Nobody can say AI arrived quietly. It’s been loud everywhere: the news, the boardrooms, the comment sections. The Flight Sim community was no exception. Threads exploded, Discord servers went into meltdown, and suddenly every second post on r/flightsim was either “this changes everything” or “I’ve survived 14 years of simming without AI and I intend to keep it that way.” Both reactions were understandable.
What’s actually happened since the noise settled is more specific than that. A handful of real tools have emerged. The most prominent among them are BeyondATC, SayIntentions.AI, SimInstructorAI among them and they solve problems MSFS simmers have complained about for years. Some DIY experiments using SimConnect and Python are impressive. And a lot of the hype was, predictably, just hype. But where stand things actually in 2026?
BeyondATC vs SayIntentions.AI 2026: The AI ATC Battle
MSFS default ATC has been a joke since FS2004 and likely before that. Phantom traffic, nonsensical vectors and clearances that would get a real controller fired. Every simmer knows this. For years the only answer was VATSIM or IVAO. Real human controllers, proper procedures, the full experience. Those platforms are great. Theere are drawbacks as well. First, coverage is patchy. Controllers are volunteers, and unless you’re flying in and out of major European or American hubs at peak hours, there’s a good chance nobody is online at the airport of your choice. Flying into a smaller regional field at 11pm on a Tuesday? You’re on your own. Second, when there is coverage, you have to show up and stay. You can’t pause a VATSIM flight because the kids need dinner. This is why online networks were simply not for everyone. AI stepped into exactly that gap.
The two main players are BeyondATC and SayIntentions.AI. They are competing for the same customers in 2026, which is good for everyone with a wallet. But they’ve arrived at the same destination via fundamentally different technical routes. These differences have consequences for what you pay and what you get.
BeyondATC was built on a traditional software architecture. The core ATC logic — sequencing, phraseology, traffic management — is rule-based and deterministic. Voices are layered on top, over 300 of them with regional accents. The brain of the system, however, is conventional code. This is why BeyondATC can run without an ongoing cloud API bill. You buy it once. Their traffic injection system models over a million real-world flights and runs locally. The recently rewritten arrival sequencing manages flow from over 100 miles out, which finally addresses what simmers call the “conga line on final”. That is a long, unrealistic queue of aircraft stacked up behind each other on approach at busy airports and all of them arriving at identical intervals like a conveyor belt. They have started integrating a custom LLM for richer interactions, but they built that model themselves rather than paying per token to OpenAI or Anthropic (Claude). Expensive to build, but cheap to run forever. That’s why their solutionworks without subscriptions.
SayIntentions took the opposite approach. Built around large language models from day one, every ATC exchange, every co-pilot response, every cabin crew announcement goes through a cloud AI API in real time. This result: interactions feel natural and with completely freeform speech in ways BeyondATC currently can’t match. It’s also why a subscription is not optional. Those API calls cost real money per token, per flight. Not everyone likes that. One simmer put it plainly in a recent r/flightsim thread: “I wanted a solution where I don’t have to talk. I’m sure it’s less feature rich but gets the job done and isn’t a subscription beyond Claude itself.” That trade-off is baked into the architecture and it isn’t going away.
SayIntentions goes considerably wider than ATC. Co-pilot, cabin crew, CPDLC, AI tour guides narrating landmarks as you fly over them, AI-generated missions. The checklist co-pilot can actually interact with the MSFS aircraft and respond to any phrasing you throw at it. The catch is that every response involves a round-trip to a cloud LLM. In practice it’s noticeably slower than a scripted system, which can break the flow during a tight turnaround. That’s not a design flaw, but the direct cost of the architecture behind the software. Anotehr downside: some simmers say that sayintentions adds features over features, but doesn’t focus on the core capability: ATC.
SimInstructorAI: An AI Flight Instructor Trained on Your Aircraft Manual
SimInstructorAI is quieter than both of the above. It’s a flight instructor trained on the actual POH, the Pilot Operating Handbook, the official systems and procedures manual for a specific aircraft, and not from general aviation knowledge. The distinction matters. Mid-flight in the Fenix A320, something odd on the overhead: you ask it and the answer comes from the right document, not a knowledge base that might be approximating. One-time purchase, no subscription. In this market that’s almost a political statement.
Using Claude and ChatGPT for MSFS Pre-flight and In-flight
Let’s be honest: AI doesn’t make pre-flight faster. SimBrief has been generating detailed flight plans for years. Navigraph has been there for charts. The pre-flight workflow has been well-served by dedicated tools for a long time. What AI adds is different: it handles the questions that don’t fit neatly into a tool.
