verity•think

It started with a simple question I was grappling with: How is AI traffic changing the way people reach our content, and how do we need to adapt our products and services to keep up?

A few months ago Mohammed Juzer Batliwala joined me and grabbed onto this question (plus a bit more). After reading through a bunch of literature, digging through a lot of data/numbers and having conversations with key individuals, we are now able to share our findings in The Only Human in the Room: What AI agents mean for any organization whose product is information.

Download PDF

For years, we have designed digital experiences assuming that the visitor is a person. We write for human eyes, track human pageviews, and structure our work around human collaboration. But over the past year, the digital infrastructure quietly shifted. If your organization’s product is information, two things are happening at the exact same time: your audience is turning into software, and the way we work together is being fundamentally rewritten.

Here are three major shifts we uncovered in the paper:

1. Your readers are becoming machines

The shift is not theoretical. The proof is already showing up in (open-web) logs. Throughout 2025, the share of overall website visits made by AI tools rather than human beings surged from roughly 1-in-200 to around 1-in-30. The fastest-growing segment of this traffic is not the (massive) background web scrapers of a few years ago. Instead it is AI tools fetching a live page to answer a specific human user's prompt in real time. So, if you publish reports, guidance, analysis, or data, your primary consumer is increasingly an automated middleman acting on behalf of a person.

2. AI browsers are confidently putting your name on answers you never gave

In our research, we ran a structured test comparing three consumer AI browsers on how they handled answering twelve identical questions from an authoritative online source. The results were troubling:

One browser read the live page in 10 out of 12 cases

Another managed to read it only 50% of the time

A third never opened the live page at all. Yet it still provided a detailed response while attaching a citation pointing directly to the source

This is not a technical glitch. It becomes a trust issue. When AI confidently attributes an answer to your organization based on out-of-date training memory or an unread link, your name and reputation end up attached to answers you never actually gave.

3. The hidden cost of the "One-Person Team"

On the organizational side, the productivity gains of AI are real. Recent research shows that an individual working with AI, in the right roles, can match the output of a traditional two-person team. And, while studies also show that AI can make an hour of work more engaging by offloading tedious tasks, it removes the human colleague you would have shared that work with.

The result? Studies across multiple countries have shown a direct link between high AI tool usage and increased workplace loneliness. The work a teammate does can be replaced.The company a teammate provides cannot. Organizations that measure only output will hollow out their human connection and culture without even noticing.

Moving Forward

To navigate this shift, organizations must prepare their infrastructure while protecting their people. Start by publishing machine-readable interfaces, labeling data clearly, and updating crawler policies so AI tools can read your information accurately rather than guessing or misquoting you. Equally important is establishing a clear operational boundary: leverage AI for drafting, synthesis, and routine workflows where errors can be easily reviewed, but keep human judgment firmly in charge of final decisions and high-stakes calls that cannot be undone. Above all, resist measuring value by efficiency alone. By moving fast on technical readiness while moving deliberately on handing over control, organizations can adapt to software-driven readers without stripping away the human connection and accountability that define their work.

Following my recent post, Retracing Our Digital Footprints, I am excited to release the Humanitarian Information Management Historical Milestones of the Past 100 Years report. This research project traces the evolution of our discipline over the last century, starting from the establishment of the International Relief Union (IRU) under the League of Nations to the modern-day Office for the Coordination of Humanitarian Affairs (OCHA).

When I began this journey, my goal was to identify markers that shaped our professional community. While I have documented these milestones by date, I realize that there is so much more behind most of these markers - the stories. My hope is to eventually tell several of these stories in order to provide a deeper context to our shared evolution.

The Roots of Modern Disaster Response

While the formal report focuses on the last 100 years, the foundations of the Information Management (IM) discipline stretch back to pivotal moments like the 1755 Great Lisbon Earthquake which is often cited as the world’s first "modern" disaster because it prompted a shift toward scientific, data-driven analysis rather than viewing catastrophes as divine punishment. A century later, the discipline was further advanced by John Snow’s 1854 mapping of the Broad Street Cholera Outbreak, which demonstrated how spatial analysis and structured data could identify the root cause of a crisis to direct life-saving interventions.

A Century of Innovation

The report organizes 100 years of progress into a framework for future study, providing current and future professionals with a structured way to retrace the origins of their daily tools and appreciate the century of innovation that built the humanitarian IM community.

It tracks the transition from physical tools to the digital ecosystems we rely on today.

I believe that there will be varying ways one could group or analyze the report. For example, one quickly group it into eras such as:

Early Coordination where Charles and Anne Lindbergh conducted the first aerial assessment in response to the Yangtze river floods in China (1932), helping to deliver aid to millions of affected people.

The Rise of Digital during which the Office of the United Nations Disaster Relief Coordinator (UNDRO) launched its first "data bank" to track disaster preparedness and donor assistance (1974).

The Internet Era where we saw a marked shift toward rapid information flow as humanitarians began exploring the potential of the web. This period was defined by high-speed project development, exemplified by the 1996 launch of ReliefWeb, which was built in just six months using shareware and freeware.

The Era of Maturation and Digital Responsibility which marks a transition from the rapid proliferation of digital projects to a focus on sustainability, security and the reality of technological lifecycles. As the sector addresses rising cybersecurity concerns, we are also seeing the planned end-of-life for several legacy initiatives such as HXL and even OCHA’s Information Management Branch.

