Meet Morgan: why the future of AI is boring, regulated and wildly profitable
Morgan Blumberg is a partner at M13 known for a contrarian thesis on agentic workflow automation. She invests in AI that replaces painful back office work in healthcare and enterprise. Her portfolio spans NormAI, Maven AGI, Niural, Kontext, PIKL, RadiantGraph, Polimorphic, Canvas Medical, Ayble Health, Lantern, Play, Workmade and Vambe.
TLDR
A grounded razor-sharp focus on where agentic AI actually creates value.
- Partner at M13 with a contrarian thesis on agentic workflow automation that targets regulated, high-ROI back office work rather than hype-driven AI.
- Morgan’s portfolio spans healthcare and work automation and is unified by clear workflow owners and real economic stakes.
Select areas of expertise:
- Agentic workflow automation
- Healthcare operations AI
- FinOps and compliance automation
- Applied AI investing
Recent investments:
- NormAI
- Niural
- Maven AGI
- Kontext
- PIKL
- RadiantGraph
Morgan is one of the most exciting rising stars in venture for a reason. While much of the market is still chasing splashy demos and generalized AI platforms, Morgan has built a differentiated thesis around agentic workflow automation that replaces archaic, painful work in compliance, payroll, healthcare ops and financial infrastructure and delivers measurable ROI.
We sat down with Morgan to unpack her contrarian thesis and pattern recognition across her portfolio.
Before M13, you worked in finance, government, philanthropy and startups. How did that path shape how you evaluate AI and automation companies?
At Morgan Stanley, I covered regulated industries and business services. This included outsourced BPO work like call centers, recruiting, customer service, and administrative functions. At the time, much of that work was being outsourced to dev shops, IT firms, and overseas teams. I was doing M&A and IPOs for those businesses and saw how massive and entrenched these workflows really were.
After politics and philanthropy, I ended up focused on the future of work and what the next iteration of these companies could look like. When ChatGPT and LLMs came onto the scene, it blew the space wide open but I had already been deep in the categories that were historically outsourced. My background gave me a real appreciation for regulated, operationally complex industries and how hard they are to change, which is exactly what makes them interesting to me.
You are known for backing “boring” automation. What is the contrarian investment thesis that others are missing?
A lot of these industries are enormous, and they exist because the work simply has to get done in a business context. They are often old school, outdated, slow moving and dominated by legacy players, which scares people off. I think that is a mistake.
There is a huge opportunity to build generational companies by modernizing these spaces. Technologists often avoid them because they are not sexy so they tend to be dominated by industry insiders rather than people coming in with a fresh technical perspective. That creates an opening.
The complexity and old integrations that turn people off are a moat so it gives technologists who are genuinely interested in the nuance and complexity an edge. Industry examples are insurance, staffing, PEOs and compliance. Once you start investing in a few of these areas, like we did with NormAI and Niural, you realize how interconnected they are. Employment touches benefits, insurance, payroll, workers’ comp. When we invested in Norm.AI, few people saw compliance as an exciting space. Now everyone thinks compliance is hot. It was not an obvious use case at the time, and that is exactly why it was interesting.
What patterns show up across your investments?
A common theme across our companies is the founders. Most are second-time founders who had real success before in something adjacent, not identical. They learned from operating their own businesses and felt deep pain in their back office functions. That pain became the seed for a much bigger opportunity.
Niural co-founder Nami Baral ran and sold a company where hiring and accessing the right data was the biggest bottleneck, which led her and co-founder Nabin to build an automated HR and CFO suite. John Nay ran a tax automation business and experienced how painful compliance was firsthand. That insight led to NormAI, which now spans compliance and legal workflows. Anmol sold into payers at Teladoc and Livongo and learned how difficult distribution, engagement and retention were in healthcare, which informed how he built RadiantGraph, a mission-critical system of record for payers.
What excites me is not just the idea but the ambition to tackle less obvious spaces and the maturity to operate inside such complexity. Jonathan ran customer support. Sami built ranking and retrieval systems at Google News. Karson was trying to automate recruiting, which was the biggest cost center in her prior business. Each founder is finally able to go after big, boring, complex problems because the technology had caught up. They are ready for a longer, even harder slog.
You are deeply embedded in founder communities in NYC and SF and are known for your well-curated AI automation dinners. How does that translate into an investing edge?
I bring people together even when I know M13 will not invest or it is too early or too late for us because it creates real value for founders. Founders learn a tremendous amount from each other, especially across stages. It can be a lonely job, and there is a lot of value in trading war stories, frustrations and wins with people who are in it too.
My philosophy with venture is to be genuine and selfless and to give as much as you can. That long-term mindset compounds. I came to M13 because I helped someone years earlier without expecting anything in return, and that person later referred me when I was exploring venture. I have introduced people to their lead investor who was a better fit than us. Being transparent and honest matters because founders can feel played by VCs. They pick up on if you are authentic and genuinely trying to help. Especially second-time founders.
What’s an unexpected moment that reminded you venture capital is about people, not technology?
One of my favorite parts of venture is knowing how much you can help yourself by helping others over the long term. People gave so much of their time to me as I was making my way in venture. It always comes back around.