Paste a raw METAR into Claude and ask what it means for a visual approach in a 737. Ask about NOTAMs in plain English rather than the compressed format they’re published in. Ask which gates Lufthansa is using at EDDF, what the real-world flight number is for your route, whether there’s a noise abatement procedure you should know about.
Where it also a time-saver is mid-flight. Something breaks. A light comes on you’ve never seen before. Previously, you’d pause, open a browser, post on a Facebook group, and wait — either for a helpful answer or for three people to tell you to “just Google it”, followed by a six-month-old forum thread that looks like it hasn’t been touched since the Obama administration. Now you describe what you’re seeing in plain English and get a plausible answer in seconds. One simmer from the thread summed it up: “I just use ChatGPT for answering questions regarding systems of the airplane sometimes. Quite good mostly. Sometimes it makes obvious mistakes though.” That caveat stands. AI is not infallible. But for understanding what just happened, it beats waiting for the forum to wake up.
It’s also useful for getting one’s head around an unfamiliar cockpit. Where is the fuel crossfeed on this aircraft? What does this ECAM message actually mean? Not everyone who flies a complex MSFS aircraft has memorised every system, and a reference that speaks English rather than technical manual prose ins much quicker.
Volanta Flight Data Analysis with AI
Volanta has had post-flight logging for a while, and virtual airlines have been running their own analysis dashboards for years. AI doesn’t replace that, but allows to generate insights in split seconds. Export your Volanta flight data with Volanta Sync and drop that CSV into Claude. Ask questions in your local languageand let it build charts. Investigate your vertical speed profile on the last 20 approaches or compare landing rates across the past month in. no time. Show where fuel burn deviated from plan. The data was always there. AI makes it accesible.
Building Your Own MSFS AI Tools with Python SimConnect
Here’s it is clear that AI is a double-edged sword, where things can also go badly sideways.
MSFS exposes live simulation data through its SimConnect SDK, and Python libraries like pysimconnect make it accessible without a computer science degree. Altitude, airspeed, autopilot state, fuel flow, every variable the sim tracks can be read and stored by anyone with enoug patience and a Claude subscription. Connect that to an AI API and the possibilities open up: an MSFS AI copilot that monitors your flight and speaks when something looks wrong, a debrief engine that logs everything and writes a summary, a custom voice interface at a fraction of subscription cost.
However, the reality is harder and the community’s own experiments are honest about that. One simmer started building a Python landing scorer — something that grades touchdown and taxi performance like an airline grading system — and then added, almost as an aside, “but it is in Python and I don’t understand anyway what Claude was doing.” That’s the real DIY AI experience for most people. It works, kind of, until it doesn’t. Debugging generated code that you didn’t write and don’t fully understand is its own particular misery. Another simmer fed Claude the entire Fenix manual and got an interactive HTML checklist page out of it. This is useful, but for the simmer it was clear that “it took a number of iterations and a lot of tokens and a couple sessions to get right. I wouldn’t trust it to get anything right without doing your own review.”
Build Your Own AI Copilot with SimConnect and Claude
The DIY route is more accessible than most simmers realise. SimConnect is Microsoft’s official SDK interface that lets external applications read and write simulator data in real time — the same API that powers tools like FSUIPC, Navigraph Charts, and many third-party instruments. Combined with Claude’s API, it becomes the backbone of a custom AI copilot you actually own and can extend however you like.
The basic architecture is straightforward: a small Python script connects to MSFS via SimConnect, polls the variables you care about, formats them into a prompt, and sends them to the Claude API. Claude’s response comes back as text — which you can log, display, or pipe into a text-to-speech engine. No subscription to a third-party service. No waiting for someone else to ship a feature you want.
