Why Blogs Died

Blogs have mostly died over the last 10 years.

Of course, if you’re reading this, that must be false?

Sure. Yes, there are 600 million blogs and WordPress is the world’s most popular content platform. Technically these are blogs, but not in the way that the word was originally used. If you want to reach and retain an audience, blogs are the worst.

I say this as someone who has blogged on and off for almost 20 years. From an author standpoint, it’s meant to be more than just a publishing platform but a way to maintain, inform and manage an audience. For such a platform to be a success, I believe it needs four things:

  • Content – easy to publish
  • Discovery – easy to find
  • Subscription – easy to follow
  • Engagement – easy to stay engaged

Most blogs have one of these, maybe two. Sure, you’re reading this content, but how did you find it?


Looking at traffic discovery is driven through other channels. Twitter, Reddit, LinkedIn, Google, HackerNews, etc. The people who discover content this way are readers looking to be entertained, or they are looking for an answer to a specific question.

When content is found this way, it’s still useful, but it’s not building or retaining an audience. The discovery is very short-lived. A typical tweet, post or social article is gone in 24 hours, scrolled off the infinite scroll, never to be seen again.

Less than 0.1% will subscribe to the email newsletter and even less will subscribe via RSS.

If your content is discovered, it’s the content itself, not the blog or the author. Google often goes further by simply showing the answers to common questions directly on its result page, limiting the traffic going to the blogs that sourced or provided the answer, without the story or the context. Apple takes over RSS links in Safari and tries to bring you into Apple News that doesn’t respect open RSS feeds.

In the Web 1.0 days, each blog you discover would recommend other like-minded content. This was before SEO was much of a thing. People would have links to others and you could actually discover and surf around the web finding topics and authors of interest. The majority of personal blogs today don’t link to any other site at all.

In terms of discovery, there’s also no way to search for a blog. Contrast that with Podcasts. I can go to numerous apps and discover and subscribe to podcasts on almost any topic. Want to discover photographers, search on Instagram. Love cooking videos? YouTube’s got you covered. Where do I go to search and discover great independent writers?


RSS Buttons of a prior web

The Web 1.0 days had a call-to-action with each article you discovered. You’d find an author through social discovery but then you could easily subscribe. While the underpinnings of RSS are still there, very few people use these tools.

Google, Facebook, Twitter, and others would rather have AI “suggest” and re-sort what I should read, rather than letting me subscribe to the things I care about.

The common solution to blog subscriptions is email. This is bad for a lot of reasons:

  • Privacy – do you want each blogs to have your email
  • Spam – is your inbox the right place to read articles?
  • Format – email is notorious for screwing up images and format
  • Analytics – hard to tell if people read your content unless you pixel track them. Bad, bad, bad.
  • Sharing – Once it’s in email, it’s harder for people to share it without clipping, re-posting or forwarding the email… More spam.


Authors write to create engagement, conversation, and reaction. They want to put forward interesting ideas and discuss them. Outside of news-publications, there are very few independent blogs that have vibrant and healthy discussions.

In reviewing some of my favorite online writers and many that I discovered from “social” discovery sites, the majority have comments turned off or have them turned on with few if any comments.

But wait, there’s more…

Great content platforms should make it possible for content creators to make a living. Not everything is about money but if an author wants to make money off of their writing, the platform should support it.

Banner ads are mostly dead. Paid subscriptions don’t work well for micro-publishers. Twenty years into blogging and there’s still no great solution.

My own experience

I started blogging around 2001-2002. I managed to build an audience and a mailing list and after 10 years I had around 2000 email subscribers. Some of the articles were very popular with hundreds of thousands of views. Not bad perhaps, but also not great when you consider the time invested. When I sold the business that particular blog went away as did that audience.

That’s ok, because I create content for the value that it brings me. This isn’t a direct monetary return. Through my writing I’ve been able to meet lots of interesting people, open many doors of opportunity and those have expanded my critical thinking and my personal network.

When I started this new blog in 2020 it was clear that blogs would be unlikely to provide a good return on my investment in time relative to the blogs of 2001. Rather than writing I decided to turn my thoughts into videos and publish them on YouTube. This came with a lot of hesitation because I consider myself an introvert.

