September 8, 2025 • 26 mins
What does it take to build an AI-native search engine for science?
In this episode, I spoke with Eric Olson, Co-founder & CEO of Consensus, the platform making peer-reviewed research accessible through AI. We covered the company’s journey from Series A to millions of users, the realities of competing with tech giants, and what truly creates defensibility for AI startups. Eric shared his perspective on the “AI talent wars,” building products at hyperspeed, and what truly creates a moat for AI applications. If you allocate to or invest in AI, you’ll want to hear Eric’s frameworks for product strategy, market sizing, and execution speed.
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:00):
How do you keep yourself from getting yourlunch eaten from the OpenAI's and Grox's and
(00:04):
Gemini's of the world?
This is a question that is on every singlefounder and investor looking at the AI
application layer of where is that line betweenhorizontal and vertical.
Think we're getting some pretty interestingdata points start to come in because OpenAI
has, over the last year, started to launch afew tertiary products beyond their just general
(00:26):
chatbot.
An example of one is they recently launched ameeting recorder transcription extension of
ChatGPT that directly competes with yourgranolas of the world that are bad, built into
your Zoom meetings.
That's done basically nothing to disrupt acompany or product like granola, and everyone
still thinks that granola adds all theseadditional features of delight and some
(00:46):
additional depth to the product.
If that's the case, I think there's so muchroom for vertical products to continue to
exist.
If a product that is as simple as a meetingtranscriber in your Zoom window can't be fully
disrupted by these horizontal models, there'smuch room for vertical products to exist.
Congrats on your Series A led by Union Square.
You also had Nat Friedman and Dan Gross, fabledAI investors.
(01:11):
Tell me about where consensus is today.
Yeah.
No.
Appreciate appreciate the kind words.
Super excited to get that round done.
USPs obviously got an incredible track recordand specifically an incredible track record at
our stage.
And then as you said, Matt and Daniel, youknow, biggest names and some of the biggest
names in AI investing today and obviously beenin the headlines quite a bit lately as part of
(01:34):
the AI talent wars.
As far as where we're at today, yeah, we'rekind of in this track from series A to series
B.
We have about 20 employees now.
We have about 5,000,000 users now worldwideand, you know, tracking towards some of those
series B revenue metrics trying to grow in the20% month over month range.
You mentioned these AI talent wars.
I've never seen anything like it.
(01:55):
Somebody was turned down a billion dollar offerover four years.
I don't know if that's true or not, but theseabsurd numbers, how does an AI company compete
against the metas and the OpenAI's of the worldtoday?
Yeah.
No, I saw the same.
I think it was a billion over four years andmaybe even won like 1,500,000,000.0.
I mean, it's crazy.
We're talking contracts bigger than any sportscontracts ever given.
(02:16):
And these folks are kind of the modern dayathletes in some ways.
You know, I think for a company like us, youhave to know the game that you're playing.
And I think fortunately, to some extent, we'renot exactly playing in this same game that some
of these big labs and hyperscaler companiesare.
And what I mean by that is a lot of the folkswho are getting these astronomical numbers are
(02:38):
this group of, you know, 100 or so folks whoare really at the cutting edge of frontier
model research and know the magic sauce of howto get these language models from soup to nuts
out the door.
And that isn't what an application layerstartup like ourselves is really trying to do.
You know, we are obviously using languagemodels and using AI in our products, and we
need to have people who know the models in andout.
(02:59):
But it is much more of software engineerclassic with some AI, you know, sprinkled in
experience that we're looking for, which arenot necessarily the folks getting the billion
dollar price tags.
That's really concentrated to a very, very,very, very small amount of people.
So we're more kind of competing.
We're You know, still competing against bigcompanies for folks like that, but it's more of
like what startups have always had to do whencompeting against big companies.
(03:23):
Maybe you could double click a little bit aboutwhat Consensus is and who your product is for.
Yeah.
Yeah.
So Consensus is an AI search engine forscientific and academic research.
Think of us like if anybody's ever used GoogleScholar or a PubMed at any point in their
academic or professional lives, we're trying tobuild the twenty twenty five AI native version
(03:45):
of those tools.
Another way to think of it is like superverticalized perplexity for a specific document
type and for a specific user and use case.
So people who are trying to do academic andscientific research.
So what that means in practice is the folksusing our tool are lots of students, academic
researchers, academic faculty members, those inindustry, a lot of clinicians use us to do some
(04:07):
scientific research, sometimes also answerclinical questions.
And then folks across the industry, we have alot of R and D workers at biotechs and, pharma
companies, even R and D workers like CPGcompanies.
Anywhere where real scientific academicresearch is being done, we usually will have
quite a bit of pocket of users using consensus.
How big of a TAM or market is this?
(04:29):
Yeah.
So I think there's, you know, any way you kindof slice up a TAM, you're making some things
up.
But to give you a few ways to kind of look atit.
So the number quoted a lot for knowledgeworkers is about a billion users.
And I don't think that academic or scientificresearch applies for all billion of those
users.
But if you look at some of those reports oflike, what are the personas and roles within
(04:53):
that billion, about 500,000,000 of thosebillion have some use case for academic or
scientific research.
So that's folks in academia, folks inhealthcare, and then some miscellaneous
industry jobs like R and D, pharma, even infinancial services, if you're in the healthcare
or bio sciences world, there is a use case fora tool like this.
So I think the total addressable market isabout 500,000,000 end users.
(05:14):
And then as a good proxy for that number,Google Scholar based on their traffic does
about 50,000,000 monthly active unique users,PubMed somewhere in the 20 ish million number.
I think of that being, you know, some fractionof that 500,000,000 using it on a monthly
basis, that about makes sense.
So I think without even really expanding ourmarket, which I do think that we can do of, you
(05:35):
know, making it more accessible to useresearch, meaning there's more people who might
benefit from using research in the work.
I think we can expand that 500,000,000 number,but I think that's a good starting point of
people who need, insights or need to use thesepapers today.
I think it's about 500,000,000 end users.
And I used Google Scholar when I was in gradschool.
Most people did.
(05:56):
How do you guys improve upon Google Scholar?
And maybe talk to me about a couple of usecases.
Yeah.
I mean, I think this is one of the mostexciting things about our business is that we
are competing against a product that's beenfrozen in time for about twenty years now.
Google Scholar was was super innovative, and Ithink there's a whole interesting story about
vertical search if it was one of the first realused vertical search products that split off of
(06:19):
a general purpose search engine back in theearly two thousands when Google split it off,
which I think speaks to the need of aspecialization in this use case.
