In this episode, Al Baker, co-founder of Prose Intelligence, discusses the latest developments in AI, especially agentic AI and OpenClaw, and their implications for OSINT and security. He explores the limitations of current AI models, the risks of misinformation, and the importance of rigorous research practices in intelligence work.
Matthew Stibbe (00:01.681)
Hello and welcome to From The Source. This is the podcast from Blackdot Solutions. I'm your host Matthew Stibbe and today I'm very lucky to have Al Baker who is co-founder at Prose Intelligence. Great to have you on the show Al.
Al (00:15.918)
Thank very much for having me.
Matthew Stibbe (00:17.693)
So I want to start with a question I ask all my guests. I'm a fully paid up geek, so I'm going to ask you what are you geeking out about?
Al (00:28.526)
So I, like a huge part of the internet, people who are terminally online got very, very excited a few weeks ago when what is now called OpenClaw got released. So I think probably the thing that I've been, the thing that's most relevant to work that I've been most excited about over the last few weeks and doing the most kind of thinking about is what the genuine possibilities are for agentic AI as it currently exists in OSINT work and in tech
in the context of tech startups in general.
Matthew Stibbe (01:01.469)
So for the uninitiated, and I'm very partially semi-hemi-demi-initiated into this, what is OpenClaw?
Al (01:11.054)
So OpenClaw is a piece of open source software that appeared seemingly from nowhere a few weeks ago. And it in itself isn't particularly, I mean, it's weird to say it's not innovative because nothing like it has existed before, but all it is really is a way of plugging LLMs into one another and into the internet that gives them capabilities that they haven't had before. So OpenClaw allows an LLM to directly execute commands on your computer. It allows an LLM to directly
access the internet, anything that a human can do on a a computer, an LLM can do on a computer via OpenClaw. One consequence of this is that there are enormous security vulnerabilities attached to this software. It's a very dangerous piece of software to use recklessly, but the counterpart to that, it's also potentially tremendously powerful. It means that, yeah,
and LLM can do anything on your computer that you can, potentially speeding up enormous amounts of tasks, doing huge quantities of admin, boring stuff. Loads of the stuff that, as one of a small number of people in a tech startup, it would be hugely valuable for me to have a robot do for me. The experience has been interesting, fascinating, kind of frustrating in equal measure. The headline is that there is too much hype.
The robots are not here to take your boring admin away just yet. But it genuinely is a matter of when and not if. The fact that the plumbing is there now and the fact that it works in principle is genuinely very exciting.
Matthew Stibbe (02:53.725)
So if you think about the history of LLMs and like, ChatGPT sort of 1.0 was really idiotic and not very helpful. But now we're on a version that actually seems... where is this agentic AI evolution? we on, is this version one or are we on version two, three, 5.2?
Al (03:04.172)
Mm.
Al (03:17.026)
So actually, despite how of exciting I find OpenClaw, I'm pretty skeptical of the... My prognosis for AI isn't that of a terribly smooth rise. I think agentic AI is going to be hard limited by the hard limitations of large language models. It's good that people are reminded of this every now and again. LLMs are not the only kind of AI, they're one kind of AI.
And they're not what you would think would be the most natural kind of AI to deploy in an agentic kind of administrative or ops role. They're a text predictor. Exactly, it's a very, very sophisticated autocorrect and that can do a lot of things, especially in the context of admin and kind of boring administrative
Matthew Stibbe (03:59.517)
The stochastic parrot.
Al (04:15.36)
work where mediocrity is kind of what you're going for. If the the aim of what you're trying to output is the most normal, mediocre kind of, kind of if you don't want to take any risks whatsoever, then an LLM is exactly the way to do it. But as soon as you want to start being remotely creative, like they're not, they're not good at it. And there's no reason to think they're going to be able to get better at it. Similarly, LLMs aren't agentic. If... like LLMs cannot be agentic.
because they're essentially predictive. Like if your raison d'êrtre is to predict the next token in a sentence, then by definition, your being is not about making decisions or creating anything new. So that puts a hard limit to my mind on what we can expect AI to be able to do, but that still leaves an awful lot of room for it to be useful in the short and medium term.