What SimConnect Exposes
SimConnect gives you access to hundreds of simulation variables. The most useful ones for an AI copilot are:
| Variable | What it gives you |
|---|---|
AIRSPEED INDICATED | Indicated airspeed (knots) |
AIRSPEED TRUE | True airspeed (knots) |
GROUND VELOCITY | Ground speed (knots) |
PLANE ALTITUDE | Altitude above mean sea level (feet) |
PLANE ALT ABOVE GROUND | Radio altitude / height above terrain (feet) |
PLANE HEADING DEGREES MAGNETIC | Magnetic heading |
PLANE HEADING DEGREES TRUE | True heading |
PLANE LATITUDE / PLANE LONGITUDE | GPS position (degrees) |
PLANE PITCH DEGREES | Pitch attitude |
PLANE BANK DEGREES | Bank / roll angle |
VERTICAL SPEED | Vertical speed (feet per minute) |
AUTOPILOT MASTER | Autopilot on/off state |
AUTOPILOT ALTITUDE LOCK VAR | Selected autopilot altitude |
AUTOPILOT AIRSPEED HOLD VAR | Autopilot speed target |
AUTOPILOT HEADING LOCK DIR | Autopilot heading bug |
ENG THROTTLE LEVER POSITION:1 | Throttle position per engine |
FUEL TOTAL QUANTITY | Total fuel remaining (gallons) |
FUEL TOTAL QUANTITY WEIGHT | Fuel weight (lbs) |
ENG OIL TEMPERATURE:1 | Oil temperature per engine |
ENG OIL PRESSURE:1 | Oil pressure per engine |
FLAPS HANDLE INDEX | Flap lever position |
GEAR HANDLE POSITION | Landing gear handle state |
GEAR TOTAL PCT EXTENDED | Actual gear extension (0–1) |
STALL WARNING | Stall warning active (bool) |
OVERSPEED WARNING | Overspeed warning active (bool) |
AMBIENT WIND VELOCITY | Wind speed (knots) |
AMBIENT WIND DIRECTION | Wind direction |
AMBIENT TEMPERATURE | Outside air temperature (°C) |
SIM ON GROUND | Whether the aircraft is on the ground |
GPS WP NEXT ID | Next waypoint identifier |
GPS WP DISTANCE | Distance to next waypoint (meters) |
What You Can Build
Feed a rolling snapshot of those variables into Claude every few seconds and you have the raw material for almost anything: a debrief engine that reconstructs your approach and grades your performance, a monitoring copilot that speaks up when something looks out of tolerance, a fuel calculator that warns you before you should be thinking about an alternate, or a voice assistant you can ask “what’s my TOD?” in plain language and get a calculated answer back. The Python SimConnect library (available on PyPI) handles the connection with about a dozen lines of code. From there, the only limit is what you ask Claude to do with the data.
Tools you build yourself aren’t polished. For example, they easily break after one of the MSFS sim updates. There’s no support channel. The documentation is whatever you wrote three weeks ago. For simmers with a coding background it’s a great. For most of us, the honest outcome is often a half-finished project and a folder you keep meaning to open.
The Future of AI in Microsoft Flight Simulator
The more interesting question is where this is heading. What isn’t being discussed enough is the impact on aircraft development itself. Building a study-level aircraft for MSFS has always been one of the most demanding things a person can attempt in this hobby. The FlyByWire A32NX is a free, community-built A320 that rivals payware. It took over a hundred contributors including real Airbus pilots and aerospace engineers, years of work, and an enormous amount of domain knowledge that no single person could have. That’s the scale of what serious aircraft development requires. AI won’t change that overnight. Flight dynamics need validation against real performance data. Avionics logic needs engineers who understand the actual systems. A language model that confidently generates plausible-sounding hydraulic system code is not the same thing as correct hydraulic system code.
What AI will do, and what some say is already doing, is lower the bar. The coding work that used to require years of SimConnect experience can now be started by someone with basic Python knowledge and a good prompting habit. Documentation that would have taken months to research can be processed in hours. This could mean more developers entering the space, more aircraft in development, and yes, inevitably, more low-quality releases flooding flightsim.to. Basically AI-generated content that looks convincing in screenshots and falls apart in flight. The community’s ability to distinguish serious work from cheap filler is going to matter more, not less.
For virtual airlines, AI is an opportunity, but maybe also a challenge. It doesn’t seem to be quite clear yet as to wich impact AI will have here. The logbook analysis and route tracking that VAs have always offered as a differentiator is now replicable by any individual with a CSV export. The social community a VA offers, however, still matters. But the technical value is thinner than it was two years ago, and smart VAs might already think about how to integrate AI rather than pretend it isn’t there.
The simmer who replied to our thread with “I agree. Though, SayIntentions fills a gap for me. I can’t go to VATSIM as I have family stuff to do and need a pause button” probably represents a fair share of the sim community. Most AI tools aren’t for the hardcore who’ve been on VATSIM since 2003 and never needed anything else. However, the technology is not going away, the trade-offs are real, and the mess that comes with it is real too. The sim has survived worse, andy maybe it will add more immersion and provide more options on all kinds of fronts for the community.
