Andrew Wilkinson engagement report
@awilkinson - 388K followers on X
Measured over 13 original posts from a 30-day window, last computed on September 1, 2026.
Engagement
A typical post picks up 296 interactions against 388K followers, an engagement rate of 0.076%. Measured over 13 original posts, its engagement rate beats 48% of 3,882 tracked accounts of a similar size, which puts it in the middle of its size range rather than at either end. Posts are seen about 43K times each, and 0.69% of those impressions turn into an interaction. That is about 11.0% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.1 post a day over the last 30 days, with activity on roughly 57% of days. Most posts go out around 14:00 UTC, and Monday is the busiest day of the week. Of the 13 posts sampled, 15% carry an image or video and 8% link out. The account's strongest tracked post pulled 4.5K interactions, about 15x its own typical post.
Measured over 13 original posts from a 30-day window, last computed on September 1, 2026.
Compared with accounts its own size
Andrew Wilkinson's engagement rate beats 48% of the tracked X accounts closest to it in follower count (3,882 accounts, accounts of similar size (decile 6 of 10)). A percentile is spread evenly by construction, so 50 really is the middle of that group and 90 really is its top tenth.
On engagement per impression rather than per follower it beats 32% of the same group. When those two numbers disagree, the gap is about how far its posts travel rather than how people react to them.
Where this sits in the catalog
At 0.076%, Andrew Wilkinson sits above the 25th percentile of the 37,701 accounts in this comparison. That places it in the below the median band, which runs 0.012% to 0.081%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.002% |
| 25th percentile | 0.012% |
| 50th percentile | 0.081% |
| 75th percentile | 0.439% |
| 90th percentile | 2.10% |
| 99th percentile | 156.3% |
This ruler is the whole measured catalog, not a size-matched group: it shows where the raw rate falls across every account we can measure, all of which are large. For a like-for-like comparison, read the size-band percentile above instead. See how the bands are built
Posting timing
This account posts most often around 14:00 UTC, and Monday is its busiest day of the week. The bars below are the catalog-wide pattern, with this account's own busiest slot marked. They do not show how this account performs at each hour: we keep one aggregate per account, not one per hour, so that measurement does not exist in our data.
Show engagement by hour posted, utc as a table
| Hour (UTC) | Vs author median | Posts |
|---|---|---|
| 00:00 UTC | -1% | 53K |
| 01:00 UTC | -2% | 53K |
| 02:00 UTC | -3% | 52K |
| 03:00 UTC | -4% | 55K |
| 04:00 UTC | -6% | 44K |
| 05:00 UTC | -4% | 43K |
| 06:00 UTC | -4% | 50K |
| 07:00 UTC | -5% | 54K |
| 08:00 UTC | -4% | 63K |
| 09:00 UTC | -3% | 72K |
| 10:00 UTC | -2% | 75K |
| 11:00 UTC | -3% | 81K |
| 12:00 UTC | -2% | 90K |
| 13:00 UTC | -2% | 98K |
| 14:00 UTC | -3% | 101K |
| 15:00 UTC | -2% | 105K |
| 16:00 UTC | -4% | 102K |
| 17:00 UTC | -3% | 95K |
| 18:00 UTC | -1% | 88K |
| 19:00 UTC | -2% | 83K |
| 20:00 UTC | -1% | 77K |
| 21:00 UTC | -1% | 69K |
| 22:00 UTC | -2% | 60K |
| 23:00 UTC | -2% | 53K |
Show engagement by day of week as a table
| Day | Vs author median | Posts |
|---|---|---|
| Sunday | +5% | 238K |
| Monday | 0% | 302K |
| Tuesday | -3% | 302K |
| Wednesday | -1% | 257K |
| Thursday | -2% | 250K |
| Friday | -3% | 259K |
| Saturday | +3% | 233K |
Best tweets
- Jul 21, 202615x their median
Today we're releasing Laguna S 2.1, our most capable model to date. It's a 118B total parameter Mixture-of-Experts model with 8B activated per token, a context window of up to 1M tokens, and thinking and no-thinking modes. Capable enough to hold its own against models many times its size. Small enough to run on a single @NVIDIAAI DGX Spark. Laguna S 2.1 is fully open under OpenMDW-1.1, with weights available today on @huggingface https://t.co/xxGeAgo35R
- May 9, 20258.9x their median
This guy from The Netherlands emailed me asking if I'd come on his podcast. I didn't want to, so I used my usual line: "I'll only do it in person in Victoria." Welp, the SOB called my bluff. Flew 12 hours, mic in hand. Well played, @WouterTeunissen. It turned out great: https://t.co/R4zPOzLblp
- Jul 18, 20266.8x their median
https://t.co/y17nfoH19a
- Aug 3, 20264.5x their median
Sam (@sama), I love Codex. But there's this one really dumb/subtle problem. Claude Code FEELS faster. I think this is primarily because as it does each thing, it immediately slams a text update into the terminal/app, whereas Codex keeps everything contained/compressed into a line that has that little side to side fade effect. It's almost like chain of thought - you were doing it in ChatGPT just not showing it, and when you added it people felt like it was smarter/faster. I think it would be worth adding a more verbose mode / experimenting with revealing more of what Codex is doing so that you viscerally feel it ripping.