Matthew Stibbe (05:10.14)
I'm
I've been dabbling not with OpenClaw, but the agents in Notion and my business is almost entirely run in Notion. And that has some cleverness. You can ask it to kind of go and get something and then turn it into a table and it kind of can do some agentic things inside Notion, which are helpful. I think the hard limit on that probably is they want to charge an enormous amount of money for it. And it's on an unmeted, you you pay per thing. So suddenly you get a thousand pound bill. I remember years ago when I was a student going on the
Al (05:17.528)
Mmm.
Matthew Stibbe (05:41.328)
pre-cursor of the internet where packet switching networks and I got a bill after a month from BT of like £1,100 and I'm not doing that anymore just because if you've got this unlimited billing. So I think billing is going to be an issue for this. But you have a background in philosophy and it may or may not be in your orbit.
Back when I was a student, I remember reading Marvin Minsky's book, The Society of Mind, and his idea of consciousness was that it was a series of, let's say by analogy, agents doing different things and sort of having communication, we would say, APIs at the moment, today. And I wonder whether LLMs might be one element of a society of artificial intelligence mind, but there might be other AI tools that will be talking to them and engaging with the world in different ways. The world models is the phrase
that's often talked about and you won't have an AI, you'll have a constellation or a family or a community of them doing your stuff.
Al (06:45.165)
So that's, I mean, that's an interesting...
That's an interesting kind of set of questions to start digging into. And it's immediately brought up by the idea of we've all very quickly got very highfalutin here. But when we're talking about agentic AI, it's very important to think about what we mean by agency in this. And a network of complex non-agentic objects doesn't necessarily create agency. Agency doesn't necessarily emerge from
an interacting network of non-agentic objects. Like, what's a really good example? I mean, any kind of computer network, right? If I'm playing an online game with a bunch of other people, these are non-agentic devices interacting and creating a game, but there's nothing, agency doesn't exist in the game. It exists in the people playing the game. So I think there's a little bit more work to do, unless you want to radically redefine what agency is,
there's a little more work to do before we can properly say that AI, as we understand them now, are agentic. Because ultimately, they can't make decisions. One of my favourite, well, I say favourite, one of, I think, the most important questions to ask about AI is whether we can trust it and how we can trust it. And this brings us maybe more neatly into OSINT-related territory.
Matthew Stibbe (07:59.965)
Yes.
Al (08:18.093)
Because one of the things, one of the real promises of AI in open source intelligence, threat intelligence, generally, is that it can do at least some of the work of analysts. So we can take a huge amount of data and it can find the insight in the noise. Now, there is no reason to think that an LLM can't do that. An LLM absolutely can do some of that to some degree.
There is even reasons to think that future LLMs will be able to do it reliably as well or better than human analysts, human expert analysts. But the missing piece and what I think is often overlooked, crucial missing piece is trust here. And it's not possible to trust an AI in the way that it's possible to trust. It's certainly not possible to trust an LLM in the way that it's possible to trust
a human analyst and that's because an LLM essentially has no skin in the game. So if you get in a fight with an LLM, right, an LLM dramatically misunderstands you, frequently happens to me, it wastes hours of my time because it's gone off on a wrong thing or it's not saved something or the context is compacted and it's forgotten what it was talking about and I wasn't paying close enough attention so I don't have the right notes. So, you know, it wastes a huge amount of my time. And my reaction then is I get frustrated at this machine.
But that is completely pointless. It's about as useful as me getting annoyed at my alarm clock if I forgot to plug it in. Right. The alarm clock doesn't have any agency in not waking me up. This LLM doesn't have any agency in frustrating my ambitions for this project. If I was working with a co-developer, with a human programmer, absolutely, I can be disappointed. Right. My trust can be frustrated. I could say, trusted you to do this and you let me down.
