the capacity. I don’t know if Alchemist will be happy with me sharing this, but that's how I set it up, and it was very successful for us because it allowed us to keep the business running, and then use Alchemist for the things that we actually needed help with. Versus, go line by line with a program that was not always fitted to us because some companies were in a very different stage. We already had 30K MRR. and were kind of already started.

Given your background, do you have any advice for other foreign founders?

Being in the U.S. I would say visiting the U.S. and going after local companies with what is called the customer advisory board (CAB), a tactic that Alchemist educates about, is a great idea. Coming here, doing the activities, being at the office, I think is very important. I would also say, bring your team members with you to Alchemist. You need them involved, and not just your founders. You want to bring your co-founder and whatever the team is and bring them into the program and get them involved.

I would say especially for foreign partners to begin the process of reaching out to Angels and Seed Investors prior to joining Alchemist and work hard on getting some funding prior to the demo day. We got some money at the Investor Feedback Summit and we got some money prior to the program even starting. That was very helpful for us to give us a good sign that the strategy of Alchemist works, prior to the program even starting. It was very helpful, especially for a foreigner who was not very knowledgeable about those things. That’s a piece of advice I would give someone who's coming from a different country to Alchemist.

Was there anyone in your life that helped you as a mentor and influenced you a great amount? If so, what about them helped you?

I'm very lucky to have many mentors. I think I wouldn’t be able to get where I am without them. Some of them belong to Alchemist, some of them belong to Alchemist network, some of them don't. First one that I had goes all the way back to my high school teacher who gave me confidence and belief that I didn't have in myself back then. That’s the first one, back when I had some issues in high school. Then, if I take it all the way to Metadata time, my first advisor, outside of Alchemist, was Mickey Alon, a serial entrepreneur from Israel. He helped me get started with my very first pitch decks, etc.

And then one of the other very prominent advisors I had to date, I would say my strongest advisor, was Bill Portelli. He is from the Alchemist network. I met him at an Alchemist event and he's been tremendously helpful with all things Metadata from sales to personnel issues, etc. Other wonderful advisors who constantly tell me things how they are include Boris, Derek, Jean, Bobby, Jonathan, Gary, Eli and the list goes on. Maybe that’s another important advice - get your advisory board early on and keep them engaged. They can do magic.

And I would say that Danielle has been very helpful. You know Danielle is kind of the de facto manager for Alchemist. She makes a lot of things happen and she's also very good at advice and very resourceful. I think she provided great advice and mentorship at times when I needed it.

What are you excited about for Metadata in the coming future?

Growth. We are onboarding more and more customers than ever before. The last four months have been stronger than the previous twenty-four months before them. We're seeing a good amount of growth. We're also seeing a lot of confirmation from the market that what we're doing is the future of marketing. So now it's a question of how quickly can we leverage that growth with the value, raise more capital, and then grow the company. I’m excited about the next stage of the company moving from 60 customers to 200. Having a fifteen person team to having a thirty person team. I'm excited about those challenges.

What constitutes success for you personally?

Success is to see a product that you really needed in the market being used by enterprise companies like Amdocs, Hitachi, and SugarCRM. Having companies like these use the product, successfully renew, excel, and giving us testimonials and case studies. For me that is success. That means that the initial idea that we had and the solution to that problem that we thought exists, these are confirmation for a product market fit. That's the first piece of success for a company at our stage.

The next success would be a big institutional venture capitalist standing behind us with a large sum of capital to grow, and of course reaching profitability. For me a personal milestone that I'd like to achieve. Those three things I think are the major successes in relation to Metadata.

Are there any other insights you've learned that you want to share with the next generation of entrepreneurs?

I think the biggest thing to do for an entrepreneur is to get started. To unblock your own limiting thoughts of “what needs to happen before I’m ready to start a company.” Nothing has to happen. You just have to start it and then break down the path to entrepreneurship through small wins. To put the first landing page together, talk to the first 20 prospects, talk to the first angel, put it on Facebook. Share it. Don't be secretive about your startup. The moment that you think you're ready to, if you have a problem that you're very passionate about and you have the domain expertise, you shouldn’t wait a second, you should just start it and then let it grow and let the market reject your idea or execution. You may have to switch, to change your idea, change your execution strategy, change your partners, what have you. Or if you see that it's gaining traction then just continue to roll with whatever is coming at you.

