Not really at all. IMO it is all hype.

Plug-and-play bioinformatics doesn’t currently exist and I’m unsure how you could get around this using AI.

The only market that I see is giving lazy pure wet-lab scientists a black box that gives them results. Sure there might be a massive edge-case bug in the code but they don’t have the expertise to notice anyway. But hey here is an excel file with fold-changes and p-values. And look we even ran GSEA for you!

Any competent technical scientist can already do these things themselves in galaxy without tons of experience.

The wet-lab field is changing and all genomics/bio PhD students at top institutes are expected to have an intro level understanding of programming and are generally expected to be able to analyze their own data. I don’t think the pure “wet-lab” scientist role will continue to exist outside of technician careers.

At this point even ChatGPT’s cheapest plan can already guide users to a basic best practices script. So what extra benefit does a random vibe coded SaaS bring ? Probably nothing. Ok, maybe you have analysis that needs to be done at massive scale and you don’t have the local compute power to do that. But is this really an AI-based technology or would this just be essentially paying for an automated AWS wrapper?

As someone that created a (now) scale-up stage biotech (albeit not in traditional drug discovery), the idea of just funnelling some data through a third party "AI" pipeline never once crossed our minds.

Horses for courses, but for us it was important that the knowhow was _inside_ the company and that we grew the capability ourselves.

I'm inclined to also believe its mostly just hype from speaking to a lot of my big pharma contacts. Many of them have partnered with or acquired AI companies promising to be the next big step change in drug discovery and as far as I can tell almost nothing has materialised. Bear in mind these are organisation with the depth of talent and capital, coupled to the actual wet lab capabilities, to make the most of these tools, and yet I think they've still underdelivered on the whole.

One caveat is that a lot of those companies were among the 'first wave' and some of the newer AI DD companies are maybe in a better position, but even now I think only in the last year or two have the first truly "AI designed" drugs entered the clinic, and success is far from certain/obvious still.

So, given that the best possible alignment of the stars in terms of tech, talent and capital is still barely making inroads in my estimation, I fail to see how an informatics consultancy can just 'plug in' to mid-size biotech and make much of a difference.

Moreover, if you did have an incredible AI that was capable of churning out winners - why would you be giving that to small companies when you could raise some money and develop them yourselves?

The parallel that comes to my mind is the dotcom boom. Everyone understood that the internet creates tremendous opportunities and values. Rushing ahead they was enormous investment into applications that could not have possibly worked.

But in parallel to that some (many) companies did prosper. And smart, motivated, and determined individuals prospered the most. Think of the startup culture that came after. Think of all the products that we take for granted today - just about all of them are by companies that did not exist during the dot com boom.

The internet was a massive equalizer; everyone could open a shop to sell a product, and anyone could write and disseminate a book. You can now be an actor, an avid hiker, a geoguesser or a YouTube weather forecaster with an audience. The internet broke down the moats of publishers, gatekeepers etc.

I hope the AI ends up being a similar equalizer.