Matthew Stibbe (09:56.7)
Yes.
Al (10:11.211)
And I cannot do that with an LLM. And like a tech bro's response to this is going to be, let's just make it, so we'll just make it better. We'll make it so it doesn't make the mistakes. But I really think that crucially overlooks the fact that we need... as much as a big part of agency is responsibility for that agency. And if we can't hold an AI meaningfully accountable for its decisions, then there's a real sense in which it hasn't made any
decision at all, or like in a meaningful sense hasn't made the decision at all.
Matthew Stibbe (10:42.661)
Yes. Well, So in your area of expertise around misinformation, there's a, I think a risk isn't there or a concern that if there's a new technology and you can use it for porn, making money or state action, people will do it, right? And if it...
Al (10:51.565)
Mmm.
Al (11:08.589)
Mm-hmm.
Matthew Stibbe (11:11.517)
I guess, what's your feeling about people using AI to generate misinformation?
Al (11:20.727)
So to be slightly glib, I think if there is a technology which can't be used for porn, for making money or for state repression, then that technology will not be successful. Like that is ultimately what all technologies that last are like able to do, even though that's not their only or kind of, or main purpose. So the question should, one of the huge mistakes I think we made in responding to social media as a civil society is,
that we managed to pretend for a long time that it wouldn't be a tool of like rampant greed, political interest, pornography, vice, illegality, whatever. Because of course it will. This is what successful, societally embedded technologies are put to. And the fact that we took social media companies
at their word when they said, like, no, that's not what these are for. It's not going to be a problem. It's fine. Like that's absolutely, that's absolutely on us. So the, the fact that we're making the same mistake again with AI is depressing to me in the extreme. So yes, AI will be put to all these uses. We should be in front of it. We should be, we should be regulating in advance of it. We're not really, that's our problem.
Matthew Stibbe (12:45.598)
No, No, there's really not any sense of that and though I'm old enough to remember the early days of the internet and the the idealism that existed around it, the internet will root around censorship was a phrase that comes back to me and there were lots of people who profoundly believed that and maybe it does. And Silicon Valley has a different reputation now. I think we talk about tech bros and we talk about you know, the big big companies there and
Al (12:55.478)
Mm-hmm.
Al (12:59.679)
Mm-hmm.
Matthew Stibbe (13:14.818)
I'm, so let's come back to, sorry, I'm going off at a slight tangent out of nostalgia mainly. Tell me about Prose Intelligence. Let's anchor this back in your work in OSINT.
Al (13:31.228)
So, Prose was founded a couple of years ago by me and my friend Jordan, based on tech that Jordan had developed during his career as a journalist and then later as a disinformation researcher for archiving data off Telegram. The tool he developed is still there. It's called Telepathy, still open source, still free, still reasonably well supported, although it's probably due an update. And it's...
And basically we got to a couple of years ago and we realized that no one had come up with a better tool so far of archiving Telegram data, specifically for intelligence use cases and for threat intelligence platforms and that kind of thing. So that seemed to us like a gap in the market. So we set up Prose specifically with a couple of different aims in mind, the things that we thought we could do better in terms of providing data for intelligence, for open source intelligence, for
you know, government research, that kind of thing. The stuff that we thought that we could do better than the rest of the market were firstly in taking the needs of OSINT analysts seriously. A lot of data sources and lot of threat intelligence platforms are marketing platforms that have been repurposed. We wanted to make sure that the needs of OSINT analysts, which are, know, constantly different to the needs of marketing analysts or business analysts, which is where,
you know, one of one of the struggles if you're a data provider is you have to sell the data where where the market is. And, we're a tiny company. So for us, the addressable market in the national security space is enormous. But if you're a huge company, then national security is a drop in the ocean for you. And you're much more interested in servicing like these huge enterprise sectors of, you know,
risk analysis, business analysis, economics, stock market movements, all of that kind of stuff. And that's good, that's fine, but that's not what I love and it's not what I think the important work is. So we wanted to make sure that we were building capacity to support the next generation of open source intelligence for big data. We
Al (15:43.433)
make sure that we are looking at the right data for open source intelligence use cases. We make sure that the data that we're providing is compliant, is rich enough to facilitate all of the fancy analytics tools that these platforms are building. Like there's no point in having a, you were kind enough to say we're not obligated to mention Blackdot but I will mention Blackdot, they have a special, I know they specialise in network mapping, which is an incredibly powerful capability for the OSINT teams that I really wish was exploited more.
