Opinion: “We have become used to talking about an ‘attention economy’, but perhaps we should think more of the ecology of attention” – 11 Aug 2022 | Mike Follett

Follett: we need to talk about the carbon cost of attention


We have become used to talking about an ‘attention economy’, but perhaps we should think more of the ecology of attention.

It’s hot. It’s too damn hot. It’s so hot that villages on the outskirts of London are burning up in wildfires. It’s so hot that even climate change sceptics like Professor Byron Sharp might be changing (or re-changing) their mind about the reality of climate collapse. It affects us all; we’re all implicated, and it’s all of our responsibilities to do something about it.

source: Follett: we need to talk about the carbon cost of attention – The Media Leader (the-media-leader.com)

The advertising industry is definitely part of the problem, but we can also definitely be part of the solution. And if we are clever, the solution we come up with may be better than what went before.

The first thing to do is admit that advertising is contributing to the climate crisis. I don’t mean that we’re responsible to creating unsustainable demand: our tardy, apish industry is better at directing—rather than manufacturing—desire. What I mean is that advertising itself produces a lot of CO₂—in our offices, in our production practices, and crucially in our media buying.

The carbon cost of media buying is a novel idea, but pretty obvious when you think about it. As the good people at Scope3 have begun to point out, digital advertising is a significant polluter in itself: millions of phones receiving billions of ads after trillions of ad auctions every day use up a lot of electricity, which, in turn, requires a lot of carbon dioxide to be pumped into the air.

All that energy for so little engagement

What’s especially tragic is that when the ads finally reach our devices we often ignore them. We incur a definite (carbon) cost but only achieve a potential attention gain. Lumen’s eye-tracking research has shown that, for some formats, as few as 9% of impressions that reach the screen end up being looked at. All that energy for so little engagement.

But there is hope. Not all ads get ignored: different formats, publishers, and platforms are much better or worse at turning the opportunity to see an ad into actual viewing. When this ‘attentive seconds per thousand’ data is combined with ‘cost per thousand’ numbers, buyers can distinguish the true ‘cost per thousand seconds of attention’ between media alternatives.

And this in turn can be linked to the carbon cost of the media employed, to create a new and potentially powerful means of assessing media: the ‘carbon cost of attention’.

Lumen has been working with Scope3 and Havas to bring this concept to life, launching our ‘carbon cost of attention’ tool at Cannes Lions earlier in the summer. We combine Lumen’s impression-based attention predictions with Scope3’s carbon cost predictions and the pricing information available to a major trading desk like Havas to understand the true financial and ecological cost of the attention that we’re buying as an industry.

Already, we are seeing considerable differences for ads of the same format across publishers:

In the bottom left-hand quadrant of the chart above, we have low attention/low emissions publishers: a sad state of affairs, but not a disaster for the advertiser or the planet. What we want to avoid is shown to the right, an ‘attention desert’: low attention, but high emissions, which is the worst of all worlds. Instead, we should aim for publishers who provide high attention with low emissions: an advertising ‘Garden of Eden’.

An ecology of attention

What puts some publishers in the ‘carbon cost of attention’ good books, and others on the naughty step? Well, there are a number of factors, but some of the biggest include:

1. Clutter: as the chart below shows, the more ads that are served simultaneously on a screen, the less attention each receives. Given that the carbon cost for each ad stays the same, the ‘carbon cost of attention’ therefore skyrockets on cluttered pages.

It’s as if people can’t see the wood for the trees. This is bad for the advertiser (because people aren’t attending to their message), bad for the reader (as they often feel overwhelmed by ads), and, in a bitter irony, bad for the trees.

2. Scroll velocity: the slower people scroll the page, the more attention they give to the accompanying advertising.

Again working with Havas, and this time in partnership with Teads on the Project Trinity report, Lumen has found that attention to advertising is in part a function of how slowly people read a page. This in turn has a knock-on effect on the carbon cost of attention: ‘slow media’ leads to ‘sustainable attention’.

3. Streaming video: video advertising tends to get significantly more attention than static display advertising. But downloading a video to your phone can be fearsomely energy intensive, the increased carbon emissions outweighing the increased attention performance.

