3043: Transforming Biotech R&D with AI and Cloud: Benchling's Mission to Accelerate Discovery

[00:00:04] [SPEAKER_01]: How is AI transforming the biotech industry? And are current R&D systems equipped to keep

[00:00:11] [SPEAKER_01]: up with a rapid pace of AI-driven innovation? Well today I'm going to be joined by the CTO

[00:00:18] [SPEAKER_01]: at a company called Benchling, the R&D cloud platform that is helping the biotech sector

[00:00:24] [SPEAKER_01]: unlock its full potential. And Steve has got to be bringing more than 20 years of experience

[00:00:28] [SPEAKER_01]: in the software industry, including roles at Motorola and Groupon where he built cloud

[00:00:35] [SPEAKER_01]: and SaaS platforms that enhanced organizational efficiency. But today his mission at Benchling

[00:00:42] [SPEAKER_01]: is modernizing drug discovery and development processes with technology that can handle the

[00:00:47] [SPEAKER_01]: speed and scale of AI's impact on R&D. So we're going to discuss how AI is applied

[00:00:53] [SPEAKER_01]: to drug discovery, the role of cloud technology in accelerating innovation and why data quality

[00:01:00] [SPEAKER_01]: and accessibility are key challenges for biotech companies today. So how can we ensure that AI

[00:01:07] [SPEAKER_01]: isn't just generating drug candidates but also transforming the entire R&D platform?

[00:01:13] [SPEAKER_01]: Well let's dive straight into this conversation with my guest right now.

[00:01:18] [SPEAKER_01]: So a massive warm welcome to the show. Can you tell everyone listening a little

[00:01:23] [SPEAKER_00]: about who you are and what you do? Hi, my name is Steve DC. I am the CTO here at Benchling

[00:01:30] [SPEAKER_00]: based in the Bay Area so at Benchling I'm responsible for our software engineering teams

[00:01:34] [SPEAKER_00]: as well as our security teams as well. So that's me.

[00:01:39] [SPEAKER_01]: Awesome. And I suspect you've seen the industry evolve so much throughout the years.

[00:01:44] [SPEAKER_01]: I mean the last two years allowed so many big changes but can you tell me a bit more about your

[00:01:48] [SPEAKER_01]: experience in the SaaS industry, some of the things that you've seen there?

[00:01:52] [SPEAKER_00]: Sure. So my background was originally in the software at the intersection of software

[00:01:56] [SPEAKER_00]: and infrastructure. I spent 10 years at EMC before doing a few years in advanced development

[00:02:01] [SPEAKER_00]: at VMware. Most recently before Benchling I was at Atlassian where I was running all of

[00:02:06] [SPEAKER_00]: the platform and infrastructure teams and then later some of the product engineering for

[00:02:09] [SPEAKER_00]: things like Jira, Confluence, Trello all those products that hopefully you're using every day.

[00:02:14] [SPEAKER_01]: I've got to ask I think most people listening in the corporate world especially corporate America

[00:02:19] [SPEAKER_01]: they know all about software as a service and have preconceptions on what that means. So I've

[00:02:24] [SPEAKER_01]: got to ask what is the main difference between SaaS in biotech compared to other industries and

[00:02:30] [SPEAKER_01]: what was it that motivated you to make that transition into a more specialized field like

[00:02:35] [SPEAKER_01]: biotech? I feel like there's a bit of a missing jigsaw piece or an origin story there. What's the story?

[00:02:42] [SPEAKER_00]: I would have said the same at the start but I think coming into it and learning very much

[00:02:47] [SPEAKER_00]: it makes more sense. So one of the challenges in biotech is just the rate of change in the

[00:02:53] [SPEAKER_00]: modalities and the science. So if you think about in any one of the dimensions,

[00:02:58] [SPEAKER_00]: the amount of research that's happening both in academic and just in commercial settings.

[00:03:03] [SPEAKER_00]: So the pace of change is immense and to support that the software needs to keep up or even be

[00:03:09] [SPEAKER_00]: ahead and anticipate these changes so the scientists have the tools they need to really get the

[00:03:15] [SPEAKER_00]: therapies they're working on or whatever projects they have to market as quickly as possible.

[00:03:19] [SPEAKER_00]: And so that's different from let's say a very horizontal space where maybe there's broader

[00:03:24] [SPEAKER_00]: trends you can tap into, you can see things emerging in a larger audience and so on.

