Arynton Hardy, a wealth manager in Los Angeles, uses artificial intelligence daily.

"Almost every single meeting I have with a client, I utilize an AI summarizer that gives me notes, gives me follow-ups, gives me to-dos," Hardy said. His employer, Savvy Wealth, is a tech startup focused on providing AI innovations to its growing roster of financial advisors and high-net-worth clients.

Hardy said AI tools regularly save his team hours on data entry, portfolio monitoring, and other back-office tasks, giving him more time to meet with clients.

If I asked Dall-e to draw me a picture of a cat juggling nine balls in the style of a French impressionist, it would do so in seconds. And once done, that painting would be far better than anything I could possibly hope to do. But, it wouldn’t be perfect – because it would appear that Chat GPT’s artistic side is too right-brained to actually count balls.

Artificial Intelligence (AI) is advancing at an unprecedented pace, transforming various industries and redefining traditional roles. The mortgage intermediary profession will be no exception. As AI systems continue to develop, they are outperforming humans in specific tasks, compelling professionals to rethink their strategies and adapt to these technological shifts. If someone invents the steam powered weaving loom, learn how to work the loom, don’t be a weaver.

In this podcast episode, host Philip Guarino speaks with Andrew Lo, a distinguished professor at MIT and an entrepreneur with a focus on healthcare and deep tech ventures. Andrew shares his journey from academia to entrepreneurship, detailing his transition from economics to founding a quantitative investment firm and later exploring the financial challenges of drug development. He discusses the fundraising challenges for deep tech entrepreneurs, how investors’ risk calculation differs for more complex ventures and how this requires continuous investor communication and learning. Lo also highlights the vibrant innovation ecosystem in the Boston-Cambridge area and his passion for mentoring students in entrepreneurial projects.

A new MIT study by professor Andrew W. Lo and co-author suggested that while large language models (LLMs) like ChatGPT are fairly new developments for financial advisors, they could be trained to adapt to simulate both the predictive and empathetic behaviors of human advisors. "To achieve performance that rivals the best human advisors, we will need to incorporate some notion of selection, evolution, and fitness into the ongoing training of LLMs," the study said, and, "if LLMs can be made safer and more robust, they have the potential to transform our financial lives."

In a bid to overcome some of the commercial challenges faced by expensive new gene therapies, Quantile Health has raised $6 million for a financial platform that’s designed to spread out costs and widen access.

The company was co-founded by MIT professor Andrew Lo, whose ideas formed the intellectual foundation for the company BridgeBio, as part of his quest to change how drug development is financed.

An event study by Professor Andrew W. Lo, research affiliate Manish Singh, and co-author, shows that acquisition, product development, investment, regulation, earnings guidance, and analyst ratings significantly impact the stock prices of biopharmaceutical companies.

Professor Andrew Lo proposed a $30bn cancer "megafund" a decade ago to combine drug testing projects to diversify risk. The idea had already inspired smaller-scale applications at companies such as BridgeBio and Roivant, he said. "If we can change the incentives and reduce risk, the increased reward will bring more money," Lo said.

The study of finance can easily be split into two eras: before and after Harry Markowitz. The economist, who died on June 22, was one of the first academics to introduce abstract mathematical concepts to investment decision-making. "Everyone knew about diversification — not putting all your eggs in one basket," said professor Andrew W. Lo. "But Markowitz told us more than that. He told us how many eggs we should put in the different baskets, and how to diversify in a systematic way."

To crack the top five, Moderna will have to transition from a research-focused business to a multidrug commercial company without losing its science edge, said [professor] Andrew Lo. Only a handful of biotechs have successfully executed that maneuver, he said, citing the risk of setbacks in the lab and supply chains — all under the gaze of profit-minded investors. "In the parlance of Wall Street, it's always, 'What have you done for me now?' They've got a really large pipeline of interesting therapeutics. So the world is waiting to see what's next."

Prof. Andrew W. Lo says: "Even if a particular drug program is likely to be successful, and even if it generates a couple hundred million dollars a year in revenue, that might not be enough to justify putting the resources of the company to that drug because the same resources can be developing a billion dollar a year drug."

Prof. Andrew W. Lo says: "Very often when you're focusing on just one disease, by itself, it may not be economical to develop the particular therapeutic. But if you combine multiple diseases into a single financial portfolio, all of a sudden the risk-reward trade off becomes more attractive. If you can put together a large number of projects that are statistically independent — that their success and failure have no bearing on the other successes and failures — that's the best way of reducing risk in a portfolio."

In a 2021 study, for instance, three academics including [Prof.] Andrew Lo looked at what happened to innovation when drug companies were no longer able to resort to one of their favorite tactics: paying generic makers to stay off the market, known as "pay-for-delay." A 2013 Supreme Court ruling had essentially barred such agreements. What the researchers found was that companies actually responded by increasing their research and development spending to find new drugs.

New research published in the inaugural issue of the academic journal Collective Intelligence suggests that evolutionary forces may be fueling collective tendencies to discriminate.

New MIT research forecasts the effect of various two-dose vaccine allocation strategies on the cumulative number of infections and deaths in the U.S.—quantifying the impact of prioritizing first versus second doses. The forecasts suggest that allocating more than 50% of available doses to individuals who have not received their first dose can significantly increase the number of lives saved and significantly reduce the number of COVID-19 infections.