This is what is so exciting about streaming video services such as SeenThis, which allow advertisers to achieve all the attention benefits of video advertising at a fraction of the carbon cost.


We have become used to talking about an ‘attention economy’ – the cost of attention and the value of attention are well now established concepts.

But perhaps we should think more of the ecology of attention: one that safeguards the interests of advertisers, publishers, consumers, and the planet.

How popular is ChatGPT? Slower growth than Pokémon GO (source: AI Impact – Author: Rick Korzekwa)

Rick Korzekwa, March 3, 2023

A major theme in reporting on ChatGPT is the rapid growth of its user base. A commonly stated claim is that it broke records, with over 1 million users in less than a week and 100 million users in less than two months. It seems not to have broken the record, though I do think ChatGPT’s growth is an outlier.

source: How popular is ChatGPT? Part 2: slower growth than Pokémon GO – AI Impacts

Checking the claims

ChatGPT growth

From what I can tell, the only source for the claim that ChatGPT had 1 million users in less than a week comes from this tweet by Sam Altman, the CEO of OpenAI:

I don’t see any reason to strongly doubt this is accurate, but keep in mind it is an imprecise statement from a single person with an incentive to promote a product, so it could be wrong or misleading.

The claim that it reached 100 million users within two months has been reported by many news outlets, which all seem to bottom out in data from Similarweb. I was not able to find a detailed report, but it looks like they have more data behind a paywall. I think it’s reasonable to accept this claim for now, but, again, it might be different in some way from what the media is reporting1.

Setting records and growth of other apps

Claims of record setting

I saw people sharing graphs that showed the number of users over time for various apps and services. Here is a rather hyperbolic example:

That’s an impressive curve and it reflects a notable event. But it’s missing some important data and context.

The claim that this set a record seems to originate from a comment by an analyst at investment bank UBS, who said “We cannot remember an app scaling at this pace”, which strikes me as a reasonable, hedged thing to say. The stronger claim that it set an outright record seems to be misreporting.

Data on other apps

I found data on monthly users for all of these apps except Spotify2. I also searched lists of very popular apps for good leads on something with faster user growth. You can see the full set of data, with sources, here.3 I give more details on the data and my methods in the appendix.

From what I can tell, that graph is reasonably accurate, but it’s missing Pokémon GO, which was substantially faster. It’s also missing the Android release of Instagram, which is arguably a new app release, and surpassed 1M within the first day. Here’s a table summarizing the numbers I was able to find, listed in chronological order:

ServiceDate launchedDays to 1MDays to 10MDays to 100M
Netflix subscribers (all)1997-08-29366941857337
Netflix subscribers (streaming)2007-01-15188923513910
Instagram (all)2010-10-0161362854
Instagram (Android)2012-04-031
Pokemon Go (downloads)2016-07-05727

Number of days to reach 1 million, 10 million, and 100 million users, for several apps. Some of the figures are exponentially interpolated, due to a lack of datapoints at the desired values.

It’s a little hard to compare early numbers for ChatGPT and Pokémon GO, since I couldn’t find the days to 1M for Pokémon GO or the days to 10M for ChatGPT, but it seems unlikely that ChatGPT was faster for either.


Scaling by population of Internet users

The total number of people with access to the Internet has been growing rapidly over the last few decades. Additionally, the growth of social networking sites makes it easier for people to share apps with each other. Both of these should make it easier for an app to spread. With that in mind, here’s a graph showing the fraction of all Internet users who are using each app over time (note the logarithmic vertical axis):

Number of monthly users over time for several applications. The vertical axis is on a log scale.

In general, it looks like these curves have initial slopes that are increasing with time, suggesting that how quickly an app can spread is influenced by more than just an increase in the number of people with access to the Internet. But Pokémon GO and ChatGPT just look like vertical lines of different heights, so here’s another graph, showing the (logarithmic) time since launch for each app:

Fraction of total global population with access to the Internet who are using the service vs days since the service launched. The number of users is set somewhat arbitrarily to 1 at t=1 minute

This shows pretty clearly that, while ChatGPT is an outlier, it was nonetheless substantially slower than Pokémon GO4.