[00:03:29] [SPEAKER_00]: And then I would say the other difference is the tools we provide really have to be fit for

[00:03:33] [SPEAKER_00]: purpose. So we are working with scientists, their job is they don't come in in the morning fired

[00:03:37] [SPEAKER_00]: up to work on software, they come in in the morning to work on experiments to collect data to do the

[00:03:43] [SPEAKER_00]: analysis and so our tool really needs to work out of the box for their deeply scientific use

[00:03:48] [SPEAKER_00]: cases. And so that's one of the things that I think is different about a highly vertical solution

[00:03:53] [SPEAKER_00]: versus a more horizontal. I would say some of the similarities are in this space there's a lot

[00:03:59] [SPEAKER_00]: of transformation happening whether it's digital transformation to the cloud with all the AI

[00:04:03] [SPEAKER_00]: changes that have been happening over the last couple of years and maybe that's the part that

[00:04:08] [SPEAKER_00]: drew me in. So coming out of the pandemic when you think about, you know, I don't think many

[00:04:13] [SPEAKER_00]: people knew what mRNA was prior to COVID vaccines coming out and so on whereas Benchling had

[00:04:19] [SPEAKER_00]: I just learned this week our first CRISPR tools were in scientists since 10 years ago. And so we've

[00:04:24] [SPEAKER_00]: been in this space for a while but seeing the explosion over the last few years made me more

[00:04:28] [SPEAKER_00]: aware of this space and got me interested in what's happening both in the science, it's happening

[00:04:34] [SPEAKER_00]: in every industry, it's happening in every region around the world. And so that got me interested

[00:04:39] [SPEAKER_00]: in having conversations with a company like Benchling. And then when I learned what was

[00:04:43] [SPEAKER_00]: happening around scale performance technology solving problems for customers, that's an area

[00:04:49] [SPEAKER_00]: that I have a lot of experience with. And so I was able to bring that experience in while still

[00:04:54] [SPEAKER_00]: learning let's say the life sciences technologies and science and business which I didn't have.

[00:04:59] [SPEAKER_00]: And so for me that that theme of being able to grow myself while also feeling like I can

[00:05:05] [SPEAKER_00]: have impact was what made biotech in particular a perfect fit for me.

[00:05:11] [SPEAKER_01]: And we've mentioned biotech there and I'm conscious to be a few people listening

[00:05:14] [SPEAKER_01]: are hearing about Benchling for the very first time. So for those people listening,

[00:05:19] [SPEAKER_01]: can you just expand on what Benchling does and also how pharmaceutical companies in particular

[00:05:24] [SPEAKER_01]: and biotech firms are using your services? Sure. Benchling's mission is to unlock

[00:05:31] [SPEAKER_00]: the potential of biotechnology. And generally that's in the pharma space but it's also

[00:05:37] [SPEAKER_00]: emerging in a whole bunch of other verticals as well. So it's happening in consumer goods,

[00:05:42] [SPEAKER_00]: industrials, a whole plethora of places around the world. And so the main mission of the Benchling

[00:05:48] [SPEAKER_00]: product then is to provide a single platform on which your scientists can work, providing very

[00:05:54] [SPEAKER_00]: scientifically aware software but also a central source of truth and a backplane for your

[00:05:59] [SPEAKER_00]: collaboration, your data platform and integrations with all the other tools you use on a day

[00:06:05] [SPEAKER_00]: to day basis. And so with that what we find is our end users are coming in, they're working on

[00:06:12] [SPEAKER_00]: their science within Benchling, they're using our modified modality software to help them on a day

[00:06:17] [SPEAKER_00]: to day basis. That's in what we call the wet lab which is in the you know you typically think

[00:06:22] [SPEAKER_00]: of scientists in the lab with coats and pipettes and so on. And then the dry lab is where

[00:06:26] [SPEAKER_00]: the science and the AI and the ML might be happening to say take all of the data that's

[00:06:31] [SPEAKER_00]: happening from those experiments or form analysis and see if there's ways that you can either

[00:06:35] [SPEAKER_00]: optimize or improve to get to an outcome faster. And so Benchling helps with that entire loop.