The Myth of the Single Spark

One of the more interesting findings from my research is that innovation in our sector rarely stems from a single "eureka" moment or a lone genius. Instead, ideas often percolate within an organization or the broader sector for years, undergoing multiple attempts before they finally succeed. I discovered that practitioners who believe they are pioneering a new concept often did not realize their ideas were explored or even "attempted" several times before.

This "percolation" is evident in several of our most recognizable tools:

Humanitarian Information Centers (HICs): While the 1999 Kosovo response is widely celebrated as the birth of the HIC, the Office of the United Nations Disaster Relief Coordinator (UNDRO) established a Relief Co-ordination and Information Center in Chad as early as 1982. This was followed by the Relief Information and Coordination Support Unit (RICSU) in 1985 and the Rwanda information center in 1994, all serving as stepping stones toward the model we knew in the 2000s.

The 3W (Who is doing What Where): The sector frequently cites the 1999 Humanitarian Community Information Center (HCIC) as the origin of the 3W. However, the 1994 Rwanda response utilized a precursor known as the "Matrix". This tool was compiled using shifting technologies throughout the emergency, eventually ending up in a database, yet it is often overlooked simply because it had not yet adopted the modern 3W name.

Financial Tracking: We often discuss the 1992 launch of the Financial Tracking Service (FTS) as a starting point for organized funding data. In reality, UNDRO was tracking donor contributions on a computer as early as 1980.

Understanding that these milestones are the result of long-term percolation, rather than a single spark, gives us a more realistic and encouraging view of how innovation truly happens in the humanitarian field.

Why These Milestones Matter

As I noted in my previous post, looking back helps us understand the trajectory of our discipline. By examining these markers through an innovation lens, we can ask why certain tools emerged when they did and how they impacted disaster relief on the ground.

Whether it was the UNDRO staff using "Le TUBE" pneumatic systems in 1985 to move urgent messages or the birth of, what some consider, the first HIC in 1999, every milestone represents a solution to a specific problem.

I hope this research helps current and future IM professionals retrace the origins of the practices they use every day. By understanding our shared history, we can better appreciate the progress that has built our professional community.

Download the full Humanitarian IM Historical Milestones Report.

Andrej

------

It is official! I am picking up a research project that I originally started in early 2023: milestones and stories from the 100-year evolution of humanitarian Information Management (IM). As we navigate rapid technological shifts and organizational changes, I believe there is significant value in looking back to better understand the trajectory of our discipline.

My Scope: From the League of Nations to Today

The initial focus of this research begins with the founding of the International Relief Union (IRU), as part of the League of Nations, and traces the lineage through the various iterations of UN coordinating entities from UNDRO to DHA to the OCHA we know today.

While the core of my research centers on these primary organizations, the timeline will also encompass innovations from other humanitarian entities when those milestones have significantly influenced the broader humanitarian IM discipline. To maintain a clear focus, I am intentionally excluding fields like Peacekeeping, Human Rights, and Mine Action, as well as agency-specific milestones. As with any research endeavor, I expect this scope may change and evolve as more historical context is uncovered.

My Roadmap: From Matrix to Stories

My first planned output will be a matrix of milestones, similar in format to my 200 Years of Technology Milestones. This will provide a high-level view of the major events, tools, and shifts that have defined our field over the last century - from the first thematic maps of the League of Nations in 1927 to the retirement of the Humanitarian Exchange Language (HXL) in 2026.

Over time, I hope to flesh this out by sharing the narratives and "stories" behind specific milestones. History is rarely just a list of dates; it is the story of people solving problems, like UNDRO staff in 1985 using "Le TUBE" pneumatic systems to rush urgent messages around the office, or what many consider the first use of a "Humanitarian Information Center" during the Kosovo response in 1999.

An Innovation Lens

My larger objective is to look at these historical markers through a lens of innovation. I would like to move beyond a simple chronology and collection of stories to ask the deeper questions:

What specific conditions or technologies enabled these milestones to happen?

Why could they not happen earlier?

How did these shifts fundamentally impact disaster relief efforts on the ground?.

Ultimately, I hope this research helps current and future IM professionals retrace the origins of the tools and practices they use every day. By understanding our shared history, we can better appreciate the 100 years of progress that have built our professional community.

Stay tuned as I begin sharing the first versions of the matrix and the stories behind them.

In my usual posts, I am generally focused on discussing emerging technology and the future. But today, I want to talk about something that took ten years to "load," requires no electricity, and offers a kind of tactile mindfulness that a screen simply cannot replicate.

I am talking about the Palais des Nations - not the one in Geneva, but the one you can build on your desk.

TL;DR: if you are interested in getting your own Palais made from LEGO®, register your interest (link) to help ensure it gets made!

The Backstory: A Decade in the Making

Back in December 2014, while working within the historic walls of the Palais des Nations in Geneva, I had a coffee with CJ Hendrix. Amidst the architectural grandeur of the Palais, we had a "what if" moment: What if we could recreate the Palais out of LEGO® bricks?

Life happened. Careers evolved. But the idea never truly left the "cache."

Fast forward to recently: CJ launched his own custom LEGO kit design firm, BrickSpeak Studio, and that decade-old conversation was rebooted. CJ decided to make the dream a reality, taking the lead on the intricate design and engineering required to capture the Palais into a precise, modular masterpiece.

The "Analog" Advantage

You might wonder why a tech enthusiast is so excited about plastic bricks.