One of the reasons it's not exploited more is that the raw data, which is provided into these platforms, isn't rich enough to support it. So really the thing that I think is very important, there's all these fantastic analytics capabilities that people are building, but kind of the missing piece is data that is reliable enough, rich enough, and like is sourced responsibly enough that.
the users of these platforms can use it safely and securely.
Matthew Stibbe (16:44.286)
And that's what you're doing with Telegram data?
Al (16:50.484)
Yes. So, Prose, we specialise... Telegram is our flagship data source, if you like, we've now expanded into a couple of other, fringe social media platforms, some custom, scraping, and so on. We try and get to the platform that's going to be kind of essential for intelligence requirements in a couple of years. So right now we're looking at some smaller platforms that have recently, cropped up, and some
some platforms in non-western contexts.
Matthew Stibbe (17:25.406)
And your area of interest, perhaps intellectual and professional interest, is misinformation. So help me as an outsider understand what you mean by misinformation.
Al (17:40.013)
That's an excellent question because I don't know exactly what I think misinformation is but I do know that, which is is maybe kind of odd for someone who's been working in the field for like five years, but it's certainly not what... I know that what I misinformation is isn't what most people who work in the field think misinformation is and this is probably due to as you said my
professional training as an academic philosopher where I was before I came into the open source intelligence space. But when I came into the OSINT space, it was specifically in the misinformation field. I was hired out of my post-doctor, Logically, where I started as their head of fact checking. They needed someone to come in and take over and try and position
Logically as a third party fact checker to Meta and other social media platforms when that was still a thing that social media platforms were prepared to pay for. So there is an interesting, there's a kind of obvious link there between philosophy and fact checking as such. Well, maybe not obvious, like it's not journalism. I've done some, you know, editorial work
Matthew Stibbe (18:40.753)
academy.
Al (19:01.035)
for academic journals, but it's, you know, it's not really journalism. But what I did have from my training as a philosopher was a really solid grounding in argumentation, logic, and epistemology. So I had a better idea than most of what could be reasonably said about things being true, false, misleading, deceptive, what have you, and what were good and bad ways of arguing
for the truth, falsehood or otherwise of a particular claim. And that was extremely helpful and allowed me to bring, I think a degree of clarity to the job, which still seems to elude quite a lot of people who working in the space, a lot of organisations working in the space, who are, I think still very,
I think a lot of people are interested in misinformation professionally. I kind of deluded about how simple the issue is, or rather how complex the issue is. And this can be illustrated by the, you asked me about definitions earlier. The standard definition of misinformation that you hear from the field is, you'll hear it in this way. You'll hear that we're interested in misinformation and disinformation. And the difference between the two is that misinformation is a falsehood spread recklessly.
So spread without necessarily meaning to spread it. And disinformation is falsehood spread intentionally. When you hear that, that seems like quite a reasonable distinction until you start to use it practically as someone working at the frontline, trying to understand how misinformation operates on the internet. Cause what your unit of analysis there is a social media post. So I see a social media post and I say, okay, this is saying that like the moon is made of cheese. I know that that's false, right?
