Straight Talk About AI

Jan 26, 2024

Q&A with Kerty Levy, managing director for Techstars Boston Accelerator

Is AI its own vertical or is it going to impact all industries?

It’s going to impact all industries. Everyone is figuring out how to leverage artificial intelligence to make their businesses and initiatives move faster, more efficiently, and with greater insight. 

What industries are the most immediately impacted?

It’s hard to pick just one. But we’ve seen healthcare jump on board leveraging AI to speed up the process and more accurately predict diagnostic images. AI can analyze data and generate a report that doctors can use to treat people. And while doctors or scientists will still use wisdom and experience to make an informed decision, the data could be presented in a way that allows them to see the answer much sooner and more clearly. Another example is in biotech. AI can impact the speed at which new compounds can be identified and discovered, to identify cancer cures, or to make plants grow faster and more robustly. 

One of our recent Techstars portfolio companies is using AI to identify optimal mineral drilling sites for the much-needed rare metals that power green energy. Another is helping construction contractors save hours by generating a bill of materials with a single scan of a PDF or blueprint. Security, customer service, compliance, and more effective lead generation are just some of the enterprise solutions we’re seeing and investing in. 

Which industries will take longer to adapt use cases for AI? 

I think AI is going to be ubiquitous but you’ll see some resistance in GovTech. Government entities may take some time to trust AI and will have to jump through an untold number of hoops and hurdles to prove anti-bias, accuracy, data security, etc. AI tools will be used in education for sure, similar to the way calculators and other software have been leveraged, but people will still want human teachers in the classroom. Another interesting area of AI advancement is in the mental health and loneliness epidemic space. There just aren’t enough professionals out there to serve the needs of the population. It’s going to take quite a while, however, for humans to trust AI bots to cure their mental illnesses.  

What are fears or misconceptions out there that people have about AI?

People think AI is going to take away their jobs. That it’s going to take away the judgment that we, as humans, have and that it’s going to make decisions that don't take into consideration biases and have hallucinations that render the decisions inaccurate. For the foreseeable future, we’ll still have humans overseeing and checking the output of AI. Displacing jobs that require creativity and complex problem solving is still something far off in the future, but repetitive task-oriented jobs are already being displaced. Companies looking to manage this reality are retraining their workforces to be able to prompt AI and interpret the output instead of having people do what AI can do much better and faster.  

What would you caution founders of in regards to adapting AI into their own solutions? 

Well, let me flip that question around. I ask every founder who comes through Techstars to tell me what their AI strategy is. Because something, whether it's to operate more efficiently, or find a less costly or faster way of doing something, exists if they leverage AI.

Why is data so important and how does it affect the performance of AI?

AI operates by leveraging Large Language Model systems, or LLMs. The most famous of those is ChatGPT, which has grown rapidly since its launch in late 2022 and is now estimated to have over 180 Million users. Multiple startups are wrapping LLMs with industry or function-specific interfaces, leveraging the data for all kinds of AI use cases. The data in Open AI’s ChatGTP comes from data on the entire internet. As you can imagine, some of the data out there is inaccurate and may lead to hallucinations. LLMs leveraging private data are rapidly being deployed within enterprises to use correct or cleaner data, but sometimes there is not enough data included to be able to solve a broad base of issues. Enterprises are rapidly realizing, too, that their data is gold and they’re going to be more and more reluctant to share, no matter how anonymized it is, unless there is significant income associated with it. Interesting times ahead for the tech industry!