In tech, we deal with things that feel very temporary and not so tactile. We build code that updates, hardware that obsoletes, and data that lives in the cloud. There is a specific, therapeutic joy in analog building. It’s kind of a "system reset" for the brain.

When you build a model like the Palais:

Latency is zero: Every brick you snap into place is a tangible result.

The UI is tactile: You feel the history of the Assembly Hall and the Council Chamber through your fingertips.

It’s offline by design: No notifications, no pings. Just focus.

My Role: The Data Provider

To keep things transparent: I have no financial stake in this project. My involvement is pure cheerleading for something that I think is really cool. Plus, I want to be able to order one for myself.

However, I did get to act as a kind of "archivist." I dug through my personal archives to provide CJ with high-quality, copyright-free photos of the Palais’s hidden nooks and architectural nuances. Seeing those photos transformed into a physical kit has been incredibly rewarding.

Why This Matters

The Palais des Nations represents the heart of modern diplomacy. It’s a place where I spent years of my career and developed a deep respect for its mission. To see it captured in this medium - part art, part puzzle, part history lesson - is special.

The catch? This will only get made if there is enough interest. So, I am sharing this because, selfishly, I want to make sure the order threshold is met. I want to get my hands on one!

-------

Q&A with CJ Hendrix (BrickSpeak)

To give you a deeper look into the process, I chatted with CJ to ask about the "specs" of this build.

Q: We first talked about this way back in 2014. What was the hardest part of finally "rendering" the Palais into bricks after all these years?

The hardest design choices are always which details to include and which to exclude. LEGO models are seldom scale models, but rather evoke the subject, capturing the most important shapes and details here and there. Deciding what to include, and exclude, is a big part of the process. 

Q: Why did you choose the flag gate as the subject?

With such a large and complex building, the primary design difficulty is choosing which part to capture. A large model capturing the whole Palais and grounds would be cost prohibitive, so I had to decide which part is most iconic to most people. I'm sure some people would have preferred the Celestial Sphere and its surroundings, others the newer and very modern H building. But I think for most people the flag entrance is iconic, and it is the entrance I used when I first started working there, so it's near and dear to my heart.

Q: In a digital world, why do you think people are still drawn to these high-end, complex physical builds?

LEGO, the company, has been the largest toy maker in the world for many years and continues to grow. A large part of the growth has been the expansion of models targeting adult builders. I think that says something about our very human need to make things, to just be still and focused and making something with our own hands. 

Q: You also worked in the Palais. Which specific detail in the kit are you most proud of capturing?

I love the fun detail of the peacocks, which was an idea you suggested, I think. They are such a part of both the mythology and day-to-day life of the Palais. I remember them walking around the garden while I'd be out there eating my lunch. I'm also really proud of the flags. The kit will include a sticker sheet that includes all member and observer nations, whose flags are displayed there, so that builders can choose their favorites.  

Q: This isn’t just a toy; it’s a logistical undertaking. What’s the "tech" behind designing a custom kit like this? It's one thing to snap some bricks together to make a model, but it's a whole different thing when I want to produce many copies of a kit. I have to limit myself to parts that are highly available in the LEGO parts market, and the palette I have to work with shrinks considerably. I've written some software that helps me easily optimize the parts I'm using and identify parts which may be problematic to obtain in the quantities I need. I'm constantly figuring out the most cost-effective way to build something, while still evoking whatever thing I'm trying to capture. It's a kind of 3D-puzzle-solving-optimization problem that I deeply enjoy. Most of my design work is done in a specialized CAD tool for LEGO (LDCad), but eventually I always test the building sequence with real parts (I have an ever-growing library of parts to play with). 

-------

How to Get You Own

If you’re a fan of history, architecture, or just need a reason to step away from the monitor for a few hours, check out the project here (https://brickspeak.studio/palais-des-nations/) and use the link at the top of the page to express your interest.

Let’s bring this piece of history home.

We all know that AI is rapidly changing how people interact with information. And, whether we like it or not, AI chatbots will likely become the new “first reader” of the data we publish. For the humanitarian sector it means that:

Donors will increasingly use AI to check funding patterns.

Researchers will feed datasets into chatbots to explore trends.

Journalists will run quick analyses to verify claims.

Students and volunteers will  rely on AI to understand humanitarian operations.

And inside organizations, staff will use these tools to speed up analysis and decision‑making.

This shift creates a change in who interacts with our data. In the past, data was generally handled by experts - specialists who knew the quirks of a specific dataset and could manually correct for "bad data" as they worked. Today, we are seeing the rise of the "Initiator", a concept explained to me by Jerri Husch as non-data experts who use AI to jump straight into exploration. Since they do not have years of experience with the underlying spreadsheets, they might not realize when a merged cell or a cryptic abbreviation has led the AI to a false conclusion. 

So what’s the issue with AI reading humanitarian data? Because most publicly available humanitarian datasets, however well‑intentioned, are not structured in ways that AI systems can reliably interpret. Research showed that even the most advanced AI models can experience up to a 59% drop in accuracy when confronted with real‑world humanitarian datasets that contain common issues: merged cells, missing values, inconsistent formatting, unclear column names, extra rows, or oversized files. This means that AI-generated insights can easily become flawed: incorrect totals, missing rows, invented numbers, wrong trends, or misleading conclusions. Bad data can no longer be overlooked; it must be fixed at the source.