- Jul 31, 20264.0x their median
“Never forget the six-foot-tall man who drowned crossing the stream that was five feet deep on average.” ― Howard Marks
- Aug 31, 20261.8x their median
Cold emails should look COLD. If it looks: 1. Like a template 2. Like it will take more than 15 seconds to read You are screwed. My best one of all time, that I would send to founders of startups when I was running @Metalab, my design agency: "love what you guys are doing, we should work together." That's it. 60% response rate.
- Aug 13, 20261.7x their median
Grok Bot = Openclaw for Normal People
- Jul 18, 20261.6x their median
Most AI companies are going to zero. You can know the thing, and still lose all your money. For example, you could have the insight that "cars will be a big deal" in the early 1900s, but making money from that insight was far from a sure thing. Over 1,900 US car companies have been started. Almost every single one has failed. To date, only two American car companies have avoided bankruptcy (Ford and Tesla). Right now, most people know AI is going to be a big deal. That is obvious. Making money from it, that's the hard part. Even a few months ago, the killer investment idea would have appeared to be to invest in the frontier labs. But as you can see with the new Kimi model, that can flip on its head in a matter of days. It's still unclear if there is value in the models themselves. Mark Twain put it best: "During a gold rush, it’s a good time to be in the pick and shovel business" Trying to pick winners is near impossible. What's easy, is selling picks and shovels: the GPUs, chips, electricity, and computing required to power the boom. Jeff Bezos likes to invert: instead of thinking about what will change, he instead focuses on what won't change. Here's a few things I believe will continue to be true in the the next five years: 1. People will use tools like Grok, Gemini, ChatGPT, Siri (lol) and Claude at increasing rates. The tools are undeniably useful, and history shows that making a technology cheaper and more efficient doesn’t reduce demand, it expands it. 2. Both training better models (training compute) and using them (inference compute) will require massive data centers. 3. We are majorly compute constrained and even before the AI boom, data center demand was growing rapidly. The choke point for all of this isn’t GPUs, it's power. Behind-the-meter generation, pre-existing high-voltage connections, and sites that can actually get energized in the next 24–48 months are scarce. This makes assets with secured power and land in the right places insanely valuable. And lucky for us, they all went on sale this week. My companies have invested in four stocks that we plan to hold for the long-term: $IREN - Previously misunderstood as a crypto miner. The stock re-rated after they signed a massive 10-year deal Microsoft. I talked about my investment on My First Million in January 2025. $CRWV - Similar to IREN, CoreWeave is one of the leading AI cloud providers. It's already locked in ~$100B in backlog from labs and enterprises that need capacity now, not in three years. Same story as the IREN-Microsoft deal: long-term committed revenue. $NUAI - Owns a huge portfolio of data center sites that are perfect for behind-the-meter development. See my post on this one from yesterday. $SPCX - I'm bullish on orbital compute over the next 5-10 years. And yes, before you yell into your computer any more, let's address some of your bearish points: "WHAT ABOUT LOCAL OPEN SOURCE MODELS?" I've thought about that, and while I agree that increasingly powerful workloads will be able to run locally, I think: 1. The cost of the hardware required to run frontier level models is still out of reach for the average person and will continue to be for some time (two year old Mac Studios with 512GB of ram are selling for $25-$35,000 on eBay). 2. Have you ever used your phone to edit photos in Lightroom or used Apple's AI image playground? Your phone quickly turns into lava. That is the GPU/CPU working overtime to run local models and it absolutely kills battery life. Given the huge portion of AI that occurs on mobile, the need for inference in the cloud (data centers) is going to continue to grow. 3. More and more tasks will be able to run locally with less and less compute/GPU. I won't argue that. But I believe our demand for intelligence is insatiable. If you're trying to win in business or create a life saving drug (or even just trying to file your taxes accurately), you want to use the best model. The question is, at what point does the return on intelligence end? Or do we just find increasingly wild tasks to give it? 4. Even "local" workflows are often hybrid. The heavy reasoning, tool use, retrieval, or multi-agent orchestration still routes to cloud. "WHAT ABOUT DATA CENTERS IN SPACE?" 