I would say that the biggest hurdle is for people to change their mind and say I'm an employee now and today I'm an entrepreneur. Nothing's doing it for you. You have to do it on your own. The best way to get it is to just start taking action. You don't have to resign from work right away, just devote 3 days or a week to it for a few months and you should be able to see some progress before you make the switch completely. That’s that's the biggest hurdle I would say for entrepreneurs.


About the Alchemist Accelerator

Alchemist is a venture-backed initiative focused on accelerating the development of seed-stage ventures that monetize from enterprises (not consumers). The accelerator’s primary screening criteria is on teams, with primacy placed on having distinctive technical co-founders. We give companies around $36K, and run them through a structured 6-month program heavily focused on sales, customer development, and fundraising. Our backers include many of the top corporate and VC funds in the Valley—including Khosla Ventures, DFJ, Cisco, and Salesforce, among others. CB Insights has rated Alchemist the top program based on median funding rates of its grads (YC was #2), and Alchemist is perennially in the top of various Accelerator rankings. The accelerator seeds around 75 enterprise-monetizing ventures/year. Learn more about applying today.

Seven Enterprise Business Models You Need to Know In the Age of Software


Most people never think of technology from an economic point of view. Instead, we focus our efforts thinking about the nuts and bolts of the technology. Tim Chou, a current lecturer at Stanford University, spent his career focusing on Enterprise Technology. He believes that it is important to know a variety of business models to better understand how we can sell to customers. In a talk given at Alchemist, he outlined seven important models of how software companies drive revenue, and offered further insights into the sales process.

Model One: The most typical, yet still extremely effective model: license the software to the user and then charge for support and maintenance. Tim gives the example of Oracle, which previously was a $15 billion corporation with $12 billion coming from support and maintenance.

Model Two: Make your software open source, but monetize the support and maintenance. Tim emphasizes that Red Hat is the only real example of success for this model.

Model Three: Outsource. “I’ll take over your mess and I’ll do it for less.” He explains that the amount of money to manage software is 4x the price so in most cases 75%-100% of the budget is fully allocated for the next year. Therefore, by outsourcing, you reduce the cost structure to purely human labor in China, India, Eastern Europe, etc. However, Tim goes on to outline two major flaws with this model. One, you are unable to maintain a low cost of labor for outsourced labor as workers will eventually want wages that match the workers in Silicon Valley. Two, the primary reason of system failure is human error.

Model Four: Tim explains, “The customer pays for the software and maintenance, while I’ll manage security, performance, etc. for a set price per user per year.” In this case specialization is key. If you can standardize the hardware and software then you can replace human labor with machine labor, crushing cost structures and increasing reliability.

Model Five: You alter the payment terms of Model Four. This can mean paying monthly or by other terms.

Model Six: Every business application company since 1999 has delivered in this model. It involves removing the at-home and at-customer aspect of the model, in order to standardize and reduce cost structures even more. In justification, Tim explains that while operating in model four or five, cost structures can be taken down to about $50 to $70 a user. On the other hand, students of model six can get down to $5 per user.

Model Seven: In reality, Facebook, Amazon, and Twitter are all software companies. What’s different is the way they charge for their service, whether it is ad-based models or embedding it in the transaction. An example is buying a book on Amazon, which is essentially paying for the software. In order to justify that there is an extra step in standardization, Tim argues that Google would otherwise charge around 70 cents per user per year in order to break even for searches. He explains all of their software is extremely standardized so their cost structure is entirely reduced to power (electricity).

Understanding your Customer is Key to Choosing a Business Model

These seven models offer a wide variety of choices to founders—however, in order to know which business model is right for your customers, you’ll need to talk to them! Tim believes in this day and age, we can now target our customers by first knowing who they are instead of just throwing your product out there. We can apply Geoffrey Moore’s idea of Crossing the Chasm to people who will buy into your vision and help you cross the chasm. Tim explains, a lot of the time you can tell if a potential customer is only interested in following the mainstream if they ask, “What is your ROI?” They are not your early investors. They are only interested to see if others have bought. Customers before the chasm are not large corporations, rather, they are individuals.

How do you Sell?