Why YouTube?

When I looked at options for long-form content, there were few. I could write a book, but all the authors I talked to said this wasn’t a good way to build long-term audience. You’d have to focus on writing and publish many books to build an audience. The social platforms are good for short-form content and TikTok is interesting but lacks maturity as a platform.

Podcasts were a consideration but Discovery and Engagement are low in comparison. Less people in the US listen to podcasts than YouTube and the engagement in terms of total number of podcasts is also low. People tend to listen to a handful of podcasts but organic discovery is very low.

I choose YouTube because it had a large and growing audience and in my research there were very few content creators in my niche. In fact, this seems to be true across many different fields and areas. Within a specific niche, there are often less than a hundred creators and often less than a dozen with any substantial audience.

I believe this is true because creating content on YouTube is much harder than on other platforms. While I can compose a tweet in less than a minute, or snap a photo in a few seconds, creating and editing a video takes substantially longer. The result is a high ratio of people interested in consuming content relative to those willing to create it.

So after 8 months, I’m at 5000+ subscribers. Audience growth was very slow at first but it seems to be self-reinforcing. Apart from a traffic bump in July, from a more popular video, the subscriber/day growth appears to be logarithmic.

Unlike blogging, each viewer has multiple ways that they influence future traffic.

  • First is the obvious. Each viewer can directly engage in content: watch, subscribe, like, dislike or comment.
  • Secondly each viewer influences the recommendations of other viewers passively. This is done because YouTube calculates the mutual overlap of both videos and viewers. Said another way, if many people watch videos A, B & C and you’ve watched A & C, it’s very likely that video B will be recommended.
  • The recommendations appear to also be true around creators. So if many people follow creators X, Y & Z and you’ve only been exposed to creator Z & Y, it’s likely that YouTube will recommend popular videos from creator X.
  • Individual searches and watch time also influence YouTube’s understanding of the videos relevance. So engagement has a re-enforcing impact on search and discovery.

YouTube published some information around how this works. This algorithm is an update to the earlier model that they described in 2010 that had a simpler model.

So let’s go back to the list:

  • Content – While content isn’t easy to create, it’s very easy to consume and travels well to mobile devices.
  • Discovery – Google is the largest search engine and YouTube is the second largest search engine. Combined from a traffic and discovery standpoint this is a huge advantage.
  • Subscription – Yes, mostly. You still have to compete for attention from organically suggested content. Even subscribers may not always see your content. While this has some drawbacks it does help ensure that one terrible video doesn’t tank your entire audience.
  • Engagement – While comments on YouTube had been very toxic in the past the trend to better moderation, filtering and engagement appears to be working.

Going forward…

At the start I was using this blog as a place to drop my video scripts. After a few months it became clear that almost no-one was reading or finding this content. Going forward I want to explore content that is complementary to the videos or unique in other ways.

I’d love to see blogs have a resurgence but I don’t see that as likely without a major shift from either Google or Apple. Instead I think that having an omni-channel approach with some elements in text and others in video will allow people to discover and follow along in a more personalized way.

I hope you’ll follow along on the journey.


Increase your creativity 60%

Let’s face it, we’ve all been spending a lot of time sitting in front of Zoom meetings and it’s kinda dumb. There’s an easy fix and there’s research that it not only makes us a little more creative, it makes us a lot more creative.

When you’re trapped in an office, I get it. But so many people are working from home and yet they’re stuck doing the same thing over and over again.

Let’s mix it up…. Grab your phone, headphones and go for a walking meeting.

Why are Zoom meetings so exhausting?

  • We are tuned to watching body language
  • Tuned to eye contact
  • Constantly scanning for where to look
  • In a meeting you can catch glances, share a moment and have little side-bar conversations.

Online meetings don’t really let you do that.

  • Looking for eye-contact but you can’t find it.
  • Looking for micro-expressions but they are harder to see
  • When we’re on video chat we’re always looking at people’s eyes and if they aren’t looking at us, we think they’re ignoring us. Humans are just wired that way.
  • And sometimes the audio is just a little out of sync from people’s lips and it drives our brains crazy.