But because it was split off, they actuallydemonetized it and they no longer put ads in it
and they don't make any money off of it.
So it's really kind of just been maintained andcontinued to run by a very, very small group of
people within Google and not really prioritizedprioritized by by Google.
Google.
Because of that, it hasn't really changed.
(06:41):
So if you use Google Scholar, it's actuallykinda like a a fun way to see what Google used
to look like.
It's the same interface.
It's the list of blue links.
There's no summary put on top.
There's no real, like, great interactabilitywith the results.
It's a list of blue links to your query.
Doesn't do that well with a natural languagequery still.
It's really built for keyword searching andfinding papers.
So I think the simplest way to just improveupon that is what we partially do, which is
(07:04):
take all of these new modern practices ofbuilding search and analysis products that now
exist with the advent of these language models.
So that's pulling information out of thosepapers and giving you to them this nice,
engaging, synthesized way.
I think there's a huge just thread to pull onthere beyond just the summary with inline
citations.
If you use our product, we give lots ofdifferent visuals of, you know, visualizing the
results below.
So whether that's showing them in a table,showing this aggregator count of papers that
(07:28):
agree with a certain stance.
The papers or key authors will use models topull all that information out from those papers
and give you in that kind of like summaryanalysis section up top.
And that's just like kind of table stakes in AI2025 search and analysis products, but that's
something that luckily our main competitor doesnot do.
And I think there's a lot to do beyond that aswell.
(07:51):
They think of them more as like workfloworiented features.
So Google Scholar really just is, again, a listof links for you to then go interrogate
yourself.
But there is a lot more depth to somebody doinga literature review process than just that and
a lot of actions that need to be takenfollowing a list of search results.
So that's integrating that with a referencemanager to store the papers to go into further.
(08:11):
That is potentially diving into one particularpaper, asking a bunch of questions of that
while still not losing your place on the searchresults page.
So think of it as like post search, postgetting a high level analysis, what happens
next?
We can keep stringing together features andworkflows to make that more seamless.
Google Scholar's done none of that.
So again, all the way back to the top, we'resuper lucky to be talking about a product that
(08:35):
is pretty frozen in time.
So anything we do beyond a list of links isdifferentiating between Google Scholar.
And it's intuitive that you're not competingagainst the same engineers as Meta and OpenAI
as you mentioned.
What's nonintuitive to me is who in AI is gonnawin from a vertical and horizontal approach.
These LMs are every single day, they get newcapacity.
(08:56):
They're they're able to do deep research.
And how do you keep yourself from getting yourlunch eaten from the OpenAI's and Crocs and
Gemini's of the world?
Yeah.
I mean, I think this is a question that is onevery single founder and investor looking at
the AI application layer of where is that linebetween horizontal and vertical that that makes
(09:18):
sense?
And I don't think anybody really knows theanswer in the world of AI products today.
I think we're we're getting some prettyinteresting data points start to come in
because OpenAI has, over the last year, startedto launch a few tertiary products beyond their
just general chatbot.
So, like, an example of one is they recentlylaunched like a meeting recorder transcription
(09:40):
extension of ChatGPT that directly competeswith, you know, your granolas of the world that
are building that built into your Zoommeetings.
From what I can see, that's done basicallynothing to disrupt a company or product like
granola, and everyone still thinks that granolaadds all these additional features of delight
and some additional depth to the product thatcan still be there.
(10:01):
If that's the case, I think there's so muchroom for vertical products to continue to
exist.
If a product that is as simple as not to trashon granola, people freaking love granola, but a
product that is as simple as a meetingtranscriber in your Zoom window can't be fully
disrupted by these horizontal models, I thinkthere's so, so, so, so, so much room for
(10:21):
vertical products to live.
And then I think to the other part of thequestion of like, what can products like us do
to not get our lunch eaten by them?
It's, you know, I think it's staying focused onthe problem that you're solving because
everybody has a a finite set of resources.
Everybody has a finite set of focus, find ayeah.
Find an amount of focus they can give.
And even the most capitalized, smartest peoplein the world can really only truly be great at
(10:45):
a finite number of things.
So your moat against big players is your focusin getting into every nook and cranny of your
problem of what your users are facing.
Like, there's never not gonna be a market forthat if you do that incredibly well, even if
intuitively some of these products should beswallowed up by a capability a model.
There is going to be some line.
Like I'm not sitting here and saying that thenew capabilities and as these models keep
(11:07):
getting better won't make some productsobsolete.
We have seen that happen with some.
I think people generally overestimate how muchthat will happen and how much surface area
there still is to build vertical products.
And I think we're seeing evidence of that stillsome today as OpenAI continues to launch out
new product lines alongside Chateapati thatdon't seem to be ripping successes yet.
(11:27):
Yeah.
The meeting recorder versus Granola use case isan interesting one.
Why is Granolah able to delight users in a waythat the OpenAI product does not double click
on that?
I'm not a Granolah user myself.
I have teammates who are, and they love it.
You know, I I but I think I can still answerthe question without even being a power user,
(11:50):
that it's it's back to what I said before.
It's it's when you truly focus on something andyou can show a user that at every step of the
way you are there to solve the problem thatthey want you to solve, that is a product that
delights, and that's what a sticky product is.
And you can feel it when you use a product thatif this is, you know, the fourth priority of a
company or a like, it it it just doesn't feelthe same with the whole stepwise process even
(12:11):
if sometimes that core function is the same.
All of the tertiary stuff in the productmessaging on onboarding, on the emails they
didn't send you after you sign up, and whathappens after a call and how they deliver you
back that information.
If all of that is done maniacally detailed,focused on the particular problem, it will feel
different than somebody else working on thatsame problem when it's their fifth biggest
(12:32):
priority.
There's so many little things that add up, andeverybody wants to know that you are there to
solve their problems.
You mentioned him earlier, Matt Friedman hasgreat quote that he said to us, I could hire
somebody off the street to clean my house andthey would probably do just about as good of a
job as somebody who has a cleaning service, butI still go to the cleaning service because they
(12:55):
might just be 10% better.
I know they already have all the suppliesbecause they're there to solve my problem.
They're marketing towards me.
They communicate in a way that is, you know,designed for this this exchange, and I'm
willing to pay a little bit extra for that, forthat for me to know that that person is there
to specifically solve my problem, even if thecore capability isn't that differentiated.
And that I really think does exist in insoftware products.
(13:17):
It's kind of interesting if if you take it takeit from the framework of scarce resources, the
scarce resources that a startup has is caring alot about a problem and having very smart
people going after that product.