First-time founders often need credibility and are right to optimize for it. Second-time founders tend to optimize for trusted partnership, and we are earning that reputation. Founders remember who showed up when there was nothing to gain. They want partners who care about them as people, not just outcomes. My goal is to lift people up as they lift me up. When that happens, the relationship is not transactional. It is durable.
Articles by Morgan
TLDR
A grounded razor-sharp focus on where agentic AI actually creates value.
- Partner at M13 with a contrarian thesis on agentic workflow automation that targets regulated, high-ROI back office work rather than hype-driven AI.
- Morgan’s portfolio spans healthcare and work automation and is unified by clear workflow owners and real economic stakes.
Select areas of expertise:
- Agentic workflow automation
- Healthcare operations AI
- FinOps and compliance automation
- Applied AI investing
Recent investments:
- NormAI
- Niural
- Maven AGI
- Kontext
- PIKL
- RadiantGraph
Morgan is one of the most exciting rising stars in venture for a reason. While much of the market is still chasing splashy demos and generalized AI platforms, Morgan has built a differentiated thesis around agentic workflow automation that replaces archaic, painful work in compliance, payroll, healthcare ops and financial infrastructure and delivers measurable ROI.
We sat down with Morgan to unpack her contrarian thesis and pattern recognition across her portfolio.
Before M13, you worked in finance, government, philanthropy and startups. How did that path shape how you evaluate AI and automation companies?
At Morgan Stanley, I covered regulated industries and business services. This included outsourced BPO work like call centers, recruiting, customer service, and administrative functions. At the time, much of that work was being outsourced to dev shops, IT firms, and overseas teams. I was doing M&A and IPOs for those businesses and saw how massive and entrenched these workflows really were.
After politics and philanthropy, I ended up focused on the future of work and what the next iteration of these companies could look like. When ChatGPT and LLMs came onto the scene, it blew the space wide open but I had already been deep in the categories that were historically outsourced. My background gave me a real appreciation for regulated, operationally complex industries and how hard they are to change, which is exactly what makes them interesting to me.
You are known for backing “boring” automation. What is the contrarian investment thesis that others are missing?
A lot of these industries are enormous, and they exist because the work simply has to get done in a business context. They are often old school, outdated, slow moving and dominated by legacy players, which scares people off. I think that is a mistake.
There is a huge opportunity to build generational companies by modernizing these spaces. Technologists often avoid them because they are not sexy so they tend to be dominated by industry insiders rather than people coming in with a fresh technical perspective. That creates an opening.
The complexity and old integrations that turn people off are a moat so it gives technologists who are genuinely interested in the nuance and complexity an edge. Industry examples are insurance, staffing, PEOs and compliance. Once you start investing in a few of these areas, like we did with NormAI and Niural, you realize how interconnected they are. Employment touches benefits, insurance, payroll, workers’ comp. When we invested in Norm.AI, few people saw compliance as an exciting space. Now everyone thinks compliance is hot. It was not an obvious use case at the time, and that is exactly why it was interesting.
What patterns show up across your investments?
A common theme across our companies is the founders. Most are second-time founders who had real success before in something adjacent, not identical. They learned from operating their own businesses and felt deep pain in their back office functions. That pain became the seed for a much bigger opportunity.
Niural co-founder Nami Baral ran and sold a company where hiring and accessing the right data was the biggest bottleneck, which led her and co-founder Nabin to build an automated HR and CFO suite. John Nay ran a tax automation business and experienced how painful compliance was firsthand. That insight led to NormAI, which now spans compliance and legal workflows. Anmol sold into payers at Teladoc and Livongo and learned how difficult distribution, engagement and retention were in healthcare, which informed how he built RadiantGraph, a mission-critical system of record for payers.
What excites me is not just the idea but the ambition to tackle less obvious spaces and the maturity to operate inside such complexity. Jonathan ran customer support. Sami built ranking and retrieval systems at Google News. Karson was trying to automate recruiting, which was the biggest cost center in her prior business. Each founder is finally able to go after big, boring, complex problems because the technology had caught up. They are ready for a longer, even harder slog.
You are deeply embedded in founder communities in NYC and SF and are known for your well-curated AI automation dinners. How does that translate into an investing edge?
I bring people together even when I know M13 will not invest or it is too early or too late for us because it creates real value for founders. Founders learn a tremendous amount from each other, especially across stages. It can be a lonely job, and there is a lot of value in trading war stories, frustrations and wins with people who are in it too.
My philosophy with venture is to be genuine and selfless and to give as much as you can. That long-term mindset compounds. I came to M13 because I helped someone years earlier without expecting anything in return, and that person later referred me when I was exploring venture. I have introduced people to their lead investor who was a better fit than us. Being transparent and honest matters because founders can feel played by VCs. They pick up on if you are authentic and genuinely trying to help. Especially second-time founders.
What’s an unexpected moment that reminded you venture capital is about people, not technology?
One of my favorite parts of venture is knowing how much you can help yourself by helping others over the long term. People gave so much of their time to me as I was making my way in venture. It always comes back around.
First-time founders often need credibility and are right to optimize for it. Second-time founders tend to optimize for trusted partnership, and we are earning that reputation. Founders remember who showed up when there was nothing to gain. They want partners who care about them as people, not just outcomes. My goal is to lift people up as they lift me up. When that happens, the relationship is not transactional. It is durable.
Articles by Morgan
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