Black Angel Group Member Brandon Johnson on AI’s Potential, Challenges, and the Need for Inclusion
Welcome to a new series where we dive into the minds of leading innovators and investors shaping the future of technology. We’ll be hosting insightful conversations with Black Angel Group members and inspiring founders in our portfolio, exploring emerging trends, insights and best practices across topics from AI to Go-To-Market and beyond. With 300+ members to date, the Black Angel Group (BAG) is a collective of angel investors from various companies who specialize in investing in seed to Series A startups. We harness our extensive experiences and connections to facilitate the growth of these companies.
Today we’re featuring one of our BAG members: Brandon Johnson, Senior AI Solutions Architect at NVIDIA, a world leader in AI and accelerated computing technology. A tech trailblazer and emerging investor, Brandon provides a unique perspective to enterprises and startups doing large-scale AI model deployment and optimized inference microservices. Prior to NVIDIA, Brandon held roles in venture capital, engineering, and business development. Known for merging innovation with real-world applications, his passion and ability to leverage multidisciplinary expertise in AI, Cloud Computing, and cross-collaboration has led to over $2 billion in revenue for customers and partners.
AI Trends
What are the AI trends and applications you’re most excited about? What sector are you most interested in seeing AI revolutionize?
I believe every application will leverage a foundation agent or co-pilot. I believe every piece of software that exists today will become AI-enabled. Despite the proliferation of AI headlines in the media, I feel that AI is not fully democratized, but we are making meaningful progress thanks to AI researchers, AI practitioners, and innovative companies. Despite advances in Large Language Models (LLMs), I believe it is still difficult for the average person to use AI tools–chatbots like ChatGPT being an exception. OpenAI made it easy for anyone to interact with ChatGPT, but most people are still not interacting with these tools. We witnessed ChatGPT grow to 100 million users in two or three months–the fastest growing app in history at the time. Threads has since taken that title, although it’s not an apples to apples comparison given that Threads had direct integration to Instagram. Despite ChatGPT’s success, only 11% of Americans have used it and roughly 180 million people have used it worldwide. So I’m delighted to see AI become more democratized…which leads into the second part of your question.
I’m excited to see EdTech revolutionized by AI. Democratizing education will enable people to learn in a way that is best for them. There are many startups working in this space and they are doing great work. I believe AI can be a catalyst for increasing individual productivity, democratizing education, and increasing our nation’s GDP. EdTech platforms that educate people on how to upskill in AI will enable people to combine their domain knowledge with AI to create new products, new businesses, etc.
How has your experience at NVIDIA shaped your understanding of AI’s potential and challenges?
For most of my career, I’ve been in customer-facing engineering roles. As a Solutions Architect, I work with engineers at other companies to help them integrate their technology with NVIDIA’s to solve a problem. The scope of these problems vary from helping customers develop medical device platforms, interacting with industrial robots in manufacturing facilities, or using computer vision to improve customer outcomes. Many of the companies I am currently supporting are in the healthcare industry. The medical devices I am working with aid doctors with performing surgeries and identifying things like cancer. My time at NVIDIA has given me an even greater appreciation for AI because everyday I get to see how people are thinking about using our technology to build new products. Some customers come up with really creative ways to solve problems which is always a learning experience.
I also believe NVIDIA has accelerated my learning because nearly 90% of companies are training their AI models using platforms, software, and infrastructure. It’s humbling and exciting to know that NVIDIA is probably the only AI company in the world that works with every AI company in the world. So I have a greater appreciation for not only the technology, but NVIDIA’s leadership team. One benefit of being an industry leader is that you get to work with the great minds from the largest companies and VC-backed startups.
You mentioned you work with a lot of healthcare companies. Could you speak to some of the challenges you’re seeing in this space with their incorporation of AI and/or if there’s any potential you see at the intersection of healthcare and AI that gets you excited?
Yes, the healthcare industry is always challenged with adopting new technology while prioritizing privacy. In some regions regardless of industry–let’s say in the Middle East–there may be strict data sovereignty rules. I worked with the Kingdom of Saudi Arabia in the past and they have strict rules that prohibit data from leaving the region. Due to data sovereignty and the geopolitical battle with chip manufacturing, many companies want to build their own large language model rather than using tools like ChatGPT. Political leaders are realizing their national dependence is largely tied to their ability to maintain their data, its uniqueness, and generate intelligence from that data. They want to use their own data to capture the nuance of their culture, values, and language.