[00:06:41] [SPEAKER_00]: When you think about our customer base we have about over 1200 customers around the world and

[00:06:47] [SPEAKER_00]: while they're everything from startups to Fortune 500 we have some of the largest

[00:06:52] [SPEAKER_00]: pharma companies in the world, they're primarily in pharma but they're in other

[00:06:55] [SPEAKER_00]: verticals as well. And then in addition to the commercial offering that we have we also

[00:07:00] [SPEAKER_00]: have a free version of Benchling that we make available for academics. So in biotech and

[00:07:04] [SPEAKER_00]: in this space there's so much research happening in universities and collaboration happening

[00:07:09] [SPEAKER_00]: that we want to make those tools available to academics as well. And so there's a free version

[00:07:13] [SPEAKER_01]: of Benchling that's available there too. And we've both done incredibly well here,

[00:07:17] [SPEAKER_01]: we've lasted 10 minutes into a tech podcast without mentioning AI but predictably AI has

[00:07:22] [SPEAKER_01]: also been held as somewhat as a game changer in the drug discovery industry. So I'm curious

[00:07:28] [SPEAKER_01]: cutting beyond all that hype from your perspective why do you think it still is one of the

[00:07:33] [SPEAKER_01]: best places to apply technology in biotech right now? I would start with the human impact.

[00:07:40] [SPEAKER_00]: If you think of AI as a way to accomplish a goal rather than a goal in and of itself,

[00:07:45] [SPEAKER_00]: if you think about the impact of the drugs and the therapies that are shipping,

[00:07:49] [SPEAKER_00]: it's saving lives, it's improving quality of life, it's hitting climate change. So it's hitting

[00:07:54] [SPEAKER_00]: all of the things that you would want to when you're applying technology for

[00:07:59] [SPEAKER_00]: huge impact in the world. I think within biotech then we're seeing two main applications of that.

[00:08:06] [SPEAKER_00]: One is a very domain specific world, so bio AI models like Alpha fold or even Bio Nemo from

[00:08:12] [SPEAKER_00]: Nvidia which are primarily around drug discovery. So how can you find better leads?

[00:08:19] [SPEAKER_00]: How can you develop drug candidates faster and better with all of the data and technology

[00:08:24] [SPEAKER_00]: that's out there? So that's where you see a lot of the bio models starting to emerge,

[00:08:28] [SPEAKER_00]: actually just yesterday we did an announcement with Nvidia around how to set up platforms to

[00:08:34] [SPEAKER_00]: support these kinds of models. The second area then which is probably more general purpose in

[00:08:40] [SPEAKER_00]: terms of the application of the technology is just helping scientists be more productive in their

[00:08:46] [SPEAKER_00]: day-to-day work. So some of the numbers we see are you know bringing a drug to market is many

[00:08:50] [SPEAKER_00]: years, it costs many billions of dollars and it is a super high failure rate. So anything you

[00:08:57] [SPEAKER_00]: can do to reduce the cycle time in that is a huge benefit to ultimately the end result of getting

[00:09:04] [SPEAKER_00]: therapies out to two patients faster but also helping customers develop their pipelines faster as well

[00:09:10] [SPEAKER_00]: and potentially weeding out things that are not working. And so we see that balance between the

[00:09:15] [SPEAKER_00]: bio models of drug discovery where we are helping and enabling our customers to that

[00:09:19] [SPEAKER_00]: and then the productivity pieces which are really happening deeply within our product

[00:09:23] [SPEAKER_00]: and we think the combination of those can be real game changers for not just us but the entire industry.

[00:09:29] [SPEAKER_01]: And as this is a tech podcast I'd love to take a little look under the hood for a moment

[00:09:35] [SPEAKER_01]: some of the technology that makes all this possible so what role do you think cloud

[00:09:39] [SPEAKER_01]: technology is playing in enabling AI driven drug discovery and indeed development how are

[00:09:46] [SPEAKER_01]: these technologies complementing each other to deliver what we're seeing here? Sure and

[00:09:50] [SPEAKER_00]: Benchling is you know a company that was born natively on the cloud and so one of the benefits

[00:09:56] [SPEAKER_00]: is the ability to rapidly turn the innovation we have at Benchling into usable features and

[00:10:02] [SPEAKER_00]: improvements in our customers hands. So that lead time from innovation to use is super, super

[00:10:08] [SPEAKER_00]: short in the cloud world and so it's something that we take advantage of all the time. The second

[00:10:13] [SPEAKER_00]: one then is just scale. The scale of infrastructure I'm old enough Neil to remember