So I want to decide our top line framework says like what our most, the most important thing we need to decide is this misinformation or disinformation. And this isn't like the vanity of civil, civil organisation companies either. This goes right to the heart of that government capabilities in understanding and responding to this issue. Because if something is disinformation, then it's plausibly a matter for national security. If a state backed actor,
Al (21:26.955)
is deliberately spreading falsehoods with a view to creating disruption, then that's plausibly a matter for national security. If it's misinformation, then it's not necessarily, or it's very arguably not a matter for national security.
Matthew Stibbe (21:42.09)
There are cases where Facilitating or supporting or even just bystanding on misinformation can actually be very useful for people. There's still intent behind it. If people are spreading rumors because you've exerted reflexive control on them to see the world in a certain way, you might not have put that disinformation into the field, but you've created an environment where misinformation supports your objectives.
Al (22:11.751)
Exactly. So you might want to say, so something that so if a Russian outlet posted first the claim that the moon is made of cheese, then that counts as disinformation. But then Joe Bloggs from Southend retweets it. He doesn't mean to spread something that's knowingly false. So is his retweet misinformation or is it still disinformation because the ultimate source is the person who spread it deliberately? So this is what I mean. It's not necessarily that
we don't want to care about intent, but the primary framework through which you're understanding the issue is this dichotomy that for any given social media post, we won't be able to tell which bucket it falls into because we have no idea of intent and we don't know whether the intent transfers between different digital objects. And basically it's clear that we haven't actually thought about how we're going to deploy this in the real work of
of researching misinformation, it's just useless. And no one has done the serious work. Don't want to say no one. There is very good work that is being done in academia in responding to precisely these issues. And almost everyone you talk to in the industry will tell you that we don't really have a clear idea of the phenomenon yet or what it does or how to respond to it. And given that it's been kind of close to the top of the political agenda,
for exactly 10 years at this point, that's a pretty sorry state of affairs. Yeah, yeah, so having worked in the field for 10 years, my big hot take is I still don't think anyone really understands what the phenomenon is that they're trying to address, much less how to address it.
Matthew Stibbe (24:00.564)
I'm... If you haven't even... If we can't... I'm not saying you, but if as an industry you can't even get to a place where the definitions or the understandings are... It shows either a lot of flux and change, I suppose.
Al (24:15.306)
I think it shows a lack of resolve, a lack of will to take an honest look at where we are and to deal with the difficult questions that arise as a result of it. So just to take a really obvious example, if we are talking about Russian propaganda and we're going to talk about why that's bad,
then we also need to talk about the ways in which Britain and our allies also make use of propaganda and the way in which that's better. And instead what we do is we pretend the propaganda is only a tool used by... and that firstly takes us all for idiots, and I don't like being taken for an idiot. And secondly, it makes it much easier for adversarial actors like Russia to
Matthew Stibbe (24:55.615)
those guys.
Al (25:13.31)
point to us and call us hypocrites because obviously we do make use of propaganda and state messaging. But also obviously the ways in which we make use of propaganda and state messaging are better than the ways that Russia do. We do it more transparently with some degree of accountability and with some reference to facts. All of this is good and something we should be proud of. And similarly, we talked briefly about the, I made a glib comment
earlier about the, in the pre discussion about the question of where like marketing ends and misinformation begins.
Matthew Stibbe (25:52.895)
Because of course I'm in marketing and I'm therefore interested in truth, beauty and justice, right?
Al (25:57.695)
Of course. And the question there is a very real one. While people were trying to figure out how to make money out of counter misinformation research and technology, one of the obvious places they tried to go to was reputation management for like big corporations, right? Because you can very, very plausibly say, you know, if someone's telling lies about Amazon or Coca Cola or whatever, it's legitimate for Amazon or Coca Cola, whoever to know about that to have a
you know, way of responding to that and to, know, protect themselves, their shareholders, their business, all the rest of it. However, as soon as you get the capabilities related to misinformation into the hands of a corporate actor like Coca-Cola, reputation management becomes the use case, not truth management, right? Coca-Cola, I hope this isn't a libellous of things to say, Coca-Cola does not care about what's true.