So, yes, in theory, OpenAI could bring in someof these people that they're paying a
$100,000,000 a year to focus on this sideproject, but in reality, they're focusing on
(13:39):
the LLM.
So what you get is kind of this effect ofhaving the b b minus players focus on these
products or maybe in like, in the case ofGoogle Scholar, they have just a couple of
people doing it as their 20% project.
So there's something around that focus andaround that having like the very top engineers
in the company focusing on that one thingthat's so critical.
(14:00):
Exactly right.
And if you don't feel that massive inertia andenergy of the whole company behind you too,
even if the people that you break out to workon that product might be incredibly talented
and not just as talented as some of the peoplewe might have in our our doors.
But if it's all we focus about, we will have anadvantage over you.
And I I really do believe that even the best,biggest, and most capitalized companies in the
(14:22):
world can really only truly be exceptional andbest at a pretty finite number of things.
And that number is usually smaller than whatpeople think it is because you need that
inertia of the whole organization behind you toreally truly build great products.
It also really answers the the, you know,generational, it also answers a question that
so many people ask in the startup world, whichis why can't Google come in and do this or
(14:44):
Facebook?
It's that concentration, that focus that couldonly be done within the concept of a startup
and within the incentive structure of a startupwhere people really care, people have that
equity.
Exactly right.
I mean, this is the same trope that's existedin startups for decades.
It's just it's just faster paced and more ondisplay as we're in this new world with with
language models.
But it's really the same debate that peoplehave have always had, and the case has always
(15:07):
been there's room for disruption and there'sroom for startups.
And we never we didn't even talk about the thetolerance for risk too involved in all of this
and the advantage that startups get of that, ofthe ability to to launch and iterate and put
things out there and have a risk tolerance thatbig companies just don't have.
I mean, Google, one of their demos, whatever itwas, two one or two years ago had, like, a a
wrong answer of one of their models and theirstock price dropped by 8%.
(15:30):
That's a $100,000,000,000.
And that's by making one you know, that is therisk that they have to deal with that, you
know, if they put an LLM into Google Scholarand it summarized the paper that said vaccines
cause autism and somebody took a screenshot andit went viral on Twitter, they might lose
$50,000,000,000 of market cap.
Is that risk worth it for them?
Like, their risk tolerance of doing innovativethings just is lower than what you get to do as
(15:52):
a startup and allows you to sometimes do moreinteresting things and build products that
they'll never build.
So outside of competing with Meta, OpenAI, andand Alphabet and all these companies, you're
also developing products in a hyperscalemarket.
Meaning by the time you've released yourproduct, the AI market has gone to its next
(16:13):
iteration.
Do you develop your product in such ahyperscale environment?
Yeah, it's a great question and it's crazy, acrazy, crazy world we live in.
I think, number one, there's absolutely just nosubstitute for complete urgency, really, really
(16:34):
hard work and trying to move as fast as humanlypossible because we are in the fastest moving
space in the world.
People know how big of an opportunity there is.
We are not the only people that have thought ofusing ILNs for scientific research.
If you are not truly pushing the pace everysingle day, you are going to fall behind,
whether it's your contemporaries doing similarthings or, you know, the models will just be
good enough that people could just end up usingthem for your use case if you don't truly win
(16:58):
mindshare.
And I think back to the earlier point of how doyou compete against these other products?
Like, I think that the bear case for startupsis that if you don't capture mindshare and you
don't move really, really fast, it isn't thatthey're going to launch these super competitive
specialized products, but their products couldbe good enough if you don't build a great
product.
So I think it does raise the bar that you haveto cross.
(17:19):
I just think it is an attainable bar if youmove really, really fast and build really,
really great products.
So I think speed is just it is more importantthan it's ever been before and started today
with the moving markets.
And then I think the second part is kind ofwhat you were you said some of it, you were
alluding to some of this in your question, butI think it's also, you know, having any mindset
of knowing that the models are going toimprove, they are going to get cheaper, they
(17:42):
are going to get faster, they are going to gethave longer context windows and building your
product with that in mind.
And maybe sometimes wearing, you know, eating alittle bit of cost for a few months because we
know GPT-five is gonna come out Thursday andit's probably gonna have a bigger context
window.
It probably will have smaller iterations thatwill be cheaper than anything on market.
And we can get something out faster if we justship it with GPT four point zero and know that
(18:03):
it's gonna be a little expensive and it'll slowfor a bit, but we'll be able to swap in
whenever a new model comes out in a few months.
Like, you have to be willing to make some ofthose trade offs.
You have to do it, you know, thoughtfully, andwe do have finite resources of cash too.
But I think startups generally taking some ofthose swings is is the right move just with an
understanding that things are gonna getcheaper, faster, smarter.
(18:24):
How much of your product development is drivenby customer feedback and customer demands
versus internally deciding as leadership or asa product manager?
This is what the customer should have in thenext iteration.
How do you balance those two forces?
Yeah.
Give a shout out to my co founder, Christian,officer.
He's a product manager by background.
(18:45):
He's a freaking incredible product mindedleader.
What he says he shoots for, and I think we do adecent job in this.
You know, there's obviously no way to say itexactly, it's always some combination of the
two.
It's about seventythirty, eightytwenty with theseventyeighty being driven by users' requests.
And then you layer in, you know, just likegeneral bets that you want to take given the
(19:08):
direction of your company that users aren'tsaying.
You want to layer in some intuition about wherethe market is going and things that we should
bet on.
And then also, like, you never want to fallinto the trope of just building exactly what
users ask for.
Sometimes you have to take what they're askingfor and distill it down into a problem and kind
of craft it in a slightly different way thanmaybe they asked for it.
(19:28):
So that all kind of goes into that, like, 30%bucket of what we're doing internally.
But the foundations, at least the generaldirections, should be very, very user guided.
The exact specifics and some other bets youkind of sprinkle in, a lot of that can come
from your own synthesizing your own marketobservations and your own goals and desires as
a company.
But I think seventythirty is roughly a decentheuristic for what we try to do.
(19:50):
Said another way, customers always know theirpain points, but they're not oftentimes
technical enough to understand how to solvethose pain points.
So sometimes you listen to their problem andnot their solution.
Exactly right.
It's a classic like user interview bestpractice is like always take a little with a
grain of salt.
The question is usually never what do you wantus to build?
It's more like, what are you feeling when youuse the product and what are you trying to
(20:11):
solve for?
What is your problem?