[00:10:18] [SPEAKER_00]: when you know when I was writing software years ago you would put in a ticket you would wait six

[00:10:23] [SPEAKER_00]: months to get it you know a physical machine assigned to you and then you would start working on it

[00:10:28] [SPEAKER_00]: and now it's you know it's instantaneous so the ability to scale up and down with the size of

[00:10:33] [SPEAKER_00]: the data with the type of model to really optimize for speed throughput collaboration. So

[00:10:39] [SPEAKER_00]: really what you're doing is you're optimizing your infrastructure for your business needs over

[00:10:43] [SPEAKER_00]: let's say trying to manage throughput of a physical data center and things like that which are important

[00:10:49] [SPEAKER_00]: but are not core and strategic to your business. And so it helps you really focus where you want

[00:10:53] [SPEAKER_00]: to be in your business which is working on your outcomes rather than working at really good to

[00:11:00] [SPEAKER_00]: manage infrastructure. And then what you're also doing is you're delegating some of those

[00:11:05] [SPEAKER_00]: specialized roles whether it's security compliance two teams that do this all day every day

[00:11:11] [SPEAKER_00]: and so you're able to take advantage of all the investments there without necessarily having

[00:11:15] [SPEAKER_00]: to stand up large investments yourself. And so Cloud really has just transformed the speed at

[00:11:20] [SPEAKER_00]: which companies and customers can use software absorb the software but also focus on the things

[00:11:26] [SPEAKER_01]: that are most important to them. Love that and obviously AI gets all the headlines right now but

[00:11:34] [SPEAKER_01]: curious right at the heart of all this is data. So how is Benchling helping organizations

[00:11:40] [SPEAKER_01]: to manage those vast amounts of data required to deliver much of what we're talking about here today?

[00:11:49] [SPEAKER_00]: We mentioned at the start I think in you and I about AI and where it's going it's hard to

[00:11:55] [SPEAKER_00]: believe it's only coming up on two years since chat GPT really kind of blew the doors open on

[00:12:00] [SPEAKER_00]: an interface to the the gen AI and the LLM technologies that were happening

[00:12:05] [SPEAKER_00]: but AI is much bigger than gen AI. Gen AI is absolutely an aspect of that and in that AI space

[00:12:11] [SPEAKER_00]: a lot of the traditional challenges are still there you need access to data so you have data

[00:12:17] [SPEAKER_00]: that is spread out through a number of applications be they legacy or third party

[00:12:22] [SPEAKER_00]: you need to you know provision access to that make sure people have access to the things they

[00:12:26] [SPEAKER_00]: need is the data high quality is it curated is it in a state that I can use in my AI models

[00:12:32] [SPEAKER_00]: or in the applications that I'm building on top so a lot of the traditional challenges that

[00:12:37] [SPEAKER_00]: may be existed for AI and ML usage are still there today and to some degree or even higher priority

[00:12:43] [SPEAKER_00]: because a lot of the newer models and newer tools will take the data you have and give you an answer

[00:12:49] [SPEAKER_00]: and so you need higher quality answers because the ease of using these tools is getting you know

[00:12:54] [SPEAKER_00]: better and better and so what we see is within Benchling outside of AI just in the

[00:13:00] [SPEAKER_00]: general use of our tool customers spend a lot of time modeling their science modeling their

[00:13:06] [SPEAKER_00]: workflows and processes and so inherently in that is the creation of high quality data structure

[00:13:13] [SPEAKER_00]: teams are working across the organizations to work on the same platform which means your

[00:13:17] [SPEAKER_00]: data is in a single place so from an AI perspective now you look at something like that and you say

[00:13:22] [SPEAKER_00]: I have the data in a certain place I know how it's modeled I have lineage I have context

[00:13:28] [SPEAKER_00]: I have all the metadata I need so it's a fantastic way now to enable rapid value from your AI

[00:13:33] [SPEAKER_00]: investments and so we see customers I'm Sanofi is a great example Sanofi is one of our European

[00:13:40] [SPEAKER_00]: customers and we've Sanofi last year actually their CEO came out and said they want Sanofi to

[00:13:45] [SPEAKER_00]: be the first drug company powered by AI at scale we've been working with Sanofi now for

[00:13:51] [SPEAKER_00]: three years where we went through a lot of legacy applications across 30 teams globally