Thus, the question becomes: What can a data provider do to help increase the chances that AI interprets their data correctly? Together with Ralfs Rudzitis, we set out to explore this question with an overall objective of developing practical recommendations for those who provide downloadable* data. 

The good news is that the solutions are practical, accessible, and within reach for organizations of any size. They are:

Keep your data clean and simple. Delete unnecessary columns and rows. Avoid merged cells. Ensure column names are in the first row. Use one table per sheet.

Use clear, human‑readable column names. Use full words (e.g., Donor_Organization, not DonOrg). Avoid cryptic abbreviations or codes unless documented. Use underscores or spaces, not dots.

Provide context and metadata. If you provide an Excel file, consider adding a second sheet that: explains every column, clarifies abbreviations, provides basic guidance on how the dataset should be read.

Favor “wide” rather than “long” datasets. AI chatbots are surprisingly better with more columns and fewer rows than the opposite. If possible, structure or aggregate data accordingly to increase accuracy.

Include instructions in the dataset or accompanying page. Even a short paragraph can prevent major misinterpretations. What the dataset represents. What limitations exist. What filters or assumptions should (or should not) be applied. Which values indicate missing or invalid data.

For more details and a self-assessment checklist, see the full report Optimizing Data for AI Chatbots 2026

Why this matters for the humanitarian sector

Humanitarian data is a public good. We publish it so that people can use it to make better decisions, generate insights, and strengthen accountability. And now, non-experts are increasingly augmented with AI chatbots. If AI is going to analyze our data, and it will, then we must prepare our data for AI. This is not a luxury, but a modern data responsibility.

The good news is that it does not require new budgets, new software or new teams. Just better habits.

I just updated my 200 Years of Digital Milestones list - from Memex to Python.

The additions I made and why they each matter:

1) Memex (1945): As a pre-digital concept, Vannevar Bush’s "As We May Think" paper introduced the Memex - a machine for storing and linking information. It is often cited as the (conceptual) ancestor of the hypertext link and the modern web.

2) Altair 8800 Context (1975): Did you know that Microsoft's very first product was Altair BASIC? The Altair was a pivotal moment....marking the true beginning of the personal computer industry.

3) DNS (1983): I added more detail, including the foundational RFCs 882 and 920. These standards established the distributed architecture that allows the entire internet to function and scale globally.

4) cURL (1996): This seemingly simple command-line tool is the "engine" behind countless scripts and applications, enabling nearly every piece of software to transfer data and talk to APIs over the internet.

5) Python (1991): Starting out out as a hobby over the Christmas season, it has become a global favorite fundamentally changing how developers build everything from data science models to massive web applications.

Understanding such milestones gives us a clearer picture of the technological and conceptual bedrock of the digital age.

Check out the updated list https://veritythink.com/200-years-of-technology-milestones/

The cover image was generated with Google Gemini. Interesting to see how AI connected things!

After sharing my 200 Years of Technology Milestones earlier this month, Michal Smolka and Yurii Arnautov kindly pointed out that I had missed a few obvious milestones. Today, I have corrected those omissions and added even more milestones. While all materials have been updated, below is a summary of each milestone added

Car phone: think about the concept of a "mobile phone". We can trace its origins back to the first true car telephone developed by Bell System in 1946. This was an 36KG (80 pound) piece of equipment that required a transmitter and a handset to work. Not exactly like the sleek smartphones of today, but it laid the groundwork for mobile communication.

The foundation for all modern wireless communication was established with ALOHAnet. Developed at the University of Hawaii in 1971, this was the first system to prove that a shared communication channel could be used by multiple computers. This "random access protocol" is a foundational concept that later influenced everything from Ethernet to cellular and Wi-Fi networks.

Building on this, the U.S. Defense Advanced Research Projects Agency (DARPA) launched the Packet Radio Network (PRNET) project in 1973. This research aimed to create a mobile ad-hoc data network and successfully linked with ARPANET, one of the first examples of "internetworking". In the 1977 tests, PRNET's node was in a nondescript silver van.

In 1980, physicist Paul Benioff published the first-ever description of a quantum computer Peter Shor's algorithm in 1994 showed how a quantum computer could factor large numbers exponentially faster than classical computers highlighting the threat to modern public-key cryptography. In 1998, the first quantum computer was demonstrated.

In 1991, Sony released the first rechargeable lithium-ion battery, which was a true game-changer. It enabled the creation of smaller, lighter, and more powerful portable devices, paving the way for the wireless and electric vehicle revolutions. The battery was based on designs by Akira Yoshino, John B. Goodenough, and M. Stanley Whittingham, who were jointly awarded the 2019 Nobel Prize in Chemistry. At the age of 97, Goodenough became the oldest person ever to receive a Nobel Prize.

Invented in 1994 by Masahiro Hara at Denso Wave, the QR code was originally a solution for the auto manufacturing industry. Traditional barcodes could only store about 20 characters but the QR code could hold up to 4,000 thereby vastly streamlining the movement of parts. The inspiration for the QR code's pattern came from the ancient Japanese board game Go. Denso Wave's decision to make the patent rights open and free is a huge reason for its global ubiquity today.

Google Earth launched in 2005. The concept of zooming from a cosmic view down to a specific location was inspired by the 1977 film Powers of Ten. The technology was originally called EarthViewer, created by a company called Keyhole, and was used by the U.S. military and news organizations during the 2003 invasion of Iraq.

In a somewhat scary milestone, bot traffic surpassed human traffic on the internet for the first time in 2024. Perhaps even scarier is that 37% of that traffic came from "bad" bots.