Don’t get me wrong. I’m a big proponent of turning on the video camera and I think Zoom is a great tool, but everything in moderation and staying in this type of zone all day is mentally draining.

Walking Meetings

I’ve been doing walking meetings for years and walking literally gets you to change your perspective. When you’re walking you’re more focused and tend to listen better. You’re not fidgeting with your phone or refreshing your newsfeed in another tab.

Because you’re not focused on trying to match people’s facial expressions you can focus more on what they are saying. There are countless health benefits to walking meetings, but don’t just do this for your physical health. It’s good for your mental health and creativity too.

I started doing walk & talk meetings about 10 years ago. I’m a big fan of the Show West wing where Aaron Sorkin would take people on a walking journey all while telling a story. The visual cue advances the story and it also gives both sides a chance to talk.

When I would go on a walking meeting with someone you can’t see their facial expressions so you’re really concentrating on what they are saying.

The other reason to go for a walk during your meetings is that it can make you more creative. A lot more creative. According to a study out of Stanford, it can increase your creativity by as much as 60% for tasks where you’re thinking of novel ideas.

They studied the effect of walking on creativity and it doesn’t even have to be walking outside. They got the same results from walking on a treadmill.

So if you’re feeling burned out on Zoom meetings, know that it’s how your brain is wired. Grab your phone, mask, headphones and go for a walk.

design innovation startups

Find Product Market Fit Fast

Product Market Fit is one of the hardest things for an early-stage startup to achieve and it’s a critical step for companies looking to scale and be successful.

Product/market fit means being in a good market with a product that can satisfy that market

Marc Andreesen – Andreessen Horowitz

So the first thing you need to do is to understand your target market. Are you building a product for the automotive market, the food & beverage market, the software or technology market or something else? To find product-market fit, you really need to narrow your market and niche down. Don’t try to make your product solve the problems of multiple markets early on. Identify a core initial target market.

Once you know your target market, make sure you really understand and research it. You can’t possibly expect to satisfy your customers let alone a target market unless you really understand their problems.

In researching a market and the problems within that market many entrepreneurs will start to identify problems. Once you have a couple conversations you’ll see patterns emerge in terms of problems that people are experiencing.

In the early stages of a startup it’s typical to build early solutions to those problems. And when these solutions solve those specific problems you have product-problem fit. You’ve identified a problem and you’ve provided a solution… many entrepreneurs think that they are set and they stop there but finding product-market fit is more complex. You need to ensure that your product doesn’t just solve a specific problem, but rather it solves a problem that is repeatable and consistent across a large market segment.

To do this you need two core things:

  • First: You need a good cross-section of customers across your market. Not just your friends’ circle, but knowing that a good portion of any customers in your target market have a similar problem you can address.
  • Secondly: Your product has to be sticky enough that people are upset if you were to take the product away.

Finding and solving a problem is a great start but to really find product market fit you need to make sure that the problem you’re solving is widespread and impacts a large enough market in a scalable way and that the solution doesn’t feel like a NICE-to-Have but rather a NEED- To-Have.

It’s better to make a few users love you than have a lot that are ambivalent.

Paul Graham – YCombinator

You need people to care and the best way to find out if they do is to ask them. Ask your users how they’d feel if they could no longer use your product. The group that answers ‘very disappointed’ will unlock the product/market fit.

Sean Ellis, who ran early early growth in the early days of Dropbox, LogMeIn, and Eventbrite and adviced that if 40% of your customers would be “Very Disappointed” then you’ve found product market fit.

When thinking about product market fit, it’s worth also considering founder-market fit. Some founders have deep experience with a particular market. Maybe they spent a decade at a large company within the target market so they know the right people and they know the problems that are un-solved. Sometimes having a good founder-market fit can be a huge advantage and investors will consider how well a founder is aligned to a market. On the flip-side sometimes founder-market fit can be road-block. Consider how sometimes only an outsider to a market can realize just how broken a market is. If Uber had deep market experience in the Taxi market they may never have build as disruptive a company.

Finding product-market fit is both one of the most misunderstood and difficult steps for any growing startup. Keeping yourself focused on the customer and how that relates to the larger market will keep your company on track.

innovation technology

GTP3 – Will AI replace programmers?