[00:13:57] [SPEAKER_00]: and we've been able to collapse that down to run on Benchling now and they've seen

[00:14:01] [SPEAKER_00]: you know really significant improvements in their throughput in their quality because of that investment

[00:14:07] [SPEAKER_00]: in the data availability the data structure and using something like Benchling on the back end

[00:14:12] [SPEAKER_00]: then to make sure that it's all in a single place so we really see ourselves as being able to

[00:14:16] [SPEAKER_00]: drive that kind of AI capability with customers particularly as this is now top of mind for

[00:14:22] [SPEAKER_01]: everyone from the scientists to the boardroom and based on the current climate and indeed your time

[00:14:29] [SPEAKER_01]: in R&D what would you say are the the biggest challenges facing the current research and

[00:14:34] [SPEAKER_01]: development systems in biotech especially when it comes to handling that speed and scale of AI

[00:14:40] [SPEAKER_01]: driven drug discovery what are you saying here? We hit a couple already I would say the

[00:14:46] [SPEAKER_00]: the data quality and accessibility is the classic garbage in garbage out I don't think that problem

[00:14:51] [SPEAKER_00]: has changed over the years but really there has been a spotlight shun on it again because of the

[00:15:00] [SPEAKER_00]: promise of AI right what AI can do is so powerful that the data quality and availability

[00:15:06] [SPEAKER_00]: is something that really drives how fast you can get value from that investment.

[00:15:13] [SPEAKER_00]: The second challenge then we see is legacy technology where companies have done a great job

[00:15:18] [SPEAKER_00]: over the last 30 40 years building applications in-house or integrations but now when you want to

[00:15:24] [SPEAKER_00]: move fast and apply models and bring the data together that legacy technology can be a pretty

[00:15:28] [SPEAKER_00]: big inhibitor and so we see companies going through whether you call it a digital transformation

[00:15:33] [SPEAKER_00]: a cloud transformation particularly in the pharma space where there has been so much investment

[00:15:38] [SPEAKER_00]: in tools over the years that now that starts to become an inhibitor and companies are thinking

[00:15:43] [SPEAKER_00]: about how to turn it into an advantage by using let's say more bespoke tools or better data

[00:15:48] [SPEAKER_00]: quality and management. And then maybe stepping out of the technology side a real challenge is

[00:15:54] [SPEAKER_00]: just talent like like everywhere else it was hard to hire software engineers it's hard to hire

[00:16:00] [SPEAKER_00]: bio informaticians bios you know you see people coming out of college now with stem backgrounds

[00:16:07] [SPEAKER_00]: which is fantastic but this is such a broad surface area of technology and science that

[00:16:12] [SPEAKER_00]: getting enough people in so anything that we can do to ease the onboarding make the data

[00:16:16] [SPEAKER_00]: available make mentoring and growth faster is an area that also helps with getting to those

[00:16:22] [SPEAKER_01]: outcomes as quickly as possible. And something I always try and do on this podcast every day

[00:16:28] [SPEAKER_01]: is give everyone listening a valuable takeaway so for everybody listening no matter where they are

[00:16:32] [SPEAKER_01]: in the world is there any advice that you'd give to any organization or business leader that

[00:16:37] [SPEAKER_01]: maybe they're just at the beginning of exploring integration of AI into their R&D processes

[00:16:43] [SPEAKER_01]: finding their way around this trying to work out the best steps and not make any mistakes

[00:16:48] [SPEAKER_01]: or make needless mistakes any advice you'd give to those people listening.

[00:16:53] [SPEAKER_00]: There's there's a few and I think one is just general best practice and it sounds

[00:16:59] [SPEAKER_00]: an Americanism mom and apple pie which is to really define the problem that you're trying to solve

[00:17:05] [SPEAKER_00]: because there is such an array of technology and capability choice out there if you're not

[00:17:11] [SPEAKER_00]: here on the outcome that you're trying to drive you can get easily distracted or

[00:17:15] [SPEAKER_00]: chase things and you're like at the end you're not sure how you ended up there or is

[00:17:18] [SPEAKER_00]: this the right technology. So being very clear on the problem and continually going back to that

[00:17:23] [SPEAKER_00]: as you make everything from technology choices to trade off decisions and saying is this helping us

[00:17:28] [SPEAKER_00]: achieve the goal. That data readiness and quality now we've spoken about a few times