Coca-Cola cares about what's gonna sell Coca-Cola and more power to them. They are not in the business of improving civic discourse, nor should they be. But for that reason, it's a mistake to try and think that Coca-Cola or literally any corporate entity actually has a business interest in what's true, as such.
Matthew Stibbe (27:24.073)
There's a...
I read a book and I'm desperately trying to remember what it was called, but it was talking about systems thinking and how systems are perfectly optimized to deliver the results that they get. And, you know, you don't necessarily have to impute
Al (27:39.955)
Hmm
Matthew Stibbe (27:45.757)
wicked intent to corporations for corporations to do wicked things. They might just be getting there on their own. I don't mean any particular corporation here if anyone's listening, but it, it, it...
This brings me back to thinking about, we started off talking about AI and the challenges and opportunities there. Does the use of technology, the use of AI particularly empower people who wish to spread disinformation? Does it give them a kind of a quantity or a volume or a quality advantage that didn't exist before?
Al (28:27.24)
It does, but it also provides the same advantages to people who want to improve social discourse. I think that the question of empowerment is interesting, but probably isn't central. The more interesting question is the systematic effect of these technologies on, if we're talking about misinformation, on the information environment as such. So the fact that during the rise of social media,
the reason that that came along with the rise of misinformation with what you might call the information crisis is less to do with the fact that people who wanted to tell lies now had an effective tool with which they could tell lies and more to do with the fact that social media became the way that everybody communicated with everybody else about anything for any reason.
And the ways in which social media is designed makes it more likely that using it for those reasons results in poor information quality. So the issue is not, there were all of these people waiting for their opportunity to spread misinformative propaganda and then suddenly, you know, social media arrives or AI arrives and now I have this tool that I can do it. The issue is that we had an information environment with one huge set of problems,
you know, the old bastions of the media, the media establishment, you know, The New York Times, The Sunday Times, The Guardian or whatever, The Mail, Radio 4, they owned the news agenda, right? They owned, they got to define what was important, what was worth talking about, what the middle position was, hugely anti-democratic, hugely problematic. And then we replaced that information environment
with social media and it solved a lot of those problems, but it also creates a whole bunch of new ones because you get rid of all of the institutional safeguards, all of the stuff that made your media houses gatekeep-y.
Matthew Stibbe (30:36.031)
Right, because You worked as a journalist and a fact checker and I've worked as a journalist and met fact checkers and was fact checked to the nth degree by Wired editors and people like that. It's an interesting observation you make. They had the anti-democratic or the kind of monopoly control of information in legacy mainstream media, if we can use that phrase.
But you understood how it worked and it had some process and some methodology and some accountability and I think you're implying that social media as it evolved takes that away.
Al (31:12.711)
It's just.. To some extent, but it also just works differently, right? It's just a different technology to print, TV or the internet. And so just at the most basic level, you would expect it to work differently and expect it to have different results or different impacts. And one of those different impacts is poor information quality. So just to bring it back to your initial question, which was about like the impact of technology, there is a huge impact of technology, but it's less, in my view, less about empowering actors, are more about changing the environment that we're operating in.
Matthew Stibbe (31:15.773)
Right, yes.
Matthew Stibbe (31:43.049)
So we're in this epistemological crisis as a society.
Al (31:48.681)
Excellent news, excellent Excellent use of the lingo.
Matthew Stibbe (31:52.511)
Almost stumbled over it.
What does OSINT have to offer to... against that? What's the prognosis? What do we do about it?
Al (32:07.924)
So OSINTS is...