Once you know your customers, the challenge becomes how to sell to them. When broken down, there are two methods of selling. Both methods of selling involve “preciseness:” low and precise selling (e.g. Amazon selling $10 books, movies, etc.), and high but imprecise selling like business software, where you need fewer sales due to high value. The challenge is sitting in the middle where selling price is still high and is still imprecise. Tim makes the analogy of big screen TVs. Just like enterprise sales, there is an education cycle before you buy where you ask friends, read reviews, and do your research. Ultimately, you find that “selling is education and education is selling”.

The challenge is that your sponsor (the guy who thinks what you’re doing is cool) is unable to answer questions to others about your software. Tim explains that the key is the art of storytelling. It really matters who is saying what. Without the right character telling the story, there is no credibility. Stories are a key part of learning as it activates a different part of the brain and your information is more believable.

There are three types of stories: Man versus man, man versus nature, and man versus self. When telling a story about your product, communicate it in 3-5 points, identify the problem, and identify the value of your solution. By comparing the situation before your product to the situation after your product, you create value.

What does this mean?

It is no surprise that in order to sell to customers, you must understand your customers. It is important to understand that while your customers’ ability to use your product relies entirely on how you structure your business model. Their role as a customer lives entirely inside the model you choose to adopt. Therefore, when analyzing your product’s reputation, you can not overlook how your model is structured. By being aware of which business models work and which ones don’t, you can begin to better understand your customers as a whole.

About the Alchemist Accelerator

Alchemist is a venture-backed initiative focused on accelerating the development of seed-stage ventures that monetize from enterprises (not consumers). The accelerator’s primary screening criteria is on teams, with primacy placed on having distinctive technical co-founders. We give companies around $36K, and run them through a structured 6-month program heavily focused on sales, customer development, and fundraising. Our backers include many of the top corporate and VC funds in the Valley—including Khosla Ventures, DFJ, Cisco, and Salesforce, among others. CB Insights has rated Alchemist the top program based on median funding rates of its grads (YC was #2), and Alchemist is perennially in the top of various Accelerator rankings. The accelerator seeds around 75 enterprise-monetizing ventures / year. Learn more about applying today.


Not BI, AI

A product business can double its revenue and quadruple its margins by moving to a service business. What is service? It's information, personal and relevant to you.  

Amazon delivers information that is personal and relevant to you, for example, with its recommendations: customers like you bought this book, or customers like you like this music. Now think about your favorite banking site and log in. I will contend that there’s very little personal and relevant information. The only reason you’re being asked to log in is for security reasons. After that you are really looking at a big shopping cart to move money from savings to checking, buy stocks, sell a bond, etc. 

Could the bank deliver information that’s personal or relevant to you? Could they say that people like you bought this stock, or people like you re-financed their mortgage? Yes, they could, so why don’t they? Well, you probably never thought about this, but the consumer Internet that Google and Bing let you see through search is believed to only be about 100 or 200 terabytes. That’s it. Now, I’ll guarantee your current IT systems have 10, 100, or 1,000 times that amount of information; so why can’t they deliver information that is personal and relevant to you? Well, I say they are held hostage by the SQL monster. So let’s just have a little fun here.

It’s the late ‘90s and I have several SQL engineers in the room. I come in with a brilliant business idea. My idea is that we are going to index the consumer Internet and we’re going to monetize it with ads. We’re going to be billionaires! Just guess what the SQL engineers would do?

The first thing they’re going to do is design a master, global-data schema to index all information on the planet. The second thing they’re going to do is write ETL and data cleansing tools to import all that information into this master, global-data schema. And the last thing they are going to do is write reports, for instance, the best place to camp in France or great places to eat in San Francisco.

Any of you who are technical are probably laughing right now thinking, “Well that’s a completely stupid thing to do.” But if you try and attack the problem using SQL and BI tools, you’re also going to fail.  

Furthermore, as you connect your machines, you have the opportunity to bring in large amounts of time-series data. Modern wind turbines have 500 sensors and the ability to transmit those sensor readings once a second. Most analytic techniques depend on the idea that the data scientist can try and visualize the data, but how is that possible if I have a 1,000 wind turbines and data for 12, 24 or 36 months?  How can we learn from that?

Artificial Intelligence (AI) has been increasingly in the news. Google’s DeepMind made headlines when the machine, AlphaGo, programmed to play Go, defeated Lee Sedol, one of the best players in the world, by 4 - 1. Amazon’s Echo and voice assistant Alexa is being widely praised for its voice recognition capabilities, and many people remember how Watson handily beat the best Jeopardy players in the world.

Things have been changing quickly and here is a great example. ImageNet is a database of millions of images. Beginning in 2010 the ImageNet Challenge was established to see how well a machine would do at object recognition. As a point of reference an average person will be able to achieve 95% accuracy. In 2010, the winning machine could correctly label an image 72% of the time. By 2012, accuracy had improved to 85%, and in 2015 the machine achieved 96% accuracy.

So why have things been changing so quickly?

First, we’re continuing to get more computing and more storage for lower and lower prices. Next generation compute and storage cloud services can provide thousands of computers for an hour or a day. AI and machine learning software require lots of computing during the learning phase. The second reason is the emergence of neural network algorithms. Third, it’s not possible to apply these advanced AI technologies without data, and lots of it. Consumer Internet companies like Facebook are able to use billions of photos to train facial recognition systems. AlphaGo learned from millions of games of Go and Alexa learned from millions of voice patterns.

While we’ll continue to see progress in replicating what humans do, we have the opportunity to apply these AI technologies to even more important challenges. Today, many of the machines that generate electricity, transport goods, farm food, or sequence genes have large amounts of data. If we were able to connect these machines and collect the sensor data from them, we would have the opportunity to use AI and machine learning technologies to operate a more precise planet. Imagine a future farm that can use fewer pesticides, which not only reduces the cost of the food, but also makes it healthier. A future power utility could be based on a vast array of solar panels, wind turbines, small hydro generators and batteries to generate more power, much more efficiently. A pediatric hospital could share the results of millions of MRI scans and diagnose patients far faster.

Next-generation machine companies could not only double their revenues and quadruple their margins, but build a better planet in the process.

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Timothy Chou, Ph.D.

Timothy Chou has lectured at Stanford University for over twenty-five years and is the Alchemist Accelerator IoT Chair.  Not only does he have academic credentials, but also he's served as President of Oracle's cloud business and today is a board member at both Blackbaud and Teradata. He began his career at one of the first Kleiner Perkins startups, Tandem Computers, and today is working with several Silicon Valley startups including as the Executive Chairman of Lecida, which is building precision assistants for the IoT using AI technologies. Timothy has published a few landmark books including, The End of Software, and Precision: Principals, Practices and Solutions for the Internet of Things, which was recently named one of the top ten books for CIOs.  He's lectured at over twenty universities and delivered keynotes on all six continents.

Not Machines, It’s the Service

If your company builds agricultural, power, construction, healthcare, oil, gas or mining machines you’ve probably heard about the Internet of Things.  All of us in the tech community are excited to tell you about our cool technology to run on your machine, connect it to the Internet, collect data from it, and then make predictions from that data using advanced machine learning technology.

But maybe the question you’re asking as the CEO of one of these companies is why should I care?  Isn’t this just stuff my geeky R&D staff cares about? How can it be meaningful to my business?  

I’ll be making the case that with IoT software; you can not only double the size of your business but also create a barrier that your competition will find difficult to cross.

Next generation machines are increasingly powered by software.  Porsche’s latest Panamera has 100 million lines of code (a measure of the amount of software) up from only 2 million lines in the previous generation.  Tesla owners have come to expect new features delivered through software updates to their vehicles.  Healthcare machines are also becoming more software defined. A drug-infusion pump may have more than 200,000 lines of code and an MRI scanner more than 7,000,000 lines. On a construction site a modern boom lift has 40 sensors and 3,000,000 lines of code and on the farm a combine-harvester has over 5,000,000 lines of code.  Of course we can debate if this is a good measure of software, but I think you get the point.  Software is beginning to define machines.

So if machines are becoming more software defined, then maybe the business models that applied to the world of software will also apply to the world of machines. Early in the software product industry we created products and sold them on a CD; if you wanted the next product, you’d have to buy the next CD. As software products became more complex, companies like Oracle moved to a business model where you bought the product (e.g. ERP or database) together with a service contract. That service contract was priced at a derivative of the product purchase price. Over time, this became the largest and most profitable component of many enterprise software product companies.  In the year before Oracle bought Sun (whilst they were still a pure software business) they had revenues of approximately $15B, only $3B of which was product revenue, the other $12B, over 80%, was high margin, recurring service revenue.

In the world of machines, you might wonder why General Electric is running ads on Saturday Night Live talking about the Industrial Internet.  Why are they doing this?  All you need to do is download the 2016 10-K (http://www.ge.com/ar2016/assets/pdf/GE_2016_Form_10K.pdf) and look on page 36.  Out of $113B in revenue they recognized $52B, or nearly 50%, as service revenue.  Imagine if GE could move to 80% service revenue, not only would the company be tens of billions of dollars larger, but also margins for the overall business could easily double. And let me remind you this is all done without connecting the product (software or machine).  Once connecte you can provide even more service and ultimately deliver your product as a service.  As we have already seen in high tech software and hardware moving to product-as-a-service is transformative.

So if you’re an executive at a power, transportation, construction, agriculture, oil & gas, life science, or healthcare machine company, how big is your service business?

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Timothy Chou, Ph.D.

Timothy Chou has lectured at Stanford University for over twenty-five years and is the Alchemist Accelerator IoT Chair.  Not only does he have academic credentials, but also he's served as President of Oracle's cloud business and today is a board member at both Blackbaud and Teradata. He began his career at one of the first Kleiner Perkins startups, Tandem Computers, and today is working with several Silicon Valley startups in roles from investor to executive chairman. Timothy has published a few landmark books including, The End of Software, and Precision: Principals, Practices and Solutions for the Internet of Things, which was recently named one of the top ten books for CIOs.  He's lectured at over twenty universities and delivered keynotes on all six continents.


Is it Time to Invest in IoT?

I published my first book, The End of Software, in 2004. At the time, I was president of Oracle On Demand, which served as a starting point for Oracle’s billion-dollar cloud business. In the book I discussed the fundamental economic reasons software should be delivered as a service.

As an example of new startups in the field, I discussed four companies, VMwareSalesforce,NetSuite and OpenHarbor. None of them were public companies when the book was published. Salesforce was still under $86 million in revenue. While I didn’t get all four correct, three of the four have gone on to be major companies driving the second generation of enterprise software.

It’s 12 years later. Some have said that enterprise software is a mature business; CEM, ERP, HR and purchasing software are now all being delivered as a cloud service. So is it the end?

I don’t think so. While second-generation software has helped reduce the cost and improve the efficiency of some enterprises, it has done little to transform our physical world. Power, water, agriculture, transportation, construction and healthcare have barely been touched. But that’s about to change.

Industrial machines or enterprise things are increasingly being instrumented and connected. John Chambers, former Cisco CEO, says 500 billion things will be connected to the Internet by the year 2025. While you may question that, we already know 100,000 wind turbines are connected with the capacity to send 400 sensors’ worth of data every five seconds. So we’re going to end up with a lot of smart, connected things.

Unfortunately, all our connection, collection, analysis, learning, middleware and application technology has been built to support applications for the Internet of People. Things are NOT people. Things exist where people aren’t. Things have much more to say and things talk much more frequently. A Joy Global coal-mining machine has vibration sensors that sample 10,000 times per second. We need a new generation of enterprise application, middleware, analytic, collection and connection cloud service products to build precision machines for mining, transportation, healthcare, construction, power, water and agriculture.

Some have begun to make the investments. GE Software was founded in 2011 with a $1 billion investment. CEO Jeff Immelt has declared that GE needed to evolve into a software-and-analytics company, lest its industrial machines become mere commodities. Immelt has set an ambitious target of $15 billion in software revenue by 2020. GE plans to achieve this through its new Predix software platform under the leadership of CEO of GE Digital, Bill Ruh.

PTC has taken an M&A path and invested more than $400 million in a series of companies: ThingWorx for $112 million, a $105 million acquisition of ColdLight andAxeda for $170 million. On the venture side you may not have noticed, but Uptake, a Chicago-based IoT startup, beat Slack and Uber to become Forbes 2015’s Hottest Startup. They raised $45 million at a $1 billion post-funding valuation.

I’ll let you be the judge of whether it’s time to invest in IoT. But if you’re an early-stage or even late-stage investor, it would be wise to be a student of this area as it promises to create as big a disruption as the second generation of enterprise software. And if you’re a startup with a vision to build products for things, not people, get started. Maybe in 12 years we’ll talk about you like we now talk about VMware, NetSuite and Salesforce.

- Tim Chou is the former president of Oracle On Demand, a computer science lecturer at Stanford and chair of the IoT Track of the Alchemist Accelerator. His book, Precision: Principles, Practices and Solution for the Internet of Things, will be released in May.