If you see any major milestones missing, please do let me know. It is a great excuse for me to do some more research and keep learning.

For years, I have been fascinated by the stories behind the technological innovations that shape our world. My recent thinking on this subject was (re-)sparked by several works (see below) which illustrated how seemingly isolated discoveries are often interconnected threads in a larger tapestry of progress. This interest sent me down a path of documenting technology milestones, with a general focus around digital, over the past 200 years and resulted in a personal research project: "Technological Milestones of the Past 200 Years."

The report is a comprehensive compilation of the major technological milestones of the last two centuries. It is an exploration of the breakthroughs that have fundamentally reshaped how we live, from the foundational elements of computing and data storage to the rapid evolution of communication and software. Beyond just a list of dates and inventions, I have sought to capture some of the stories and dynamics of change that accompany these advancements.

History is not merely a recounting of the past; it is a vital tool for understanding the present and preparing for the future. I believe this compilation can serve as a public good to help us appreciate the ingenuity that drives technological transformation. It can be used to track innovation cycles, benchmark historical progress, identify interdependencies between technologies, and even help in forecasting future trends. 

I invite you to explore this resource in all its forms. In a spirit of collaboration, the report is not only available in PDF, but as a Google Document, a Google Spreadsheet, and a TimelineJS interactive. You can find everything on my website. I do not want my work locked behind a PDF, a fancy interactive, or an overly restrictive license. I want you to be able to download and use the data as needed.

I welcome your thoughts and collaboration as we continue to explore the ever-evolving story of human ingenuity.

Andrej

Books:

Map of Knowledge: How Classical Ideas Were Lost and Found: A History in Seven Cities by Violet Moller

Invention and Innovation: A Brief History of Hype and Failure by Vaclav Smil

The Innovators by Walter Isaacson

The Age of Heretics by Art Kleiner

Recently, I had started down a rabbit-hole of exploring AI podcast generation. Down one of those holes, I got side-tracked when I came across Synthesia - an AI text-to-video generation tool. I quickly asked: Could I create a short video using the Armchair Technologist narrative that I had previously written about?  The results were, frankly, astonishing.

Within about 20-30 minutes of using Synthesia for the first time, I had a polished, lifelike video. The process was remarkably intuitive.  I simply input my text, choose an avatar, fiddled a bit with the layout in a few places, and the AI did the rest, seamlessly synchronizing the avatar's lip movements and voice to my script.  The speed and ease of use were truly impressive, especially for someone with no prior experience in video editing or AI video generation.  Leveraging my pre-existing "Armchair Technologist" narrative certainly helped streamline the process, allowing me to focus on the tool itself. See the results on Youtube or below:

The benefits of this technology are clear.  Synthesia and similar platforms can democratize video creation, empowering individuals and organizations to produce high-quality video content quickly and affordably.  Imagine the possibilities for training materials, marketing videos, personalized communications, and more.  The ability to create professional-looking videos without needing specialized equipment or technical expertise is a game-changer.

However, with such powerful technology comes significant responsibility. The ease with which realistic videos can be created also raises serious ethical concerns.  The potential for misuse, particularly in the creation of misinformation and disinformation, is a real and present danger.  Imagine how easily fabricated videos could be used to spread false narratives, manipulate public opinion, or even incite violence.  The line between genuine content and deepfakes is blurring, and the implications for society are profound.

My brief experiment with Synthesia highlighted both the immense potential and the inherent risks of AI video generation.  While the ability to create high-quality video content quickly and easily is undeniably exciting, it's crucial that we, as a society, address the ethical challenges that this technology presents.  We need to develop robust safeguards against misuse, promote media literacy, and foster critical thinking skills to ensure that these powerful tools are used for good, not ill.  As Armchair Technologists, and indeed as citizens of the digital age, we have a responsibility to engage in these important conversations and help shape the future of AI video generation.

Andrej

----

I am exploring the possibility of automating the creation of AI-powered podcasts by combining different (no or low-cost) services.

My goal is to create a somewhat streamlined workflow that integrates tools like:

AI Podcast Generators: To generate scripts and content outlines from existing content.

Text-to-Speech engines: To convert scripts into natural-sounding audio.

Music libraries: To provide royalty-free background music.

Podcast editing software: To add intros, outros, and transitions.

The below image gives you a concrete idea of what I am thinking about including some of the possible tools or services.

Personal drawing of streamlining AI-powered podcasts

This approach would enable someone to create podcasts without the need for extensive technical skills or (significant) financial investment.

Why this matters:

By automating podcast creation, one could make content more accessible to a wider audience. Podcasts offer a unique format for consuming information, allowing listeners to multitask while engaging with content. This accessibility can benefit individuals with visual impairments, those who prefer auditory learning, and those who simply find it easier to consume information through listening.

I am looking for collaborators who are passionate about AI, podcasting, or no-code solutions. If you have experience with any of these tools or are interested in exploring this project together, please reach out. Share your thoughts and expertise on my LinkedIn Newsletter copy.

Let's discuss how we can leverage the power of AI to make podcasting more accessible and efficient.

Andrej

--------------

While Armchair Technologists can bring a fresh perspective and innovative ideas to an organization, it is essential to recognize the potential pitfalls associated with such individuals. When unchecked, their enthusiasm can lead to misguided decisions and ineffective strategies.