[00:17:33] [SPEAKER_00]: that continues to be the thing that is huge driver or inhibitor of the speed at which

[00:17:39] [SPEAKER_00]: you can realize the value of those AI investments and just technology in general

[00:17:44] [SPEAKER_00]: is a huge thing and then in any company as you start building if you're something like a

[00:17:50] [SPEAKER_00]: sanofi or an AstraZeneca some of the customers we have or even we have small customers like

[00:17:55] [SPEAKER_00]: Hoxton Farms in London are doing some very interesting things as well. Just having the

[00:17:59] [SPEAKER_00]: collaboration across your team we found talking to customers that one of the largest drivers

[00:18:06] [SPEAKER_00]: of delays was handoffs between teams, context sharing, knowledge transition and so how do you

[00:18:13] [SPEAKER_00]: set up the collaboration loop so that they can happen quickly with as much context as possible

[00:18:18] [SPEAKER_00]: so that you're not losing weeks and months to just explaining what you've been doing for the

[00:18:23] [SPEAKER_00]: last few months to someone who's going to pick it up and run from there and so they're the

[00:18:27] [SPEAKER_00]: kinds of things that can really set you up for success. And I think something that listeners

[00:18:32] [SPEAKER_01]: will also resonate with at the moment is that real pressure on us all to be in a state of

[00:18:38] [SPEAKER_01]: continuous learning so I've got to ask someone right in the heart of the R&D space

[00:18:42] [SPEAKER_01]: where or how do you self-educate? How do you keep up speed with the latest trends and

[00:18:47] [SPEAKER_01]: and ensure you don't get left behind and continue to lead the way anything you could share around

[00:18:51] [SPEAKER_00]: that? Just acknowledging it's hard to keep it. The rate of change is so high and not

[00:19:00] [SPEAKER_00]: you know we've talked a lot about AI it's just like every day there's a stream of new

[00:19:04] [SPEAKER_00]: capabilities from existing and new players so I would say the but then just in the general

[00:19:09] [SPEAKER_00]: software and then science and the modalities that are happening it's a huge surface area to

[00:19:13] [SPEAKER_00]: keep up to date on. So I would say there's a few things there's things I read so I read

[00:19:19] [SPEAKER_00]: hacker news regularly obviously it has a certain you know it has a certain view on the world.

[00:19:25] [SPEAKER_00]: I listen to podcasts like this to hear what others are doing that one of the best ways to

[00:19:31] [SPEAKER_00]: learn I feel is from practitioners who have been through it and you can learn from the

[00:19:35] [SPEAKER_00]: what others have done and learned rather than you know making the same mistakes I love to

[00:19:40] [SPEAKER_00]: learn from the experience of others. So podcasts I commute so I listen to podcasts on the commute

[00:19:46] [SPEAKER_00]: and then within benchling we're fortunate to have folks that come either from the bench

[00:19:52] [SPEAKER_00]: from the lab or come from academic backgrounds and so there's a natural

[00:19:57] [SPEAKER_00]: leaning towards teaching and so I lean on colleagues both at benchling and then former

[00:20:01] [SPEAKER_00]: colleagues so I'm in various text chains, WhatsApp groups whatever we're in and you know if something

[00:20:06] [SPEAKER_00]: comes up we're just leaning on each other to say have you done this how are you thinking about

[00:20:11] [SPEAKER_00]: this space so like there's a there's a small private couple of private networks and then

[00:20:15] [SPEAKER_00]: there's the public professional networks that you stay to as well and I would say just

[00:20:19] [SPEAKER_00]: understanding it's a fire hose and so there's certain things that I'll go deep in and then

[00:20:23] [SPEAKER_00]: it's okay to let other things maybe just skin the surface and say if I need to I can always come back

[00:20:27] [SPEAKER_01]: and find it later. Love that some priceless advice there and for anyone listening just want to find

[00:20:34] [SPEAKER_01]: out more information what's the best place for listeners to find you or your team online or

[00:20:38] [SPEAKER_01]: we'll just dig a little bit deeper on anything we discussed today. Yeah benchling.com is a great

[00:20:44] [SPEAKER_00]: place to be and we do a lot of updates there we also have a community for our customers so

[00:20:49] [SPEAKER_00]: if people are benchling users we've seen a huge uptick in community activity over the last year

