I interviewed four impressive leaders at the forefront of incorporating AI into product live at BuildGroup’s Annual Meeting this year:
- Ben Eachus (CEO) and @Jason Harbert (CTO) of Flowspace, which powers fulfillment for mid-sized merchants with software and a network of warehouses
- Mickey Alon (CTO/CPO) of VidMob, which measures and optimizes creative effectiveness for large brands.
- Liam DeCoste (CTO) of PressW, a consulting firm which helps companies adopt AI into their workflows.
Here are four takeaways from our discussion:
- Tech execs should focus on real ROI for the power user as opposed to just leveraging cool technology for its own sake. For Flowspace, the power user could be a single person at a DTC company with all of the responsibility of ensuring the right product gets to the right consumers on time at minimal cost. The ability to tell a system to “freeze orders from this bad batch of dogfood” can save hours of time and improve consumer happiness. VidMob’s power users of brand managers can instantly interpret what creative ads are appropriate for different audiences and ensure that all ads adhere to brand best practices without manually going through and scoring each.
- The next takeaway was written in large print at the Pablo Escobar museum: “Easy money doesn’t last”. Putting a simple wrapper around GPT4 and upcharging customers might make money in the short term, but is far less durable as a business model in the long term. The hard work of automating workflow and building a proprietary data asset bolsters a company’s ability to provide outsized value to users. In the land-grab that the latest AI wave has kicked off, founders should go after the high ground that they are uniquely capable of defending. For VidMob, this means leveraging 18 million analyzed videos and insights from the feedback loop of knowing which ads were successful. For Flowspace, this means leveraging millions of historical order history of addresses, warehouses, shipping times, and costs for individual merchants. The harder work of marrying proprietary data with Foundation Models yield enduring economic value.
- This Time Really Is Different: While prior innovative cycles in AI over the decades were followed by “AI Winters”, the current deep learning renaissance is different. Foundation Models are in only the early innings of quality and companies are already seeing significant savings in specific areas like customer support. Developers can leverage a host of off-the-shelf tools that didn’t exist a decade ago, and the ability for AI to generate new content is a step function above what was possible not long ago.
- While all agree OpenAI is here to stay, it is unclear who the overall winner will be. 3/4 panelists took a $100 bet that OpenAI will be worth over $100B in 10 years, but the first mover is not always the biggest winner. Accordingly it makes sense to plan for flexibility with multiple Large Language Models. All are focused on cost and speed, and the hardware paradigm that supports these models is shifting quickly.
You can check out the full video here.
The information in this blog post is provided in good faith without any warranty. It does not constitute investment advice, recommendation, or an offer of any services or products of BuildGroup Management and it is not intended to provide a sufficient basis on which to make an investment decision. This document is provided for educational purposes only. Discussions of current or former BuildGroup portfolio companies are intended for educational and discussion purposes only. Any portfolio company so discussed has been selected based on objective, non-performance based criteria.
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