Identifying the Unconstructive Armchair Technologist

To effectively mitigate the negative impact of Armchair Technologists, it's crucial to identify their key characteristics. Building on Michal Smoka’s comment on an earlier Armchair Technologist post, I have come up with a rather long list of traits or behaviours to be aware of. 

Knowledge and Understanding:

Textbook approach: A rigid adherence to theoretical frameworks without considering practical constraints.

Buzzword bingo: Excessive use of industry jargon without a deep understanding of their implications.

Lack of contextual awareness: Inability to assess the unique nuances of a situation and tailor solutions accordingly.

Limited original thought: A reliance on regurgitating existing ideas rather than generating novel solutions.

Attitude and Behavior:

Reliance on titles: Prioritizing hierarchical positions over expertise and experience.

Talk the talk, Can't walk the walk: A tendency to propose solutions without considering the practical challenges of implementation.

Resistance to change: They may be resistant to new ideas or technologies, particularly if they don't align with their preconceived notions.

Overconfidence: They may overestimate their own abilities and knowledge, leading to poor decision-making.

Micromanagement: They may micromanage projects and teams, stifling creativity and innovation.

Tunnel vision: They may focus on a single solution or approach, ignoring alternative options.

Lack of empathy: They may be insensitive to the needs and concerns of others, leading to strained relationships.

Fear of failure: They may avoid taking risks and prefer to stick to the status quo.

Ego-driven: They may be more concerned with personal recognition than with achieving team goals.

It is important to note that not all Armchair Technologists will exhibit every single one of these traits. It is often a combination of these behaviors that can lead to negative consequences. Be mindful of individuals who consistently display several of these characteristics, as they may be more likely to hinder progress and create challenges within the organization.

Such individuals can hinder an organization's progress in several ways. They may promote ill-conceived initiatives that consume valuable resources without delivering tangible results. This can disrupt teams, create tension, and erode trust within the organization. Moreover, their unrealistic expectations and timelines can lead to missed opportunities and a decline in the credibility of the technology function.

Mitigating the Risks

To mitigate these risks, organizations need to foster a culture of critical thinking, prioritize practical experience, and encourage continuous learning. By establishing clear expectations and promoting collaboration between technical and non-technical teams, organizations can ensure that Armchair Technologists contribute positively to the organization's goals.

Individuals can also take steps to avoid becoming an unconstructive Armchair Technologist. Embracing humility, prioritizing practicality, and actively listening to others are crucial. By continuously learning and practicing empathy, individuals can ensure that their contributions are valuable and impactful.

By recognizing the potential pitfalls and taking proactive measures, organizations can harness the positive aspects of Armchair Technologists as I outlined in previous posts in this series 2a Unleashing the Power of Armchair Technologists and 2b the Resurgent Technologist

Andrej

------

Disclaimer:

Google Gemini used to brainstorm and help with drafting

In my initial post in the Armchair Technologist series, I questioned if a former technician could become a Armchair Technologist after a significant period of time not using their technical skills. I raised the question partly as a self reflection. While I have started using the title Resurgent Technologist, I want to give kudos to Craig Williams who referred to such individuals, in comment on an earlier post the series, as a Rusty Expert. And, I found his definition quite clear and easy to understand as he noted that “they retain enough knowledge to envisage a technology in-context and perhaps have the influence to make it happen”.

In today's fast-paced technological landscape, it's easy to overlook the potential of individuals who have transitioned from technical roles to managerial positions. These "resurgent technologists" (or rusty experts) possess a unique blend of technical expertise and leadership skills that can be a significant asset to any organization.

While their day-to-day responsibilities may have shifted away from coding or system administration, their foundational understanding of technology remains. This knowledge allows them to:

Identify Opportunities: Spot potential areas for technological innovation and improvement.

Assess Risks: Evaluate the potential risks and benefits of new technologies.

Make Informed Decisions: Base decisions on a solid understanding of technical feasibility and business impact.

Beyond their technical acumen, resurgent technologists can also play crucial roles in:

Mentoring and Guiding: Sharing their experience and knowledge with younger, less experienced team members.

Leading Innovative Teams: Inspiring and motivating teams to think creatively and embrace new technologies.

Defining Realistic Projects: Setting achievable goals and breaking down complex projects into manageable tasks.

Enhancing Collaboration: Fostering effective communication and collaboration between technical and non-technical teams.

Governance and Risk Management: Ensuring compliance with regulations and standards while mitigating security risks.

To fully leverage the potential of resurgent technologists, organizations should prioritize their continuous learning and development. This can be achieved through:

Training Programs: Providing opportunities to learn about emerging technologies and industry best practices.

Mentorship: Pairing resurgent technologists with experienced mentors to share knowledge and insights.

Exposure to Innovation: Encouraging participation in innovation initiatives and hackathons.

By investing in the development of resurgent technologists, organizations can unlock a wealth of innovation, improve efficiency, and achieve long-term success. These individuals can serve as strategic assets, driving digital transformation and ensuring the organization's competitiveness in the ever-evolving technological landscape.

In my previous post, I introduced the concept of the "Armchair Technologist" from which I have greatly enjoyed the engagement and comments. The concept might initially evoke negative connotations, conjuring images of individuals who lack practical experience but are quick to offer opinions or former technicians who have faded away from the industry. However, it is essential to recognize the potential benefits these individuals can bring to an organization. By embracing their curiosity and enthusiasm, organizations can tap into a valuable resource for innovation and technological advancement. I am planning a few follow-up posts, but I wanted to focus this one (Part 2a) on the benefits that the non-technical-background Armchair Technologist can bring to an organization.

