Ginkgo Bioworks(NYSE: DNA) is a biotech that's going big on artificial intelligence (AI). Between its need to perform core workflows with high efficiency, and its generation of tons of biological and manufacturing data that demand analysis, Ginkgo will likely find AI becoming more important to it over time.
That's especially true if it starts selling a key input that other biopharma companies need to use AI models of their own. Its impact under those conditions might even be revolutionary for bioscience as a whole. Let's dive in and appreciate why it's so uniquely positioned.
Data is fuel for AI
One of Ginkgo's goals is to become a business capable of ingesting a customer's requirements for an experiment or workflow, implementing the requirements and conducting the experiment in its highly automated wet lab facility, and then delivering the data set back to the customer, cleaned and ready for analysis. The point is for customers to get the scientific data they need at a lower cost and with much less fiddling around at the bench than if they used only their organization's resources.
Ideally, Ginkgo's profit margin would derive from its ability to generate a lot of inexpensive experimental data, due to its investments in adaptable laboratory automation technology, as well as AI and machine learning (ML) solutions for bioengineering. But the company isn't profitable just yet.
This business model is nothing new in biopharma; there are many contract research organizations (CROs) and contract development and manufacturing organizations (CDMOs) catering to a wide array of laboratory and biological manufacturing tasks. Much like a CDMO, Ginkgo's platform is equipped to take a client's biomanufacturing requirements and implement them, handing off the final product (whether it's a population of bioengineered yeast cells, or a vat of a specific protein or biomolecule). Once again, the idea is to off-load each customer's most painful processes and hand them their desired results at lower cost, with the company's return stemming from the efficiency afforded by its automation.
One thing that's different about Ginkgo's approach, and one of several elements that make it an AI stock rather than just a biotech stock: It wants to be able to extract so much information from a customer's experiments or manufacturing runs that it has enough high-quality and well-labeled data to contribute to training customers' specialized AI models, assuming they exist.
If this ambition is realized, it will mark a major revolution in biopharma, and biomedical sciences as a whole. And it's largely a matter of refining the relevant engineering to be more efficient until it's profitable. Here's why.
In the idealized form of the scientific method, researchers try to understand the impact of changing one variable, like the temperature in a laboratory incubator, on another variable, like a cell's rate of production of a certain protein. But when it comes to experiments involving cell biology, nobody believes that the variable being investigated is the only factor that's changing as a result of the investigator's actions.
Even something as simple as turning the heat up in the incubator leads to a huge number of subtle alterations in cellular activity, nearly all of which are invisible to the researcher, because it's far too cumbersome to use the laboratory techniques to evaluate those changes all at once.
At its best, Ginkgo's platform could make all of them visible and understandable. It could be as trivial as setting up another automated work-cell module for each of the additional analyses. Then it could pass off the comprehensive data set to customers, whose AI models could subsequently be trained to find rich relationships between variables that were previously thought to be unrelated.
This is the stuff that major breakthroughs are made of.
This revolution is still picking up steam
As amazing as realizing its data-generation service vision would be, for the Ginkgo of today, it might also be a death knell.
In Q2, it reported a net loss in excess of $217 million, most of which stemmed from its deeply unprofitable operations. It has not proven that it can execute the desired programs for its customers in a way that, on average, creates value for shareholders rather than destroys it. Providing very expensive services based on developing a comprehensive experimental data set, whether for training AI or any other purpose, will not contribute to the bottom line until the biotech drastically lowers its operating costs, and its efficiency increases substantially.
That makes Ginkgo a risky stock to buy right now.
Still, it plans to realize $100 million in annualized cost savings in 2024, and another $100 million in 2025. Management's target is to break even on its adjusted earnings before interest, taxes, depreciation, and amortization (EBITDA) by the end of 2026. In the second quarter, its adjusted EBITDA was a loss of $99 million -- worse than the same quarter a year earlier, when it had a loss of $80 million. And if Ginkgo really is going to foster a scientific revolution in biopharma, it needs to keep cutting costs while continuing to onboard more customers.
Investors should get a better picture of whether Ginkgo's ambitious plans will come to fruition within the next 12 months. Until then, it's best to hold off on a purchase.
Should you invest $1,000 in Ginkgo Bioworks right now?
Before you buy stock in Ginkgo Bioworks, consider this:
The Motley Fool Stock Advisor analyst team just identified what they believe are the 10 best stocks for investors to buy now… and Ginkgo Bioworks wasn’t one of them. The 10 stocks that made the cut could produce monster returns in the coming years.
Consider when Nvidia made this list on April 15, 2005... if you invested $1,000 at the time of our recommendation, you’d have $731,449!*
Stock Advisor provides investors with an easy-to-follow blueprint for success, including guidance on building a portfolio, regular updates from analysts, and two new stock picks each month. TheStock Advisorservice has more than quadrupled the return of S&P 500 since 2002*.
*Stock Advisor returns as of August 26, 2024
Alex Carchidi has no position in any of the stocks mentioned. The Motley Fool has no position in any of the stocks mentioned. The Motley Fool has a disclosure policy.