That is more insightful than strictly justasking what would you like there to be.
There can sometimes be really good ideas, butit is more universally applicable when you look
for problems, not solutions.
As you build out consensus, how do you thinkabout building a moat and is that even possible
in an AI consumer product?
(20:33):
Yeah.
Good question.
I think for startups, moats are kind of a myth.
I think the only like real, real moats thatexist are usually distribution and brand.
And those are not typically things you have theadvantage of of as a start up.
I think your your quote, unquote moat as astart up is your focus, as I said before, and
(20:55):
your ability to be narrowed in on a certain setof problems and your speed and ability to
innovate and take risks.
And I think you just have to rely on that untilyou truly have scale of distribution and brand,
and that truly is a moat against, you know,upstart competitors.
There are obviously exceptions.
Like, I'm, you know, not a I'm not a hardwareexpert, but I know NVIDIA has some, you know,
(21:16):
multiyear lead on its competitiontechnologically speaking.
There are some exceptions where there's this,like, special technological breakthrough
innovation that you have internally that othersdon't.
Usually, that's not the case.
In the history of software, it isn't that, youknow, Salesforce has some incredible
technological breakthrough that some othercompany doesn't have that gives them this moat.
What is their moat is they were they executedincredibly well.
(21:38):
They focused on a narrow set of problems.
They built a great product.
And then eventually, they had the scale of thisbrand and this distribution that really is
defensible and really is hard to crack throughif you're an upstart.
So I think as startups like a moat and youhaving something that nobody else could really
do is kind of a myth, but you can protectyourself from getting crunched by being really
(21:58):
focused and moving really, really fast.
And eventually, you build these more durablemoats over time.
Is that a yellow or a red flag when a VC asksyou that question?
It's a reddish reddish yellow flag.
Reddish.
Yeah.
You you can ask Isn't it isn't it kind of aninteresting thought experiment, though, to to
think about these things even if it's it's kindof a misnomer?
(22:20):
Yes.
And that's why I didn't say it's a full redflag.
But if all they do is they just look at you andsay, Hey, what's your moat?
That's kind of a red flag.
But if they ask in a slightly more likethoughtful way with some like kind of other
threads to pull on, I think it's a perfectlyacceptable question of like, how do you want to
develop develop a durable business over time?
(22:40):
It's like a perfectly reasonable question.
Or like, what do you view as how you defendagainst some big players?
What do think your unique advantage is?
Like, I I don't know.
What if all they do is just ask for the moat,you're probably talking to an associate who's
just on tech Twitter and is just kind of askingstock stock questions.
No offense to associates.
(23:01):
So so I'm gonna ask you a kind of difficultquestion, which is to take off your startup
founder and CEO hat and just look at it, noteven from a venture side, but from a asset
allocator side, let's say you're a familyoffice, you're an institutional investor.
How would you play this, quote, unquote, AImarket?
Are you trying to like, how would you investinto the AI space?
(23:22):
Would you do kind of like a spray and pray andknow that something's gonna hit very big?
Are you focusing on a co kind of thematic, acouple themes?
Or how would you play how would you invest inthe space if you're an asset allocator?
Yeah.
Let me caveat by saying I'm not an assetallocator.
So definitely not the best person to ask thisquestion to.
(23:46):
Yeah.
I mean, I'd say, number one, I don't reallybelieve that there are that particular, like,
defensible advantages of any part of the thestacks, like the application layer, the kind
of, like, infrastructure layer, the hardwarelayer.
Like, I think a lot of the same things are allpresent across all of them.
So, like, I would be interested in havingexposure across the different layers of the
(24:09):
stack and not just investing in only one.
And then I think within each layer, I think,honestly, to some of like the themes of what we
talked about before, I think history doesn'trepeat itself, it rhymes.
And I think all of the same best practices ofhow we'd take the best companies and the
founders in those each particular area are justgonna be true today and look to stick your
fundamentals that way.
(24:30):
Look for really, really, really great founders,really, really great teams who are solving a
really important problem.
And that's really the best that you can do.
And there's going to be ones you miss on,there's going be ones you hit home runs on.
But if you just keep indexing on that, ifpeople are really great people solving sharp
problems, you'll eventually do pretty good inthe long term.
And I think it isn't really reinventing thewheel of what exactly that you're looking for.
(24:51):
I think the one thing to be caution on is justhow crazy some of these rounds can get in our
space and knowing if that makes sense for whatyour goals are of a particular institution or
firm.
Like USB is a great example.
They don't really do any growth stage.
They're pretty focused on Series A mostly withsome Cs, some Bs.
And they're looking for contrarian bets, theyalways have.
(25:13):
And they're not going to be chasing thisbillion dollar round, raising a Series B
billion dollar round in vets because that'sjust the that is how they've made their hay.
That's how they know their that's what theyknow they're great at.
And that isn't the way that they're set up as afirm to win is getting into those billion
dollar rounds.
So I think you just have to operate with yourconstraints and mostly stick to the same
fundamentals that have worked in in softwareinvesting for a decade.
(25:35):
Yeah.
It's an interesting take because essentially,it's a new market.
It's obviously large, but you have to focus onyour controllable variable, which is backing
the best managers and backing the best foundersand let them take you to the promised land,
take you to the next trillion dollar business.
Nothing about AI fundamentally changes the ABCsof investing, is backing the best talent, going
(25:57):
after the best opportunities.
Exactly.
And then within the constraints of of whateveryou're doing as an asset allocator of of what
types of firms and stages you wanna be givingallocation to, or if you are one of those VC
firms, you know, what types of rounds andstages you're going after within those
constraints, it's gonna be mostly the samefundamentals.
Well, Eric, this has been a great deep dive onconsensus.
(26:19):
Congrats on everything that you've done andlook forward to continuing conversation live.
Yeah.
Much appreciate.
Thanks for having me on, David.
Check us out at consensus.app.
Yes.
How should people follow you and keep up todate on consensus?
Yeah.
Follow you can sign up and create a freeaccount if you wanna check out the product at
consensus.app, app, or follow us on Twitter atconsensus n l p.
(26:40):
You'll see lots of product updates andinteresting musings on AI and science products.
Thank you, Eric, and appreciate you jumping on.
Appreciate it, Dave.
Thanks for having me.
Thanks.
Thanks for listening to my conversation.
If you enjoyed this episode, please share witha friend.
This helps us grow.
Also provides the very best feedback when wereview the episodes analytics.
Thank you for your support.
How do you keep yourself from getting yourlunch eaten from the OpenAI's and Grox's and
(00:04):
Gemini's of the world?
