Areas of Interest, March 2021

I used to write more posts or essays covering ideas, trends, and themes that were compelling to me in the moment. In the current market, there may be an abundance of capital, but I’ve also found a plethora of startups and entrepreneurs excavating in exciting places I would never have considered. This has pushed me to be more reactive to the broader ecosystem, setting aside the usual presumptions. Nevertheless, I’ve always enjoyed taking a step back to publicly share what I’m thinking about to see if my interests (usually quite obscure) are aligned with anyone else’s.

The interplay between code and visual workflows

Others have written about this, but more enterprise processes and workflows are increasingly defined in code, turning configuration files and scripts into templatized, repeatable logic. Examples include cloud native security (Open Policy Agent), business operations (Salto), and infrastructure provisioning (Terraform). By a different token, I’ve also seen new projects that help users define, create, or interact with various processes through visual builders. Terrastruct in software architecture or Tonkean in process automation come to mind. The historical knock against visual workflow tools (WYSIWYG website builders like Squarespace come to mind) is that they wouldn’t be able to sufficiently capture the complexity of the system in question, especially the edge cases. Now, “code” can act as an escape hatch when a UI isn’t enough. Design tool Plasmic is an example. State machines are also an avenue to explore. My underlying thesis is that many parts of the enterprise (from website to API design) can be produced through the combination of visualizations and code, not isolated to either or.

Industrial biotechnology

What would Dow or DuPont look like if they were started today? Multinational chemical companies like these produce products that touch many parts of the economy, from agriculture to materials to therapeutics. Yet, the chemical industry is one of the most energy intense in the world with significant carbon contributions. Alternative solutions with the potential to be cheaper, safer, and more sustainable have begun to emerge. They’re typically defined by biosynthesis, where enzymes catalyze reactions to create compounds, standing in opposition to the legacy chemical processes. Enzymes have been part of industrial processes in various forms for some time, but companies like Solugen now use computational protein design methods to identify specific catalysts. Pow is another interesting platform in this space. Traditional software venture investors dipping their toes into biotech can be a slippery slope, but the expansive possibilities that come as a result of decreasing cost curves, along with the decarbonization of a global engine industry, excite me.

Machine learning systems as an enterprise attack vector

In the enterprise, new technologies bring forward the promise of business acceleration, but they also introduce the possibility of new attack vectors. In turn, this domino effect contributes to the emergence of new security businesses. This was evident in cloud security (Lacework, Orca) or IoT (Armis) a couple years ago, and now has taken hold in the API space (Salt, Noname). I’m the first to admit “machine learning” is a loaded term, and there exists some debate about the level to which ML platforms have penetrated into enterprise accounts. However, over the next decade, ML systems will increasingly go into production as the largest banks & financial institutions will use it to fight fraud, retailers & grocers will deploy personalized recommendations for their customers, industrial companies & manufacturers will optimize supply chains...the list could go on. These activities only increase the surface area for misuse and cyberattacks, with one example being adversarial machine learning. The risk is so pronounced that leading research institutions and tech companies co-published the Adversarial ML Threat Matrix. Robust Intelligence is a startup working on defending operational AI systems. 

If you’re interested in exploring any of these topics or working on something in one of these categories, I’d love to chat. Email me at aashay [at]