Innovation Catalysts and Knowledge Democratizers

Armchair Technologists possess a unique ability to spark innovation. Their fresh perspectives, unburdened by traditional IT constraints, can lead to groundbreaking ideas. Their curiosity and open-minded approach encourage others to think creatively and explore new technological horizons. While they may not always have the perfect question, their willingness to ask can ignite valuable discussions and uncover hidden opportunities. As no-code and low-code platforms continue to gain power, Armchair Technologists will be empowered to transform their ideas into tangible solutions, democratizing access to technology and fostering a culture of innovation.

A Powerful Alliance: Armchair Technologists and IT Teams

Although Armchair Technologists may not possess deep technical expertise, their enthusiasm and theoretical knowledge can significantly enhance IT teams. They can bridge the gap between IT and other departments, translating complex technical concepts into plain language and fostering effective communication. Their external perspective can help IT teams identify blind spots and challenge long-held assumptions.

The Constructive Heretic: Disrupting for the Better

The convergence of Armchair Technologists and powerful no-code/low-code platforms gives rise to a fascinating phenomenon: the "constructive heretic." These individuals can leverage their technological understanding to challenge established practices and propose innovative solutions that might otherwise be overlooked. As technology rapidly evolves, they can quickly identify and capitalize on emerging trends, potentially outpacing more entrenched teams.

Navigating the Governance Landscape

Harnessing the innovative potential of Armchair Technologists without compromising IT governance and security is a delicate balancing act. Organizations must establish clear guidelines and frameworks that encourage experimentation while ensuring compliance with security and data management policies. Open communication and collaboration between IT teams and Armchair Technologists are essential to strike the right balance between innovation and control.

By embracing the curiosity and enthusiasm of Armchair Technologists, organizations can unlock their potential to drive innovation, democratize access to technology, and navigate the ever-evolving technological landscape.

Planned future Armchair Technologist posts to cover the pros of former technicians, the cons to an organization, and how to avoid becoming a (unconstructive) Armchair Technologist.

Andrej

-------

Disclaimer:

Google Gemini and NotebookLM used to brainstorm and help with drafting

In today’s digital age, technology has seeped into every aspect of our lives, from the mundane to the extraordinary. Yet, there’s a growing trend of individuals who are deeply interested in technology, who follow its advancements closely, and who even discuss it passionately, but who have never actually written a line of code or worked as a citizen developer. I recently coined the term 'Armchair Technologist' to describe such individuals.

For me, an armchair technologist is either (1) someone who projects an air of deep knowledge about technology, yet lacks the practical experience or training to back it up or (2) someone who was once technologically savvy, but has moved into more managerial roles and has not kept up with the rapid change of technology. Similar to the "armchair quarterback" who critiques sports teams from the comfort of their comfy armchair or sofa, the armchair technologist offers theoretical advice that can be superficial and oversimplified, misleading, or even incorrect.

Identifying the Armchair Technologist

So, how can we spot an armchair technologist? In my experience, I would say that an armchair technologist emphasizes theoretical concepts gleaned from articles or social media, rather than hands-on experience building, troubleshooting, or implementing technology. They often imply work will be simple and are “hand wavy” in their explanations. They are often overconfident in what they believe can be done especially if they have a technical team under their command. For example, an armchair technologist might suggest implementing a complex AI solution without considering the necessary infrastructure or data requirements.

Have I become one myself?When I first used the phrase armchair technologist, I was using it as a bit of a tongue-in-cheek way to describe how I recently started to feel with the rapid advancement of AI - given my study of the topic was in the late 90s. But, over time, I started to really question myself: had I become an armchair technologist?

In my self reflection, I came across the Dunning-Kruger Effect, a cognitive bias. It describes the tendency of people with limited knowledge to overestimate their abilities. In the context of technology, someone new to a complex field like cybersecurity or quantum computing might be more likely to make pronouncements based on limited understanding. Some researchers also include the opposite effect for high performers: their tendency to underestimate their skills. I am not sure where I fit exactly. Hopefully more toward the high performer that underestimates themself. Regardless, I realized that I needed to:

Embrace a Growth Mindset: Cultivate a desire to learn and adapt, acknowledging that technology is constantly evolving. Continuous learning is critical.

Seek Reliable Sources: Look for information from reputable sources like tech journals, academic papers, and conferences hosted by industry experts.

Practice Humility: Don't be afraid to admit when I don't know something, and actively seek out opportunities to learn from those with deeper expertise.

By acknowledging the existence of the armchair technologist within ourselves and others, we can promote better communication and understanding within the ever-changing world of technology.

The Future of the Armchair Technologist

Given the rapid pace of technological advancement and the increasing prevalence of user-friendly, no-code/low-code solutions, it is probable that the number of armchair technologists will continue to grow. Bubble recently noted (PDF) that, by 2030, no-code will be the primary solution used to create software applications. As technology becomes more accessible to non-technical users, it can empower individuals to solve problems, automate tasks, and innovate without needing extensive coding knowledge. In an even more aggressive stance Matt Garman, Amazon’s Cloud Chief, recently talked (PDF) about how developers may stop coding when AI takes over in a couple years: “Coding is just kind of like the language that we talk to computers. It’s not necessarily the skill in and of itself. The skill in and of itself is like, ‘How do I innovate? How do I go build something that’s interesting for my end users to use?’”. Essentially, developers will be working at a higher level of abstraction. The traditional triad of designer, developer, and product manager will have collapsed into a single role. Could a savvy Armchair Technologist fit that role?