This is a question that is on every singlefounder and investor looking at the AI
application layer of where is that line betweenhorizontal and vertical.
Think we're getting some pretty interestingdata points start to come in because OpenAI
has, over the last year, started to launch afew tertiary products beyond their just general
(00:26):
chatbot.
An example of one is they recently launched ameeting recorder transcription extension of
ChatGPT that directly competes with yourgranolas of the world that are bad, built into
your Zoom meetings.
That's done basically nothing to disrupt acompany or product like granola, and everyone
still thinks that granola adds all theseadditional features of delight and some
(00:46):
additional depth to the product.
If that's the case, I think there's so muchroom for vertical products to continue to
exist.
If a product that is as simple as a meetingtranscriber in your Zoom window can't be fully
disrupted by these horizontal models, there'smuch room for vertical products to exist.
Congrats on your Series A led by Union Square.
You also had Nat Friedman and Dan Gross, fabledAI investors.
(01:11):
Tell me about where consensus is today.
Yeah.
No.
Appreciate appreciate the kind words.
Super excited to get that round done.
USPs obviously got an incredible track recordand specifically an incredible track record at
our stage.
And then as you said, Matt and Daniel, youknow, biggest names and some of the biggest
names in AI investing today and obviously beenin the headlines quite a bit lately as part of
(01:34):
the AI talent wars.
As far as where we're at today, yeah, we'rekind of in this track from series A to series
B.
We have about 20 employees now.
We have about 5,000,000 users now worldwideand, you know, tracking towards some of those
series B revenue metrics trying to grow in the20% month over month range.
You mentioned these AI talent wars.
I've never seen anything like it.
(01:55):
Somebody was turned down a billion dollar offerover four years.
I don't know if that's true or not, but theseabsurd numbers, how does an AI company compete
against the metas and the OpenAI's of the worldtoday?
Yeah.
No, I saw the same.
I think it was a billion over four years andmaybe even won like 1,500,000,000.0.
I mean, it's crazy.
We're talking contracts bigger than any sportscontracts ever given.
(02:16):
And these folks are kind of the modern dayathletes in some ways.
You know, I think for a company like us, youhave to know the game that you're playing.
And I think fortunately, to some extent, we'renot exactly playing in this same game that some
of these big labs and hyperscaler companiesare.
And what I mean by that is a lot of the folkswho are getting these astronomical numbers are
(02:38):
this group of, you know, 100 or so folks whoare really at the cutting edge of frontier
model research and know the magic sauce of howto get these language models from soup to nuts
out the door.
And that isn't what an application layerstartup like ourselves is really trying to do.
You know, we are obviously using languagemodels and using AI in our products, and we
need to have people who know the models in andout.
(02:59):
But it is much more of software engineerclassic with some AI, you know, sprinkled in
experience that we're looking for, which arenot necessarily the folks getting the billion
dollar price tags.
That's really concentrated to a very, very,very, very small amount of people.
So we're more kind of competing.
We're You know, still competing against bigcompanies for folks like that, but it's more of
like what startups have always had to do whencompeting against big companies.
(03:23):
Maybe you could double click a little bit aboutwhat Consensus is and who your product is for.
Yeah.
Yeah.
So Consensus is an AI search engine forscientific and academic research.
Think of us like if anybody's ever used GoogleScholar or a PubMed at any point in their
academic or professional lives, we're trying tobuild the twenty twenty five AI native version
(03:45):
of those tools.
Another way to think of it is like superverticalized perplexity for a specific document
type and for a specific user and use case.
So people who are trying to do academic andscientific research.
So what that means in practice is the folksusing our tool are lots of students, academic
researchers, academic faculty members, those inindustry, a lot of clinicians use us to do some
(04:07):
scientific research, sometimes also answerclinical questions.
And then folks across the industry, we have alot of R and D workers at biotechs and, pharma
companies, even R and D workers like CPGcompanies.
Anywhere where real scientific academicresearch is being done, we usually will have
quite a bit of pocket of users using consensus.
How big of a TAM or market is this?
(04:29):
Yeah.
So I think there's, you know, any way you kindof slice up a TAM, you're making some things
up.
But to give you a few ways to kind of look atit.
So the number quoted a lot for knowledgeworkers is about a billion users.
And I don't think that academic or scientificresearch applies for all billion of those
users.
But if you look at some of those reports oflike, what are the personas and roles within
(04:53):
that billion, about 500,000,000 of thosebillion have some use case for academic or
scientific research.
So that's folks in academia, folks inhealthcare, and then some miscellaneous
industry jobs like R and D, pharma, even infinancial services, if you're in the healthcare
or bio sciences world, there is a use case fora tool like this.
So I think the total addressable market isabout 500,000,000 end users.
(05:14):
And then as a good proxy for that number,Google Scholar based on their traffic does
about 50,000,000 monthly active unique users,PubMed somewhere in the 20 ish million number.
I think of that being, you know, some fractionof that 500,000,000 using it on a monthly
basis, that about makes sense.
So I think without even really expanding ourmarket, which I do think that we can do of, you
(05:35):
know, making it more accessible to useresearch, meaning there's more people who might
benefit from using research in the work.
I think we can expand that 500,000,000 number,but I think that's a good starting point of
people who need, insights or need to use thesepapers today.
I think it's about 500,000,000 end users.
And I used Google Scholar when I was in gradschool.
Most people did.
(05:56):
How do you guys improve upon Google Scholar?
And maybe talk to me about a couple of usecases.
Yeah.
I mean, I think this is one of the mostexciting things about our business is that we
are competing against a product that's beenfrozen in time for about twenty years now.
Google Scholar was was super innovative, and Ithink there's a whole interesting story about
vertical search if it was one of the first realused vertical search products that split off of
(06:19):
a general purpose search engine back in theearly two thousands when Google split it off,
which I think speaks to the need of aspecialization in this use case.
But because it was split off, they actuallydemonetized it and they no longer put ads in it
and they don't make any money off of it.
So it's really kind of just been maintained andcontinued to run by a very, very small group of
people within Google and not really prioritizedprioritized by by Google.
Google.
Because of that, it hasn't really changed.
(06:41):
So if you use Google Scholar, it's actuallykinda like a a fun way to see what Google used
to look like.
It's the same interface.
It's the list of blue links.
There's no summary put on top.
There's no real, like, great interactabilitywith the results.
It's a list of blue links to your query.
Doesn't do that well with a natural languagequery still.
It's really built for keyword searching andfinding papers.
