CoachCat AI cycling training is the FasCat Coaching AI tool that pushed my Functional Threshold Power up 32 watts in twelve months. That is not a marketing line. That is the number on the file. I am an investor in FasCat Coaching, the company that builds it, and the disclosure goes first. However, the receipts come next.
Yesterday’s ride was the day all of that work showed up. The plan called for a steady Zone 3 hour. Then my legs voted differently. In short, I went full gas for sixty minutes and set two new personal records on the same file. The rest of this post is the workout, the plan, and what the program did underneath.
The workout this CoachCat AI cycling training plan prescribed
My FasCat coach Alli designed the day. The CoachCat AI cycling training plan laid it out on the calendar and pushed it to my head unit. The goal was simple. Build endurance. Hold steady power for an hour at 76 to 90 percent of FTP. That is Zone 3 tempo work.
The exact file the app served up
- Warm-up: 5 minutes at 160W (Zone 1), then 10 minutes at 227W (Zone 2).
- Main set: 60 minutes at 272W (Zone 3 tempo).
- Cool-down: 5 minutes at 192W, then 35 minutes at 227W, then 5 minutes at 160W.
- Total time: about 2 hours.
- Target intensity: steady, sustainable, repeatable.
What happened when the legs voted differently
However, that is not what I rode. From the first pedal stroke, the legs felt rare. The breathing settled fast. In fact, focus locked in by minute three. Furthermore, the data on the head unit said the plan was conservative for the day.
So I pushed. For 60 minutes I gave it everything I had. The result: a new 20-minute record of 339 watts and a new 60-minute record of 323 watts. Also, my FTP bumped to 322 watts to start the year.
What CoachCat AI cycling training did with the new file
Then the platform did the part I cannot do for myself. It reviewed the file, updated my zones for the new FTP, and flagged tomorrow as a recovery day. Also, it pushed a note to Alli. Furthermore, my whole next two weeks rebuilt around the new ceiling.
Why this approach works for me at 50
CoachCat AI cycling training assumes recovery is the limiter at 50, not effort. In fact, at 30 I could ride badly and still gain fitness. Also, at 50 I cannot. The margin between progress and dig-a-hole is small.
The CoachCat AI cycling training system is what watches that margin for me. Furthermore, it sees the trend before I do. Also, when the load is climbing and the freshness is dropping, it tells me. Then it adjusts. Without that loop, I would dig the same hole I dug at 35.
The three signals the app tracks closest
- Training load: the rolling weekly stress score, compared to the prior four weeks.
- Fatigue: how fresh the legs are versus how hard the next workout demands.
- Power decay: whether my late-ride watts are dropping faster than they should.
The AI plus human coach combo
In CoachCat AI cycling training, the AI handles the data. The human handles the judgment. That is the deal. Furthermore, Alli reads the trend every Sunday and writes notes that the algorithm cannot write. Also, she catches the things that look fine on a chart but feel wrong in person.
For example, last month she told me to drop a planned interval block after a tough travel week. The plan still showed green. However, the human read said yellow. She was right. In fact, two days later I was glad I rested.
What CoachCat AI cycling training means for the broader build
Cycling brackets every chapter of my career. The Whinstone years, the AI Factory build, the days when the calendar burns down. In all of it, I rode anyway. The bike is the through-line.
Also, the discipline that this program enforces is the same discipline the build needs. Plan the work. Measure the output. Adjust when reality changes. Then repeat. That is true on the bike and true at Savrn.
The cycling stats that bracket this CoachCat AI cycling training year
- 33,243 miles since 2023.
- 773 rides.
- 607,747 feet of elevation.
- 1,668 hours on the bike.
- 20.9 Mount Everests of climbing.
What to take from this if you are considering CoachCat AI cycling training
If you are choosing whether to try it, the short version: it is worth the trial. However, the long version matters. The tool is most useful when the rider treats the data as input, not as identity. Also, it works best paired with a human coach who reads context.
What I would tell a rider just starting
First, get a real FTP test before you trust any plan. Then connect every data source you already use. Furthermore, do not change the workouts in the first two weeks. The algorithm needs honest input data to learn your patterns. After that, push back when the plan feels wrong. The chat function exists for exactly that.
Also, do not skip the rest days. In fact, the rest day is the workout for a 50-year-old rider. Recovery is when fitness actually builds. Furthermore, treating recovery as a reward is how you dig the hole that costs you six months of progress.
Where the program fits in a busy week
My week is rarely the planned week. Between Savrn build calls, the Pinarello, the dog, and travel, the calendar shifts most Tuesdays. However, the daily check-in only takes a minute. Then the workout for the day is on the head unit, the zones are right, and the cumulative load is honest about where I am.
For example, a typical Tuesday looks like this. Five-minute review in the morning. Push the file to the head unit. Ride at lunch or after work. Upload back automatically. Then the next morning, the loop runs again. That is the entire ritual.
What 32 watts of CoachCat AI cycling training means at this stage
A 32-watt FTP jump in a year is meaningful at any age. For a rider in their fifties, it is rare. Furthermore, the conventional wisdom is that masters athletes plateau or decline. In short, the chart-junk reading of the data says you should be losing power, not gaining it.
However, the actual data says the opposite. Most masters riders are under-training, over-stressed, or both. Also, they are running on plans they downloaded in 2014. In fact, an adaptive plan paired with a real human coach is the single highest-leverage change a rider over 40 can make.
The closing read on a year with this program
For the longer arc, my story page covers the cycling chapter of the career arc. Furthermore, the dispatches feed publishes more rides as they happen. In short, the bike is the data set. The data set is the plan. The plan is what gets faster.
Also, the gear matters less than the consistency. The Pinarello does its job. The Wahoo head unit does its job. Furthermore, the data layer underneath does the harder job of telling me what those tools are actually doing for my fitness. Without that layer, the bike was a hobby. With it, the bike is a measured practice.
Updated: 2026-05-12