1. I am a SpaceX shareholder and think orbital compute will be huge if they can pull it off. 2. This will take years, and Elon usually delivers a few years late. 3. Even if orbital works at scale, latency and the multi-year timeline mean it’s additive, not a substitute, for the next wave of demand. Companies will still sign terrestrial leases as insurance. Companies like IREN, Coreweave, and New Era Energy are finite resources that require upfront lock-in. Think of them as the last available industrial warehouses in a crowded city with no other availability where tenants are required to sign 10-year leases. If you want to play, you need to pay. If AI compute is the most in-demand service in the world, and not securing it could cost a company its moat, are people really going to bet that Elon will deliver data centers in space and wait 3-5 years to secure their compute or are they going to hedge their bets and sign deals with terrestrial data centers? My bet is the latter. "BUT AI ISN'T DELIVERING _____" I’m currently spending $30–40k a month on Anthropic and OpenAI credits. I treat it as payroll for an extra 30+ digital employees doing work that would cost $200–300k/month in human equivalents. There is no scenario where this genie goes back in the bottle. The demand is real, it’s compounding, and it’s only getting started. Mark Twain was right about gold rushes. Billions will be made by prospectors chasing the next big model, but most of them will go broke. The boring money is in the picks and shovels: the power, the land, the GPUs, and infrastructure that every serious AI effort will need more of for years to come. So, are you a gold miner or a pick axe salesman? PS: I'd love to hear any rebuttals if I've missed something. --- Important disclosure: I (and entities I control) beneficially own shares of IREN, CRWV, SPCX and NUAI. I wrote this after establishing these positions. I may buy more or sell at any time without updating this post. This is not investment advice or a solicitation to buy/sell securities. This is a high-risk, speculative situation and you can lose all your investment. Forward-looking statements and scenarios in this post are speculative and may not occur. Do your own research and read company filings.
- Aug 18, 2026
Having major issues with Grok @bot. I have about 15 bots and they now frequently say they’ll do something then flake or disappear. Can anyone on the @bot team help?
- Jul 13, 2026
Wow, was I wrong. I was choked when I heard OpenAI was discontinuing Atlas. I just setup @diabrowser from @browsercompany. Atlas now feels like a joke by comparison. Super impressed.
Ranked by total interactions across everything we have tracked for this account, which is a longer history than the 30-day window the rates above use. The multiple compares each post to this account's own median.
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Reading these numbers
A typical post picks up 296 interactions against 388K followers, an engagement rate of 0.076%. Measured over 13 original posts, its engagement rate beats 48% of 3,882 tracked accounts of a similar size, which puts it in the middle of its size range rather than at either end. Posts are seen about 43K times each, and 0.69% of those impressions turn into an interaction. That is about 11.0% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.1 post a day over the last 30 days, with activity on roughly 57% of days. Most posts go out around 14:00 UTC, and Monday is the busiest day of the week. Of the 13 posts sampled, 15% carry an image or video and 8% link out. The account's strongest tracked post pulled 4.5K interactions, about 15x its own typical post.
- What is Andrew Wilkinson's engagement rate on X?
- Andrew Wilkinson (@awilkinson) has an engagement rate of 0.076%, based on the median interactions across 13 original posts from the last 30 days against 388,246 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.076%, Andrew Wilkinson sits above the 25th percentile of the 37,701 accounts in this comparison. Those comparison accounts are all large ones, because our scanning cadence is weighted towards big accounts, so this is a ranking among peers of similar scale rather than a ranking across X.
- Does @awilkinson have real engagement?
- Its engagement rate beats 48% of the tracked X accounts closest to it in follower count (3,882 accounts), which puts it in the middle of its size range group. Ranking inside a size band matters because engagement rate falls as accounts grow, so a raw rate would mostly re-measure the follower count. It is a starting point for a look at follower quality, not a verdict on it.
- When does @awilkinson post?
- Most posts go out around 14:00 UTC, and Monday is its busiest day, at roughly 1.07 posts per day across the measured window.