Additional comparisons

One more comparison we can make is to other products and services that have a very fast uptake with users and how their reach increases over time:

  1. YouTube views within 24 hours for newly posted videos gives us a reference point for how quickly a link to something on the Internet can spread and get engagement. The lower barrier to watching a video, compared to making an account for ChatGPT, might give videos an advantage. Additionally, there is presumably more than one view per person. I do not know how big this effect is, but it may be large.
  2. Pay-per-view sales for live events, in this case for combat sports, are a reference point for something that people are willing to pay for to use at home in a short timeframe. The payment is a higher barrier than making an account, but marketing and sales can happen ahead of time.
  3. Video game sales within 24 hours, in some cases digital downloads, are similar to pay-per-view, but seem more directly comparable to a service on a website. I would guess that video games benefit from a longer period of marketing and pre-sales than PPV, but I’m not sure.

Here is a graph of records for these things over time, with data taken from Wikipedia5, which is included in the data spreadsheet. Each dot is a separate video, PPV event, or game, and I’m only including those that set 24 hour records:

Records for most sales, views, and users within the first 24 hours for video games, PPV bouts, YouTube videos, and apps, plus a few points for users during first week for apps (shown as blue diamonds). Each data point represents one event, game, video, or app. Only those setting records in their particular category are included.

It would appear that very popular apps are not as popular as very popular video games or videos. I don’t see a strong conclusion to be drawn from this, but I do think it is helpful context.

Additional considerations

I suspect the marketing advantage for Pokémon GO and other videogames is substantial. I do not remember seeing ads for Pokémon GO before its release, but I did a brief search for news articles about it before it was released and found lots of hype going back months. I did not find any news articles mentioning ChatGPT before launch. This does not change the overall conclusion, that the claim about ChatGPT setting an outright record is false, but it should change how we think about it. 

That ChatGPT was able to beat out most other services without any marketing seems like a big deal. I think it’s hard to sell people on what’s cool about it without lots of user engagement, but the next generation of AI products might not need that, now that people are aware of how far the technology has come. Given this (and the hype around Bing Chat and Bard), I would weakly predict that marketing will play a larger role in future releases.

Appendix – methods and caveats

Most of the numbers I found were for monthly users or, in some cases, monthly active users. I wasn’t always sure what the difference was between these two things. In some cases, all I was able to find was monthly app downloads or annual downloads, both of which I would naively expect to be strictly larger than monthly users. But the annual user numbers reflected longer-term growth anyway, so they shouldn’t affect the conclusions.

Some of the numbers for days to particular user milestones were interpolated, assuming exponential growth. By and large, I do not think this affects the overall story too much, but if you need to know precise numbers, you should check my interpolations or find more direct measurements. None of the numbers is extrapolated.

When searching for data, I tried to use either official sources like SEC filings and company announcements, or measurements from third-party services that seem reputable and have paying customers. But sometimes those were hard to find and I had to use less reliable sources like news reports with dubious citations or studies with incomplete data.

I did not approach this with the intent to produce very reliable data in a very careful way. Overall, this took about 1-2 researcher-days of effort. Given this, it seems likely I made some mistakes, but hopefully not any that undermine the conclusions.

Thanks to Jeffrey Heninger and Harlan Stewart for their help with research on this. Thanks to the two of them and Daniel Filan for helpful comments.

  1. I also found some claims that the 100M number was inferred from some other figure, like total site visits, and that it might be an overestimate. I haven’t actually seen any sources doing this, so I’m sticking with the original number for now.
  2. I skipped Spotify because at first glance it seemed not to be unusually fast, it didn’t seem very easy to find, and I thought the other apps were sufficient to put things in context.
  3. Be warned that, at the time of this writing, the Google sheet is a bit of a mess and the sources are not cited in the most user-friendly way. If you’d like to use the data and you’re having trouble, please don’t hesitate to ask for a cleaner version of it.
  4. This is still the case if we do not divide by the number of Internet users, which increased by less than a factor of two between 2016 and 2022.
  5. The relevant Wikipedia pages are: