Transitioning to AI: My Story as An Entrepreneur and An Investor

Alex Ren, BoomingStar Ventures

Three years ago, when I first arrived in the Bay Area, I barely knew anyone here; And I barely knew anything about AI(Artificial Intelligence); I was new to everything about entrepreneurship and investing. So, how did I get here?

In the past three years, I have experienced three extremely rewarding career changes: the first was through my transformation from a big enterprise professional to an entrepreneur; the second was when I transitioned from a non-AI sales marketing professional to an AI entrepreneur and investor; and the third was my transition from an entrepreneur to an AI investor.

I definitely experienced a great deal of joy and pain during these career changes. Recently, quite a few people asked me for my suggestions on their careers. So I decided it was a good idea to conduct this sharing session. Today, we will focus on three career changes as well as AI related topics such as AI commercialization and investment. Unlike the majority of people today, I followed a different career path.

I graduated with an EE Master’s degree in 2003. I remember that I conducted some early research and experiments on something related to autonomous driving, which was Anti-collision Radar during my Master’s degree program. Back then, the technology was not what it is today. Today, we are using cameras, Lidar and Radar. However, before we only used Radar and it was really hard to identify different objects with digital signal processing technologies alone. But I soon realized that I really didn’t want to be an engineer and sit at the desk for 8 hours a day, 5 days a week.

I joined a company called Agilent and led their software sales in Southern and Eastern China. It was a great job, and within a short span of time, I became one of their top software salesperson across the globe during a nine-year career.

I gained a lot of exposure in sales and marketing and I realized that I had reached another career ceiling with my lack of knowledge in R&D, entrepreneurship or investment. Subsequently, in 2012, I got an opportunity to relocate here in order to lead a global business development team.

In 2014 and 2015, the LinkedIn mobile experience was considered to be really bad. I saw an opportunity and talked to some of my friends. We thought it was a good idea to build a mobile app to disrupt LinkedIn and we raised seed money from a venture capital firm called Bojiang Capital and started Linkr. Back then, we were not the only ones attempting the same thing, as there were many similar new mobile social networking startups.

We built a pretty cool app where you could directly apply to a job in the app and talk to recruiters and hiring managers. However, six months later, we realized the challenges of convincing people to accept a new social networking app. We were faced with slow user acquisition and decided to address the recruiting problem directly by pivoting to referral based recruiting. Even today, if you check Crunchbase or AngelList, you can find that more than fifty startups are still working on referral based recruiting.

However, we failed again. In my opinion, there is a fundamental dilemma with this model. The best referrers are individuals with a great network, but they are often quite busy or have no motivation to function as a referrer. Then, the program also acquires a lot of users who are actually not beneficial as they have spare time but their network is not that good. Eventually, if you do have a position to fill from your clients, you spend most of the time handling a ton of less qualified candidates. The model simply doesn’t work!

And then there was AI! I quickly realized there was a big talent shortage issue in the AI space and saw an opportunity to try AI recruiting. Even though I had never been a recruiter, I was always a great salesperson and loved to talk to people. As it turned out, I did fill several positions and it was fun. Thus, again we pivoted to an AI talent headhunting service, which we operationalized late last year.

As I mentioned, I barely knew anyone in the Bay Area when I moved here in 2012. However, I knew how to build my network quickly, so I referred many startups to my investor, Bojiang Capital and eventually I became their parter.

A little bit about Bojiang Capital and BoomingStar. Bojiang Capital is a 1.5B fund focusing on AI, Robotics, enterprise software and so on. BoomingStar is the name of its US fund.

This is a brief synopsis of my career path. Yes, there have been a lot of changes in the past three years. Let’s talk about my takeaways from these changes!

While preparing for this sharing session, yet another time, I opened my farewell letter from when I had quit the previous company. In it, I found this picture that I remember I had spend on this picture for over two months, introspecting in depth. I tried to figure out what my passion was, what I truly loved to do, and how I could do interesting things while also paying my rent.

I firmly believe that these four categories define a successful career path:

Do what you love, do it well, do what the world needs, and get paid for it.

Passion lies at the intersection of doing things you love and doing them very well. A good career path, or your calling, lies at the intersection of doing what the world needs and getting paid for it. If you are doing things that others need, doing what you love, and getting paid for it, it won’t feel like work. This is the holy trifecta of an ideal work life.

It’s important to do a little in-depth research before making a career change. From what I have learned from my successful career changes, there are four points to keep in mind:

  • First, it’s important to learn effectively. Yes, you can definitely spend five to six years trying to acquire a PhD degree, but I’d rather talk to smart people every day and learn from them rather than keep my head in books. Toward that effort, I have literally had conversations with more than 300 PhDs and AI experts in the past three years.
  • Secondly, you have to be honest and authentic with yourself and your friends to quickly gain their trust. We often deny the truth. I used to talk to an entrepreneur who raised about $2M with a bad idea. He burned them all in three years, but still pitched me the same idea. Come on, buddy! If your idea has already failed, think hard about the reason. Iterate quickly. Many times we are flattered by our investors and early adopters, who are usually our family and friends, and even our partners. “What a great idea!” “That’s cool! Go try it!” These words make us feel like we have already succeeded! Stay clear-headed and ask yourself if the product is truly ready to go to market.
  • The third point I learned from Peter Thiel. I think being a contrarian is essential to being an entrepreneur as well as an investor. Avoiding competition simply means not following others. So observe, and if existing players are still employing outdated technologies, which could be more than ten years old, it is time to try to build the next generation of technology. Build something new and start from a niche market, which eventually might become a blue ocean market.
  • The fourth idea is to capture the next wave of revolution, which is where I believe AI is today. Being a player in a new market can be tremendously advantageous to you when the market becomes mainstream, because the earlier you learn, the faster you will capture opportunities compared to latecomers.

When I became an entrepreneur, I often asked myself what a good idea for a startup would be. After three years in the startup life, I think I can summarize it with this chart.

The first angle is technology readiness.The technology should not be too early or too late. It has to arrive at the right time.

When I first made an investment, I often asked myself, what makes a good investor? Many people know that only 20% of investors make money. You don’t want to be among that 80% failure rate, so that is always my first priority. I believe a good investor is a contrarian. Also, many new graduates ask me how to enter the VC industry. I tell them you shouldn’t work in VC at the early stage in your career because you don’t have enough knowledge about how startups work and you also don’t have any domain knowledge to help you justify if the business model or technology is valid in that space.

As the founder of AI recruiting company, I often ask myself how to achieve the excellence of AI recruiting, I think these three points are critical:

  • First, because we are in the valley, we can take advantage of ever-evolving technologies to make our recruiting process more efficient so that we can handle high volume hiring.
  • Second, we really dig deep into each domain in AI. Our recruiters study hard and learn a lot from our candidates every day. And because I’m doing investment as well, we know a lot more than other recruiting agencies.
  • Third, our standard recruiting process is better. We don’t spam our candidates and try to be helpful to their AI career.

Here are some of our accomplishments. We helped Zippy fill 6 robotics positions in one week. We also introduced Zippy to Google Ventures, NEA, Lightspeed Ventures and other top VCs in the Bay Area and helped Immerex build up their product management, sales and BD teams.

Let’s talk about AI.

Prof. Nils Nilsson gave a great definition of AI, which is the

“Artificial intelligence is that activity devoted to making machines intelligent, and intelligence is that quality that enables an entity
to function appropriately and with foresight in its environment.”

We have three keywords here: “intelligence”, “appropriate function,” and “foresight in its environment”. That means we need to build an intelligent agent and embed it into the workflow, and make it adapt to the environmental changes. For autonomous driving, we need to build vision and perception components to track the environmental changes and update the map and policy changes.

AI is definitely getting a lot of traction this year. The battle for top AI talent only gets tougher year to year, according to a report from CBInsights.

Let’s look at the investment into the AI space. Machine learning related applications are still the majority of the investment focus, followed by other computer vision, language processing, autonomous driving and robotics related applications.

Let’s look at the different verticals which apply with AI technology. This is report from McKinsey. The X axis is the current AI adoption rate of one or more AI technologies at scale. The Y axis is the next 3-year change in AI spending. High tech, telecommunications, financial services, automotive and assembly, transportation and logistics are leading the adoption. While Healthcare, Education, Travel and Tourism are lagging behind.

Here is the general process to adopt AI technologies:

  • First, you have to figure out the use cases and sources of value to your business.
  • Second, build a data pipeline. Find enough useful data to train a model.
  • Third, you need to build a good model. Then embed this algorithm into the workflow.
  • Then work with external or internal partners as so on.

To name some of key AI applications, we can use AI to handle a lot of the data crunch work such as BI, IOT predictive maintenance, search recommendations and forecasting models.

For vision related applications, we can do autonomous driving, drone collision avoidance, E-commerce search, pick-and-place robots, or healthcare diagnostics such as detection of cancer. For language processing, we have chatbots, news and media content creation, smart home voice interfaces and text analytics and so on.

AI will bring dramatic change to the startup business model. Companies have to focus on data acquisition and how to better utilize the value of data to build insights. But again, back to real business. When you talk about an investment, you have to ignore AI first and analyze the basic business model to see if it works. Then check if AI can actually boost the business.

Many people ask me how I allocate my time between recruiting and investment. I actually don’t feel I’m doing two things. To me, talking to candidates will help me learn more about the domain and ecosystem. At the same time, doing due diligence and interacting with founders will also help us develop the recruiting business, because the number one thing they will do after fundraising will be recruiting. And because our recruiters reach out to more than 50 to 100 AI researchers every day, we also find many investment opportunities much earlier than even many other famous investors.

For the future, I’d like to be a mentor or helper to founders, career seekers and other friends. I think our good judgement of talent, and better use of capital will increase your odds of success.

TalentSeer is hiring Artificial Intelligence Recruiter, please send your resume to if you have interest!

About TalentSeer:

TalentSeer is an AI talent search company backed by Bojiang Capital. We are primarily focused on AI, robotics, cloud and FinTech. We fill mostly AI related positions such as machine learning, NLP, computer vision, mechanical engineers, vision and perception, robotics system engineers, etc. We have two departments in our company. One is any application related to computer vision or image, such as video surveillance, autonomous driving, robotics and Fintech. Another department is working on all applications related to speech or language, such as text analytics, Conversational AI, Speech and some applications in Fintech.

Currently, we have about 50 AI clients such as Vicarious, an artificial general intelligence startup focusing on solving the pick-and-place problem in robotics. We also serve autonomous driving companies such as Drive.AI,, Auto-X, delivery robotics companies like, and an apple picking robotics startup Abundant Robotics, which is funded by Google Ventures. Another client is VR startup Immerex, who aims to deliver a VR-based immersive entertainment experience. We also serve a speech recognition startup AISense, big enterprises such as Baidu and Ant Financial, and many others.

Here are some of our accomplishments. We helped Zippy fill 6 robotics positions in one week. We also introduced Zippy to Google Ventures, NEA, Lightspeed Ventures and other top VCs in the Bay Area and helped Immerex build up their product management, sales and BD teams.

About Alex Ren:

Alex Ren, Managing Partner at BoomingStar Ventures, Founder at AI recruiting startup TalentSeer

Alex is unique in the startup world. He’s an entrepreneur and a proactive investor in the AI space. In 2015, an interest in data science led him to start an AI-powered talent search firm, TalentSeer, which now represents one of the pioneering AI recruiting firms in the Bay Area. It helps more than 40 companies a year to build scientist teams in both AI enterprises and AI startups, Alex later launched a venture capital fund called BoomingStar Ventures (US fund of Bojiang Capital, a $1.5B fund focusing on AI, Robotics and Enterprise software) in 2016.

Prior to his current role, Alex worked at Agilent Technologies and he has over 15 years of experience in marketing across enterprise software, telecommunication, and semiconductor sectors. In Alex’s words, talent is the biggest driver of success, so TalentSeer and BoomingStar aim to capture both the best talent and investment opportunities to boost the market share.

Source: Medium

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