So I think the simplest way to just improveupon that is what we partially do, which is
(07:04):
take all of these new modern practices ofbuilding search and analysis products that now
exist with the advent of these language models.
So that's pulling information out of thosepapers and giving you to them this nice,
engaging, synthesized way.
I think there's a huge just thread to pull onthere beyond just the summary with inline
citations.
If you use our product, we give lots ofdifferent visuals of, you know, visualizing the
results below.
So whether that's showing them in a table,showing this aggregator count of papers that
(07:28):
agree with a certain stance.
The papers or key authors will use models topull all that information out from those papers
and give you in that kind of like summaryanalysis section up top.
And that's just like kind of table stakes in AI2025 search and analysis products, but that's
something that luckily our main competitor doesnot do.
And I think there's a lot to do beyond that aswell.
(07:51):
They think of them more as like workfloworiented features.
So Google Scholar really just is, again, a listof links for you to then go interrogate
yourself.
But there is a lot more depth to somebody doinga literature review process than just that and
a lot of actions that need to be takenfollowing a list of search results.
So that's integrating that with a referencemanager to store the papers to go into further.
(08:11):
That is potentially diving into one particularpaper, asking a bunch of questions of that
while still not losing your place on the searchresults page.
So think of it as like post search, postgetting a high level analysis, what happens
next?
We can keep stringing together features andworkflows to make that more seamless.
Google Scholar's done none of that.
So again, all the way back to the top, we'resuper lucky to be talking about a product that
(08:35):
is pretty frozen in time.
So anything we do beyond a list of links isdifferentiating between Google Scholar.
And it's intuitive that you're not competingagainst the same engineers as Meta and OpenAI
as you mentioned.
What's nonintuitive to me is who in AI is gonnawin from a vertical and horizontal approach.
These LMs are every single day, they get newcapacity.
(08:56):
They're they're able to do deep research.
And how do you keep yourself from getting yourlunch eaten from the OpenAI's and Crocs and
Gemini's of the world?
Yeah.
I mean, I think this is a question that is onevery single founder and investor looking at
the AI application layer of where is that linebetween horizontal and vertical that that makes
(09:18):
sense?
And I don't think anybody really knows theanswer in the world of AI products today.
I think we're we're getting some prettyinteresting data points start to come in
because OpenAI has, over the last year, startedto launch a few tertiary products beyond their
just general chatbot.
So, like, an example of one is they recentlylaunched like a meeting recorder transcription
(09:40):
extension of ChatGPT that directly competeswith, you know, your granolas of the world that
are building that built into your Zoommeetings.
From what I can see, that's done basicallynothing to disrupt a company or product like
granola, and everyone still thinks that granolaadds all these additional features of delight
and some additional depth to the product thatcan still be there.
(10:01):
If that's the case, I think there's so muchroom for vertical products to continue to
exist.
If a product that is as simple as not to trashon granola, people freaking love granola, but a
product that is as simple as a meetingtranscriber in your Zoom window can't be fully
disrupted by these horizontal models, I thinkthere's so, so, so, so, so much room for
(10:21):
vertical products to live.
And then I think to the other part of thequestion of like, what can products like us do
to not get our lunch eaten by them?
It's, you know, I think it's staying focused onthe problem that you're solving because
everybody has a a finite set of resources.
Everybody has a finite set of focus, find ayeah.
Find an amount of focus they can give.
And even the most capitalized, smartest peoplein the world can really only truly be great at
(10:45):
a finite number of things.
So your moat against big players is your focusin getting into every nook and cranny of your
problem of what your users are facing.
Like, there's never not gonna be a market forthat if you do that incredibly well, even if
intuitively some of these products should beswallowed up by a capability a model.
There is going to be some line.
Like I'm not sitting here and saying that thenew capabilities and as these models keep
(11:07):
getting better won't make some productsobsolete.
We have seen that happen with some.
I think people generally overestimate how muchthat will happen and how much surface area
there still is to build vertical products.
And I think we're seeing evidence of that stillsome today as OpenAI continues to launch out
new product lines alongside Chateapati thatdon't seem to be ripping successes yet.
(11:27):
Yeah.
The meeting recorder versus Granola use case isan interesting one.
Why is Granolah able to delight users in a waythat the OpenAI product does not double click
on that?
I'm not a Granolah user myself.
I have teammates who are, and they love it.
You know, I I but I think I can still answerthe question without even being a power user,
(11:50):
that it's it's back to what I said before.
It's it's when you truly focus on something andyou can show a user that at every step of the
way you are there to solve the problem thatthey want you to solve, that is a product that
delights, and that's what a sticky product is.
And you can feel it when you use a product thatif this is, you know, the fourth priority of a
company or a like, it it it just doesn't feelthe same with the whole stepwise process even
(12:11):
if sometimes that core function is the same.
All of the tertiary stuff in the productmessaging on onboarding, on the emails they
didn't send you after you sign up, and whathappens after a call and how they deliver you
back that information.
If all of that is done maniacally detailed,focused on the particular problem, it will feel
different than somebody else working on thatsame problem when it's their fifth biggest
(12:32):
priority.
There's so many little things that add up, andeverybody wants to know that you are there to
solve their problems.
You mentioned him earlier, Matt Friedman hasgreat quote that he said to us, I could hire
somebody off the street to clean my house andthey would probably do just about as good of a
job as somebody who has a cleaning service, butI still go to the cleaning service because they
(12:55):
might just be 10% better.
I know they already have all the suppliesbecause they're there to solve my problem.
They're marketing towards me.
They communicate in a way that is, you know,designed for this this exchange, and I'm
willing to pay a little bit extra for that, forthat for me to know that that person is there
to specifically solve my problem, even if thecore capability isn't that differentiated.
And that I really think does exist in insoftware products.
(13:17):
It's kind of interesting if if you take it takeit from the framework of scarce resources, the
scarce resources that a startup has is caring alot about a problem and having very smart
people going after that product.
So, yes, in theory, OpenAI could bring in someof these people that they're paying a
$100,000,000 a year to focus on this sideproject, but in reality, they're focusing on
(13:39):
the LLM.
So what you get is kind of this effect ofhaving the b b minus players focus on these
products or maybe in like, in the case ofGoogle Scholar, they have just a couple of
people doing it as their 20% project.
So there's something around that focus andaround that having like the very top engineers
in the company focusing on that one thingthat's so critical.
(14:00):
Exactly right.
And if you don't feel that massive inertia andenergy of the whole company behind you too,
even if the people that you break out to workon that product might be incredibly talented
and not just as talented as some of the peoplewe might have in our our doors.