OSINT has a huge amount to offer. It is the way in which the work that is done that will resolve this crisis will be done. Like OSINT describes the series of methodologies that must be employed in order to understand the misinformation problem as it exists and in order to, you know, analyse relevant information, monitor and respond to it. Basically looking stuff up on the internet, exploiting information on the internet to
discover how information moves around it, what actors are involved in it, where they are, who they are, all of that kind of stuff. It's vitally important. It's a raw, intellectual work of understanding the information environment online. But it's not the only...
Not only is it not the only piece, it's also kind of in a lot of ways has outpaced and outdeveloped... OSINT as a practice and as a community of practitioners has radically outpaced the institutions which it should be serving, which it should be working for, has also radically outpaced the kind of conceptual frameworks that we're trying to use to understand the problems that it's trying to solve. So room full of OSINT analysts
Matthew Stibbe (33:25.801)
Really?
Al (33:36.125)
can do amazing things, even with no technology. Literally give a roomful of OSINT analysts who've been working for a couple of years, a laptop each and a web browser, and they will solve incredible problems for you. But you've got to define the problems that you want them to solve, and you've got to give them the frameworks to provide the solution. So if you tell an OSINT analyst, I want misinformation related to the conflict in Iran, and you don't clearly define what it is that you're looking for,
they're not going to give you useful output and that's not their fault. And it's also going to dramatically limit how useful any intelligence product is. Garbage in, garbage out, you know?
Matthew Stibbe (34:14.367)
I feel like we could talk all day and I'm enjoying this so much, but I think we've got time for one last question. If you were going to give advice to the commissioners, the consumers, the people who are asking the questions of a room full of OSINT analysts, advice about how to use that capability, what is the most important thing that you need to...
Al (34:20.201)
We're running out time already.
Matthew Stibbe (34:44.702)
What would you tell them?
Al (34:54.291)
Give OSINT analysts research questions, not methodological direction. I mean, this is just good research management practice, but I have seen more of it in OSINT than I would like to. It's bad research. It's bad research ethics, bad research practice to tell your people what you expect them to deliver. Tell them the question that you expect them to answer, and then it's their job to come up with the methodology. Also,
OSINT practitioners come from the good old wild west of the internet and of open source software and of like piratical punky investigations. This is wonderful. And it has led to the community being as, interesting, welcoming and useful as it is. However, that way of working doesn't lead to a lot of documentation and OSINT analysts, I think need to understand as a community
far better the importance of documenting your methodology before you do the research, outlining your research questions before you do the research, being clear about when research questions that you embark on don't turn up any results, be just as noisy about your failures as your successes, you know all of these kind of good practices from academic research, which just haven't made their way and other kind of, you know, serious
edgier research and so on, which just haven't really made their way into, the OSINT world, but should and not just because I'm kind of a, a hoary old academic who thinks that everything should be done in the academic way, but because if you're a government buyer of OSINT services, it is much easier for you to spend money or if you're a private, even a private sector, but especially if you're a public sector buyer of open service, of open source intelligence services, much easier for you to spend money
if you know that if asked, your supplier will produce a document that says, this is the methodology we use. This is who we got to review the methodology for its soundness. This is why it's a sound methodology. You know, basically an audit trail, a paper trail that says the results that you've got are, you know, predictable, repeatable, and, you know, based on sound methodological practice. We'll never be able to have a
Al (37:20.701)
peer-reviewed research system in OSINT probably, but that doesn't mean that we shouldn't try and emulate good research practices where they exist.
Matthew Stibbe (37:30.407)
That feels like an incredibly powerful insight for us to close on. And Al, it's been a delight and a intellectual workout conversation. I'm so grateful. I've enjoyed it very much. Thank you so much for joining me today.
Al (37:48.969)
Thank you very much for letting me hear myself speak and never get bored of it. And thank you so much for talking to me.
Matthew Stibbe (37:51.68)
And that brings this episode to a close. And if you'd like to learn more about OSINT, Videris or Blackdot Solutions, please visit blackdotsolutions.com. And thank you very much for listening. Goodbye.