[00:20:54] [SPEAKER_00]: or so and then just we have our bench talk conference coming up in a few weeks so you'll

[00:20:59] [SPEAKER_00]: hear a lot more about the things we've been working on there so there may be some of the

[00:21:02] [SPEAKER_00]: ideas to stay connected and then like everyone else I'm on LinkedIn as Stephen DC and so you'll

[00:21:09] [SPEAKER_00]: find all of the benchling updates too we post them on LinkedIn with you know product updates

[00:21:13] [SPEAKER_01]: customer updates and things like that. So I'll make sure links are added to everything so

[00:21:18] [SPEAKER_01]: people can find that nice and easily and just look chatting with you today about how you're

[00:21:23] [SPEAKER_01]: working to transform the pharma and biotech R&D processes with technology and make it possible

[00:21:29] [SPEAKER_01]: to improve the entire drug discovery and development industry. A huge problem that

[00:21:35] [SPEAKER_01]: you're going after there it's great to hear the role of cloud technology AI and everything in

[00:21:39] [SPEAKER_01]: between but just thank you for shining a light on this today. Neil thanks lovely to chat this morning.

[00:21:44] [SPEAKER_01]: After listening to Stephen now I think it's clear that the biotech industry is a critical crossroads

[00:21:50] [SPEAKER_01]: benchlings work in providing a unified platform for scientists and integrating AI across the

[00:21:56] [SPEAKER_01]: entire R&D process I think is essential for turning discoveries into real world treatments.

[00:22:03] [SPEAKER_01]: And Stephen also highlighted the importance of data quality and accessibility and ensuring AI

[00:22:10] [SPEAKER_01]: in ensuring AI success in biotech but what steps will you take to modernize your approach to R&D

[00:22:16] [SPEAKER_01]: and make the most of AI's potential. The new year is just around the corner and if starting

[00:22:23] [SPEAKER_01]: your own podcast for you or your business is on your 2025 to-do list I've got three

[00:22:29] [SPEAKER_01]: incredible options that will help you get started and make sure we get that podcast

[00:22:34] [SPEAKER_01]: of yours off the ground. Option one let's keep it simple you focus on recording and

[00:22:39] [SPEAKER_01]: work alongside me I'll handle everything else for $2,000 I will edit produce and publish a season

[00:22:45] [SPEAKER_01]: of 12 episodes and I will ensure that they hit Spotify Apple podcasts and all other podcasting

[00:22:51] [SPEAKER_01]: platforms on the schedule that you dictate so if you want to go a podcast a week a podcast every

[00:22:57] [SPEAKER_01]: two weeks a podcast every month $2,000 you've got 12 episodes in the bag ready to go. So if

[00:23:03] [SPEAKER_01]: you or your business are interested in getting a podcast live and you want to work directly

[00:23:07] [SPEAKER_01]: with me visit my website techblogwriter.co.uk email me techblogwriter.outlook.com or also please

[00:23:16] [SPEAKER_01]: send me your DM on any social platform such as LinkedIn, X or Instagram I'm just at Neil C Hughes

[00:23:22] [SPEAKER_01]: on every single platform but if you need additional support check out AIpodcast.ing

[00:23:28] [SPEAKER_01]: they'll manage everything from your website to social media promotions plus you'll also get

[00:23:34] [SPEAKER_01]: 25% off your first month with the code Neil C Hughes and that'll make it easier than ever for you to

[00:23:41] [SPEAKER_01]: get your podcast off the ground but option three if you're the kind of person that is tacky enough

[00:23:46] [SPEAKER_01]: and prefer to go it alone lips in the podcast host where I host this podcast as you covered with

[00:23:52] [SPEAKER_01]: up to two months of free hosting if you use my promo code TBW so if you sign up on November

[00:24:00] [SPEAKER_01]: the 1st to maximise your free trial you won't have to pay a thing till January the 1st 2025 so

[00:24:07] [SPEAKER_01]: what are you waiting for? What do you need a little help or a lot of help or no help at all?

[00:24:12] [SPEAKER_01]: 2025 is the year to make you or your business's podcast dreams a reality let's make it happen

[00:24:18] [SPEAKER_01]: together but that's it for today I've got another guest a completely different topic lined up for

[00:24:23] [SPEAKER_01]: tomorrow you are all cordially invited to join me once again hopefully I will speak with

[00:24:28] [SPEAKER_01]: you all then but bye for now