But if it's all we focus about, we will have anadvantage over you.
And I I really do believe that even the best,biggest, and most capitalized companies in the
(14:22):
world can really only truly be exceptional andbest at a pretty finite number of things.
And that number is usually smaller than whatpeople think it is because you need that
inertia of the whole organization behind you toreally truly build great products.
It also really answers the the, you know,generational, it also answers a question that
so many people ask in the startup world, whichis why can't Google come in and do this or
(14:44):
Facebook?
It's that concentration, that focus that couldonly be done within the concept of a startup
and within the incentive structure of a startupwhere people really care, people have that
equity.
Exactly right.
I mean, this is the same trope that's existedin startups for decades.
It's just it's just faster paced and more ondisplay as we're in this new world with with
language models.
But it's really the same debate that peoplehave have always had, and the case has always
(15:07):
been there's room for disruption and there'sroom for startups.
And we never we didn't even talk about the thetolerance for risk too involved in all of this
and the advantage that startups get of that, ofthe ability to to launch and iterate and put
things out there and have a risk tolerance thatbig companies just don't have.
I mean, Google, one of their demos, whatever itwas, two one or two years ago had, like, a a
wrong answer of one of their models and theirstock price dropped by 8%.
(15:30):
That's a $100,000,000,000.
And that's by making one you know, that is therisk that they have to deal with that, you
know, if they put an LLM into Google Scholarand it summarized the paper that said vaccines
cause autism and somebody took a screenshot andit went viral on Twitter, they might lose
$50,000,000,000 of market cap.
Is that risk worth it for them?
Like, their risk tolerance of doing innovativethings just is lower than what you get to do as
(15:52):
a startup and allows you to sometimes do moreinteresting things and build products that
they'll never build.
So outside of competing with Meta, OpenAI, andand Alphabet and all these companies, you're
also developing products in a hyperscalemarket.
Meaning by the time you've released yourproduct, the AI market has gone to its next
(16:13):
iteration.
Do you develop your product in such ahyperscale environment?
Yeah, it's a great question and it's crazy, acrazy, crazy world we live in.
I think, number one, there's absolutely just nosubstitute for complete urgency, really, really
(16:34):
hard work and trying to move as fast as humanlypossible because we are in the fastest moving
space in the world.
People know how big of an opportunity there is.
We are not the only people that have thought ofusing ILNs for scientific research.
If you are not truly pushing the pace everysingle day, you are going to fall behind,
whether it's your contemporaries doing similarthings or, you know, the models will just be
good enough that people could just end up usingthem for your use case if you don't truly win
(16:58):
mindshare.
And I think back to the earlier point of how doyou compete against these other products?
Like, I think that the bear case for startupsis that if you don't capture mindshare and you
don't move really, really fast, it isn't thatthey're going to launch these super competitive
specialized products, but their products couldbe good enough if you don't build a great
product.
So I think it does raise the bar that you haveto cross.
(17:19):
I just think it is an attainable bar if youmove really, really fast and build really,
really great products.
So I think speed is just it is more importantthan it's ever been before and started today
with the moving markets.
And then I think the second part is kind ofwhat you were you said some of it, you were
alluding to some of this in your question, butI think it's also, you know, having any mindset
of knowing that the models are going toimprove, they are going to get cheaper, they
(17:42):
are going to get faster, they are going to gethave longer context windows and building your
product with that in mind.
And maybe sometimes wearing, you know, eating alittle bit of cost for a few months because we
know GPT-five is gonna come out Thursday andit's probably gonna have a bigger context
window.
It probably will have smaller iterations thatwill be cheaper than anything on market.
And we can get something out faster if we justship it with GPT four point zero and know that
(18:03):
it's gonna be a little expensive and it'll slowfor a bit, but we'll be able to swap in
whenever a new model comes out in a few months.
Like, you have to be willing to make some ofthose trade offs.
You have to do it, you know, thoughtfully, andwe do have finite resources of cash too.
But I think startups generally taking some ofthose swings is is the right move just with an
understanding that things are gonna getcheaper, faster, smarter.
(18:24):
How much of your product development is drivenby customer feedback and customer demands
versus internally deciding as leadership or asa product manager?
This is what the customer should have in thenext iteration.
How do you balance those two forces?
Yeah.
Give a shout out to my co founder, Christian,officer.
He's a product manager by background.
(18:45):
He's a freaking incredible product mindedleader.
What he says he shoots for, and I think we do adecent job in this.
You know, there's obviously no way to say itexactly, it's always some combination of the
two.
It's about seventythirty, eightytwenty with theseventyeighty being driven by users' requests.
And then you layer in, you know, just likegeneral bets that you want to take given the
(19:08):
direction of your company that users aren'tsaying.
You want to layer in some intuition about wherethe market is going and things that we should
bet on.
And then also, like, you never want to fallinto the trope of just building exactly what
users ask for.
Sometimes you have to take what they're askingfor and distill it down into a problem and kind
of craft it in a slightly different way thanmaybe they asked for it.
(19:28):
So that all kind of goes into that, like, 30%bucket of what we're doing internally.
But the foundations, at least the generaldirections, should be very, very user guided.
The exact specifics and some other bets youkind of sprinkle in, a lot of that can come
from your own synthesizing your own marketobservations and your own goals and desires as
a company.
But I think seventythirty is roughly a decentheuristic for what we try to do.
(19:50):
Said another way, customers always know theirpain points, but they're not oftentimes
technical enough to understand how to solvethose pain points.
So sometimes you listen to their problem andnot their solution.
Exactly right.
It's a classic like user interview bestpractice is like always take a little with a
grain of salt.
The question is usually never what do you wantus to build?
It's more like, what are you feeling when youuse the product and what are you trying to
(20:11):
solve for?
What is your problem?
That is more insightful than strictly justasking what would you like there to be.
There can sometimes be really good ideas, butit is more universally applicable when you look
for problems, not solutions.
As you build out consensus, how do you thinkabout building a moat and is that even possible
in an AI consumer product?
(20:33):
Yeah.
Good question.
I think for startups, moats are kind of a myth.
I think the only like real, real moats thatexist are usually distribution and brand.
And those are not typically things you have theadvantage of of as a start up.
I think your your quote, unquote moat as astart up is your focus, as I said before, and
(20:55):
your ability to be narrowed in on a certain setof problems and your speed and ability to
innovate and take risks.