AI Integration
I’d love to dig deeper into your experience working with a variety of companies. Last year, we saw many organizations exploring AI and this year, we will see more deployment and integration. For businesses looking to integrate AI, what advice would you offer to ensure successful implementation?
The first would be to make sure there’s a clear ROI on the project. I can’t tell you how many times people tell me they want to use AI and I ask, well why? Well, we want to do XYZ. It’s important to have metrics to assess the outcome. The bottom line is: know what you’re measuring.
The second is: do you need AI to accomplish whatever you’re trying to do? You can use AI for many things, but do you always need to?
There are many more things to consider: Does your company have the resources and talent to support the AI project? Is the project sponsored by executive leadership? Oftentimes companies will experiment with something, and it won’t make it into production. Only about 13% of AI models actually make it into production, which means many companies are experimenting, but not reaping the full benefits of AI…yet!
What data is sensitive? Do you even have enough data? Is it siloed across your business or is it unified so your team can turn the data into intelligence? What ethical practices are in place to ensure you don’t unintentionally harm a group of people? How do you accommodate for bias and unconscious bias in your data? All of these and more, should be part of your data strategy before you decide to build anything.
Others would be: how do you scale this project, beyond just starting the project? How does this project get rolled out? If you are going to be iterating on this and you have a ton of data, do you have a cloud provider that you’re going to be partnering with or do you have the infrastructure locally to continue to train these models? That’s really important and you’re starting to see that come to fruition with the chip shortage over the last few years. Most chips are not manufactured in the United States. Most chips are manufactured by a company called Taiwan Semiconductor (TSMC). That being said, Biden passed the U.S. CHIPS and Science Act to bring back manufacturing to the United States. So if you are building an AI company, it’s necessary to understand the geopolitical nature of the chip industry. You need to have a solid GTM and resilient supply chain strategy and at least two cloud infrastructure providers.
AI Policy
2024 is an election year for many countries globally, and beyond that, the world is increasingly facing economic and geopolitical risks. How do you think about ethical AI and policy/regulations?
When it comes to ethics, before an AI model is developed and pushed into production, companies have an obligation to make sure that the datasets are diverse. Companies should proactively seek datasets from underrepresented groups to ensure models represent our society’s best interests. I truly believe that if we are intentional, transparent and introduce the right ethical policies, AI can mirror the good in humanity without amplifying the flaws.
Beyond AI
Outside of AI, are there specific initiatives or developments that you find particularly promising for the growth of the tech industry?
I’m excited that there is an increase in the number of retail investors. You see that with BAG. That’s important because oftentimes we want change, but we are not a part of the economic base to make that change. We [Black people] are often consumers of technology rather than investors in technology. We need more Black investors to understand the VC asset class. My hope is that the angel investors have a few wins, go on to be LPs at venture firms, acquire some wealth, and begin building companies. In BAG, we have operators from Google, NVIDIA, Microsoft, Tesla, AWS… That’s great, but can we gather those talented people in a room, incubate a project, de-risk the project, launch and scale the product into a global business, and have BAG members invest in it? That is a trend that I see coming–an increase in retail investors, more awareness about what it takes to actually invest in companies and then, long-term, building companies ourselves. I think that’s where we’re headed, and that is very exciting to me.
That’s a perfect segue to our last question. I’m curious to learn more about what drew you to the Black Angel Group and how has your angel investing experience been so far?
I reached out to Bonita Stewart from Gradient and I mentioned to her that I have an interest in investing and in AI. She mentioned BAG and so I joined immediately. Eventually, other members from NVIDIA joined BAG. Since I’ve joined, my experience has been great. I love the fact that they’re providing an opportunity for people to invest in this asset class. I think it’s also helpful that opportunities are “somewhat” de-risked because you know that you will be investing alongside top VC firms. I think that brand recognition could be extremely helpful, especially for someone who is newer to the space who hasn’t had formal experience performing deep due diligence on a deal.