In future posts, I will delve deeper into the potential benefits and drawbacks of the armchair technologist. How can they can drive innovation and democratize access to technology, but also how they can lead to unintended consequences, such as increased cybersecurity risks and a decline in technical expertise.

Andrej

---------

Disclaimer:

Google Gemini used to brainstorm and help with drafting

While much of the world is buzzing with excitement about the potential of artificial intelligence, quantum computing is quietly taking big steps. And, while quantum computers offer some truly amazing possibilities, such as solving complex problems and accelerating scientific discovery, it is important to acknowledge the darker side especially in regards to our current digital security infrastructure.

In recent months, there have been growing concerns about the potential impact of quantum computing on our digital world. A prime example is the recent news of Chinese researchers breaking RSA encryption (PDF) using a quantum computer. While experts have cautioned against overstating the significance of this achievement (PDF), it serves as a stark reminder of the looming threat.

This threat is amplified by the concept of "store-now-decrypt-later" attacks. Malicious actors could potentially intercept and store encrypted data today, knowing that future quantum computers could decrypt it. This insidious strategy could compromise sensitive information, such as financial records, intellectual property, and personal data.

Mosca's Theorem: A Ticking Time Bomb

In 2023, I started to re-explore the concern surrounding quantum security with Cameron Vrckovnik. During our collaboration, we came upon Mosca’s theorem created by Dr. Michele Mosca. The theorem helps one easily and quickly assess the urgency of the quantum security threat. This theorem introduces three key variables:

X: The time an organization needs to keep data secure.

Y: The time required to migrate to quantum-resistant encryption.

Z: The estimated time to build a powerful quantum computer.

If X + Y > Z, then the organization's data is already at risk. Even if a quantum computer isn't available today, it could be built before the organization can fully migrate to quantum-resistant encryption.

For example, consider an organization that collects and stores biometric data. If this data needs to be protected for 40 years, and it takes 5 years to migrate to quantum-resistant encryption, and a powerful quantum computer could be built in 30 years, then the organization's data is already vulnerable (40+5 > 30).

A Glimpse into the Future

To gain a better understanding of the industry's response to this threat, I attended QuTech’s Quantum for Business Roundtable at TU Delft in November 2023. It was encouraging to see both private companies and public sector organizations actively working to quantum-proof their systems and data. There were still lots of questions and uncertainty. But, organizations with critical infrastructure had accepted both the possibilities and the related challenges.

Given the urgency of the situation, organizations must take immediate steps to prepare for the quantum era. By assessing vulnerabilities, developing migration plans, and investing in quantum-safe technologies, we can safeguard our digital future.

When I started in the humanitarian sector about 20 years ago, I was told a joke that “humanitarian innovation is just doing what the private sector did 5-10 years ago”. Unfortunately, quantum security is so critical that no industry can afford to simply sit back and wait for others to solve their problems. Rather, we will need to work hand-in-hand with the private industry, standards bodies, and government entities to ensure our digital landscape remains safe.

Andrej

------

Details / Disclaimers:

Google Gemini used to brainstorm and support in drafting

As a tech enthusiast, I recently decided to delve into researching the world of digital innovation in my personal capacity. Almost as soon as I began, I started coming across fascinating stories and milestones that have shaped this rapidly evolving landscape. So, I decided to capture them and over time, I have accumulated 48 (draft) pages of milestones and related stories. These milestones include, but are not limited to, data, data storage and collection, computing, major software development, standards, and advances in communication.

Recently, I stumbled upon Google's NotebookLLM, an AI model with a remarkable ability to generate audio output. Intrigued by its potential, I decided to experiment with my research material. The results were nothing short of astonishing. The audio generated was impressively coherent and informative, capturing the essence of my research. It was clear that AI could be a valuable tool for transforming written content into engaging audio experiences.

Given the success of this initial experiment, I have decided to share the resulting podcast-like episode. By doing so, I hope to inspire others to explore the wonderful history of digital innovation and the creative possibilities of AI.

Andrej

Details / Disclaimers:

PDF of my milestones

Podcast created with Google's NotebookLLM

With the hype cycle for Generative AI in full swing, it has become increasingly difficult to cut through all the noise, understand the current realities and identify immediate, practical use cases. Facing that challenge, Nasim Motalebi joined me earlier this year with the intention to better understand the technology, identify appropriate use cases, and produce a paper that would provide a concrete stepping stone for others to hopefully move forward more quickly. We are super excited to be able to share our paper today: Generative AI for Humanitarians.

The paper is aimed at humanitarian practitioners and leaders who would like to gain a general knowledge about Generative AI or would like to gain insight on trending strategies for mainstreaming these tools within their organization. By providing main topics of concern and recommendations, we laid out the landscape of capabilities and potential pathways for safe and responsible adoption of Generative AI.

[PDF]

Leveraging our conversations with a variety of people, from experts in the AI field to practitioners already implementing AI projects in their humanitarian organizations, we were able to explore possibilities and risks; document likely use cases; outline practical guardrails; explore various shortcomings; and provide recommendations for building an AI ecosystem. In the end, we hope that we have provided a practical report that others can use to kick-start their practical, yet responsible, adoption of Generative AI.