And I think you just have to rely on that untilyou truly have scale of distribution and brand,
and that truly is a moat against, you know,upstart competitors.
There are obviously exceptions.
Like, I'm, you know, not a I'm not a hardwareexpert, but I know NVIDIA has some, you know,
(21:16):
multiyear lead on its competitiontechnologically speaking.
There are some exceptions where there's this,like, special technological breakthrough
innovation that you have internally that othersdon't.
Usually, that's not the case.
In the history of software, it isn't that, youknow, Salesforce has some incredible
technological breakthrough that some othercompany doesn't have that gives them this moat.
What is their moat is they were they executedincredibly well.
(21:38):
They focused on a narrow set of problems.
They built a great product.
And then eventually, they had the scale of thisbrand and this distribution that really is
defensible and really is hard to crack throughif you're an upstart.
So I think as startups like a moat and youhaving something that nobody else could really
do is kind of a myth, but you can protectyourself from getting crunched by being really
(21:58):
focused and moving really, really fast.
And eventually, you build these more durablemoats over time.
Is that a yellow or a red flag when a VC asksyou that question?
It's a reddish reddish yellow flag.
Reddish.
Yeah.
You you can ask Isn't it isn't it kind of aninteresting thought experiment, though, to to
think about these things even if it's it's kindof a misnomer?
(22:20):
Yes.
And that's why I didn't say it's a full redflag.
But if all they do is they just look at you andsay, Hey, what's your moat?
That's kind of a red flag.
But if they ask in a slightly more likethoughtful way with some like kind of other
threads to pull on, I think it's a perfectlyacceptable question of like, how do you want to
develop develop a durable business over time?
(22:40):
It's like a perfectly reasonable question.
Or like, what do you view as how you defendagainst some big players?
What do think your unique advantage is?
Like, I I don't know.
What if all they do is just ask for the moat,you're probably talking to an associate who's
just on tech Twitter and is just kind of askingstock stock questions.
No offense to associates.
(23:01):
So so I'm gonna ask you a kind of difficultquestion, which is to take off your startup
founder and CEO hat and just look at it, noteven from a venture side, but from a asset
allocator side, let's say you're a familyoffice, you're an institutional investor.
How would you play this, quote, unquote, AImarket?
Are you trying to like, how would you investinto the AI space?
(23:22):
Would you do kind of like a spray and pray andknow that something's gonna hit very big?
Are you focusing on a co kind of thematic, acouple themes?
Or how would you play how would you invest inthe space if you're an asset allocator?
Yeah.
Let me caveat by saying I'm not an assetallocator.
So definitely not the best person to ask thisquestion to.
(23:46):
Yeah.
I mean, I'd say, number one, I don't reallybelieve that there are that particular, like,
defensible advantages of any part of the thestacks, like the application layer, the kind
of, like, infrastructure layer, the hardwarelayer.
Like, I think a lot of the same things are allpresent across all of them.
So, like, I would be interested in havingexposure across the different layers of the
(24:09):
stack and not just investing in only one.
And then I think within each layer, I think,honestly, to some of like the themes of what we
talked about before, I think history doesn'trepeat itself, it rhymes.
And I think all of the same best practices ofhow we'd take the best companies and the
founders in those each particular area are justgonna be true today and look to stick your
fundamentals that way.
(24:30):
Look for really, really, really great founders,really, really great teams who are solving a
really important problem.
And that's really the best that you can do.
And there's going to be ones you miss on,there's going be ones you hit home runs on.
But if you just keep indexing on that, ifpeople are really great people solving sharp
problems, you'll eventually do pretty good inthe long term.
And I think it isn't really reinventing thewheel of what exactly that you're looking for.
(24:51):
I think the one thing to be caution on is justhow crazy some of these rounds can get in our
space and knowing if that makes sense for whatyour goals are of a particular institution or
firm.
Like USB is a great example.
They don't really do any growth stage.
They're pretty focused on Series A mostly withsome Cs, some Bs.
And they're looking for contrarian bets, theyalways have.
(25:13):
And they're not going to be chasing thisbillion dollar round, raising a Series B
billion dollar round in vets because that'sjust the that is how they've made their hay.
That's how they know their that's what theyknow they're great at.
And that isn't the way that they're set up as afirm to win is getting into those billion
dollar rounds.
So I think you just have to operate with yourconstraints and mostly stick to the same
fundamentals that have worked in in softwareinvesting for a decade.
(25:35):
Yeah.
It's an interesting take because essentially,it's a new market.
It's obviously large, but you have to focus onyour controllable variable, which is backing
the best managers and backing the best foundersand let them take you to the promised land,
take you to the next trillion dollar business.
Nothing about AI fundamentally changes the ABCsof investing, is backing the best talent, going
(25:57):
after the best opportunities.
Exactly.
And then within the constraints of of whateveryou're doing as an asset allocator of of what
types of firms and stages you wanna be givingallocation to, or if you are one of those VC
firms, you know, what types of rounds andstages you're going after within those
constraints, it's gonna be mostly the samefundamentals.
Well, Eric, this has been a great deep dive onconsensus.
(26:19):
Congrats on everything that you've done andlook forward to continuing conversation live.
Yeah.
Much appreciate.
Thanks for having me on, David.
Check us out at consensus.app.
Yes.
How should people follow you and keep up todate on consensus?
Yeah.
Follow you can sign up and create a freeaccount if you wanna check out the product at
consensus.app, app, or follow us on Twitter atconsensus n l p.
(26:40):
You'll see lots of product updates andinteresting musings on AI and science products.
Thank you, Eric, and appreciate you jumping on.
Appreciate it, Dave.
Thanks for having me.
Thanks.
Thanks for listening to my conversation.
If you enjoyed this episode, please share witha friend.
This helps us grow.
Also provides the very best feedback when wereview the episodes analytics.
Thank you for your support.
Popular Podcasts
24/7 News: The Latest
The latest news in 4 minutes updated every hour, every day.
Dateline NBC
Current and classic episodes, featuring compelling true-crime mysteries, powerful documentaries and in-depth investigations. Follow now to get the latest episodes of Dateline NBC completely free, or subscribe to Dateline Premium for ad-free listening and exclusive bonus content: DatelinePremium.com
The Clay Travis and Buck Sexton Show
The Clay Travis and Buck Sexton Show. Clay Travis and Buck Sexton tackle the biggest stories in news, politics and current events with intelligence and humor. From the border crisis, to the madness of cancel culture and far-left missteps, Clay and Buck guide listeners through the latest headlines and hot topics with fun and entertaining conversations and opinions.