I’m not usually prone to hyperbole, but having spent time with a bunch of startups in the most recent Y Combinator batch, I feel like I’ve had a chance to glimpse the future. As a firm, we diligently meet over 200 YC startups a year which gives us a good sense of the Zeitgeist (embedded fintech —> web3 —> AI). But while flavors of the month typically fade, I have come to believe that
LLMs will transform a significant swath of business processes and that we are likely entering a golden age of B2B SaaS investing.
The conclusion above was informed by meeting with various LLM startups, so I want to start by giving some examples. From there, I try and draw some general conclusions about how LLMs can demonstrate value in the enterprise. Finally, I lay out some implications for founders building LLM startups that I see from my vantage point as an investor (still evolving!).
Examples of startups using LLMs to transform business processes
What struck me was how many startups across disparate industries had rapidly come up with 10x better solutions (which is a general benchmark that investors think about when investing in new companies). As the examples below show, many companies used LLMs to save businesses time, headcount, money, automate menial tasks and generate incremental revenue.
- Linc (AI copilot for freight brokers) -- freight brokers typically receive 1000+ emails per day which need to be manually entered into ERP systems. Linc’s tool automatically extracts relevant data from emails and syncs it with the ERP, saving brokers several hours per day of data uploads
- Casehopper (AI-assisted legal drafting) -- there are 17 million visa applications submitted each year, with the average application requiring 5 hours of an immigration lawyer (or paralegal’s) time. Casehopper reduces this drafting time down to 5 minutes, effectively replacing the paralegal in this process. For a small immigration law firm, this can result in a substantial increase in cases processed and revenue generated
- Revv (AI-powered vehicle sensor calibration) (*) -- all modern vehicles have sensors on them which need to be recalibrated when the car needs to be repaired. Sensor calibration requires technicians to review complex 100-page manuals (which differ for every make, model and year) to figure out the correct procedures to undertake. Revv’s software turns this multi-hour research task into seconds, allowing shops to capture substantial revenue without expending any effort
- AskLio (AI-copilot for procurement teams) -- internal procurement teams typically have policies and guidelines around software and equipment that their company can buy. By training an LLM on this internal corpus, they can significantly reduce the back-and-forth that happens between internal buyers and procurement teams, resulting in faster approvals and less bottlenecks
- Sweetspot (AI-powered search engine for government contracts)(*) -- companies that bid on government contracts do so through an antiquated and dysfunctional online portal called SAM.gov. Sweetspot uses LLMs to help businesses find relevant contracts in seconds, rather than spending hours digging through the government’s complicated online database
A screenshot of Revv’s product that shows what sensor calibration operations a body shop needs to perform. Sourcing this information manually would likely have taken 5+ hours of manual work, which technicians don’t have the time to do.
Generalizations about where LLMs are most valuable
To synthesize across some of the examples above, there are a few core skills that are well-suited to LLMs:
- Automating manual, repetitive work that can be clearly defined (e.g. Linc) — doesn’t have to be limited to text-based tasks
- Massively simplifying complex, text-based research and summarization tasks (e.g. Revv)
- Giving employees creative superpowers
- In some cases, replacing employees entirely (e.g. Casehopper)
- Sarah Tavel recently wrote a great blog post about how LLMs have the potential to not just augment, but replace, humans for certain tasks
Some implications (for founders and investors)
LLMs can power products that are so clearly better than the existing way of doing things, and can demonstrate rapid time-to-value and clear ROI in doing so. Some implications of this below:
All in all, it’s as good a time to be a B2B founder as I’ve seen since I first became an investor in 2018. Founders should think creatively about their wedge product and vertical, but can be emboldened in knowing that there are many 10x better solutions across a wide variety of industries to be built using LLM technology.
If you are a founder building business software powered by LLMs and this post resonates, I’d love to hear from you. My email is email@example.com.
* We at 1984.vc are invested in these companies