Huge thank you to those who checked out my last video, I was taken aback by the reaction and it was awesome to interact with so many people from around the world.

Reactions tended to break down into two categories.

  • This is so cool
  • OMG – my job! Will AI replace programmers?

I want to show some additional GPT3 demos and I also explain some of the different types of AI, machine learning, and how it’s amazing and cool but you don’t have to worry about your job, at least not YET! We do however need to worry about AI having bias and being racist, sexist, and more.

Let’s start with some recent highlights…

A context aware dictionary that knows the definition of a word based on the context.
An example of image recognition paired with GPT3 to show good and bad ingredients in a product.

An example of using GPT3 as a function within a spreadsheet.
An example of CSS and layout generation using GPT3 An example of a quote generator based on GPT3
An example of UI generation within Figma using GPT3
An example of using GPT3 to write SQL queries

These demos are amazing progress but in order to unsertand why programmer jobs aren’t in gepordy you need to understand the basics of AI. There are four categories to consider:

  • 1. Reactive machines – Big Blue / Chess-playing – they look at the environment and react.
  • 2. Machines with memory – These are AI’s that can remember things over time and learn from their environment. Self-driving cars are an example of this.
  • 3. Theory of mind – This is when a machine can think and understand that others can think. It can put itself in the shoes of someone else and serve basic needs and functions in a general way. This is called Artificial General Intelligence
  • 4. Self-aware – This is when a machine can has the abilities of the previous categories and can also understand it’s own existance. This is in the realm of science fiction and both categories #3 & 4 are theoretical areas of research and we’re not close to these yet.

GPT3 is mostly #1. While it has a lot of data it’s not designed to remember things from session to session. The model is pre-trained, that’s the PT in GPT and you can think of it as the world’s most sophisticated auto-complete. Similar to how when you start typing and Google completes the sentence. GPT is able to complete questions, code, html files, and more. Because it’s trained on so much data the auto-complete has context but not memory. It’s incredibly good but it’s not perfect and it isn’t tested as valid.

Most of the time the output of GPT3 will be a starting point, not the final product. In the examples above the HTML, SQL, CSS, and text that is produced is most likely to be a starting point but its quality and fidelity, while impressive is unlikely to be a final result.

As I said GPT3 is an amazing piece of technology and I can understand why people may worry about their job. Technology can cause this concern and it’s been going on since Aristotle and Ancient Greeks. Farmers have worried about tractors. Scribes worried about the printing press and mathematicians and typists worried about computers. There’s a term for this is Technological unemployment.

While technology can eliminate or shift jobs it also tends to create new jobs and new opportunities. Even if GPT3 is really good, the world will still need engineers, designers, poets, and creators, perhaps more than ever.

The problem with AI

I tend to be an optimist but there are areas that still need a lot of work when it comes to AI and in particular, bias tends to be a real problem.

Here Chukwuemeka shows an image recognition that isn’t trained with diversity in mind…

This is why diversity in technology is so important and it’s also why we need to be careful about the data that’s driving and powering the worlds most powerful autocomplete. AI tends to work off of large collections of data. This can be imaging data, text data. If we’re not careful about the input data and testing it can produce problems.

In another study out of MIT Joy Buolamwini explores the notion of algorithmic bias and how it can be a huge problem.

Joy has a great TedX talk on this topic if you want to learn more.

As developers start incorporating GPT3 into their products and technologies, it’s important that they consider all sorts of biases that may be in the data.

Bad jokes, offensive ideas, historical inaccuracies, propaganda, sexism, racism, and more. In the billions of tokens that GPT3 has processed, it’s gotten good at auto-completing many things including some that we may offensive, inaccurate, or even dangerous.

Sam Altman – one of the founders of OpenAI touched on this recently in response to Jerome Pesenti, the head of AI at Facebook:

It’s great that OpenAI is taking bias seriously and it’s important that engineers building and incorporating AI into their products consider how their training data may have biases.

Thank you to everyone who watched my last video and checked out this post. I’m incredibly grateful to you for your feedback and comments. If you’re new to the blog, I tend to talk about entrepreneurship, technology, design, so if you like that sort of thing you can sign-up to get updates when I post. You can also subscribe on YouTube if you prefer.