Big Pineapple, the platform behind 1.1.1.1, Gateway DNS, DNS Firewall, AS112, and several other Cloudflare DNS services, stores over 250 billion DNS cache entries at any given time. At that scale, wasting a single byte per entry costs more than 250 gigabytes of memory across our fleet.

Five successive changes to how cache entries are stored in memory cut the per-entry footprint by over 50%. Across our fleet, these changes freed up roughly 100 terabytes of memory, equivalent to the amount of RAM in 130 of our Gen 13 servers. The cache also got faster. Insert throughput rose 43% and lookup latency dropped 19%, as fewer allocations and better memory locality meant we did not trade speed for space.

What we cache

On cold start, Big Pineapple starts out with an empty cache. As DNS queries arrive, the cache fills until it hits its maximum entry count, at which point we evict older or less popular items to make room.

The exact cache size varies by data center. When EDNS Client Subnet (ECS) is in use, authoritative servers return different answers depending on the client's network, so we cache multiple versions of the same query. This increases both the number of entries and the memory each one consumes, making the optimizations in this post especially impactful for ECS-heavy locations.

Each item in the cache is a key-value pair. The key identifies what was queried:

pub struct CacheKey {
    qname: Name,
    qtype: Rtype,
    authenticated: bool,
    tag: Vec<u8>,
}

The value stores the DNS response itself: the answer, authority, and additional record sections, along with metadata like the creation time, a hit counter, and the Time-to-Live (TTL).

pub struct CacheEntry {
    timestamp: UnixTimeStamp,
    pub inception: Instant,
    pub ttl: Ttl,
    pub hits: u32,
    pub answers: Vec<Record>,
    pub authority: Vec<Record>,
    pub additional: Vec<Record>,
    pub errors: Vec<ExtendedError>,
    ...
}

Both structs have room for improvement. Several fields use types that carry overhead we don't need once the entry is stored.

Benchmarking memory usage

To measure the impact of each change, we benchmark by filling the cache with randomly generated entries that roughly match the traffic distribution we see in production: 56% A records, 25% AAAA, and 19% TXT. Each entry contains between one and four records.

TXT records serve as a stand-in for all non-A/AAAA record types in the benchmark. Their size is randomized between 64 and 224 bytes, close to the average response size we see for variable-length record types.

We track memory usage using a custom allocator that wraps Rust’s System allocator and records the number and size of allocations per cache entry. Alongside memory, we measure insert throughput and lookup latency across the full cache flow to make sure memory savings don’t come at the cost of performance.

These inputs approximate production rather than reproduce it exactly. Process memory also depends on traffic mix, cache occupancy, allocator state, and memory used outside the cache. We therefore measured resident memory across production instances during the rollout.

The cost of capacity

Vec<T> stores three fields: a pointer to heap-allocated data, the current length, and the total capacity. When you push an item, Vec checks whether the length exceeds the capacity and reallocates if needed. If there’s room, it just appends the item and increments the length.

Once we store a DNS response in the cache, however, we never modify it again. The capacity field serves no purpose, but still costs 8 bytes per Vec. The over-allocated heap space is wasted as well, as a Vec with capacity for eight items but only five stored leaves three slots unused on the heap.

Using Box<[T]> solves both problems. It can’t grow after creation, so it doesn’t need a capacity field or reserve space for future elements. The same applies to String, which also carries a capacity field. Box<str> drops it.

Each cache entry stores 8 Vec and String fields. Replacing them with Box<[T]> and Box<str> saves 8 bytes per field, 64 bytes per entry. It also eliminates the excess heap memory that Vec reserves for future growth. The combined savings add up to over 15 terabytes with over 250 billion cache entries.

Fewer lists, fewer pointers

Rather than storing the answer, authority, and additional sections in separate lists, we can store a single list with offsets to the start of each section. Since DNS record counts per section fit in a u16, we can use a u16 (2 bytes) for each offset, compared to the 8-byte pointer and 8-byte length that each separate Box<[T]> requires.

This removes two lists, each with an 8-byte pointer and 8-byte length, and replaces them with two 2-byte offsets, saving 28 bytes per entry.

These savings do not always map directly to the number of bytes removed from individual fields. Rust inserts padding to satisfy alignment requirements and rounds a struct’s size up to a multiple of its alignment. Removing a small field can therefore eliminate additional padding. For example, we also packed several boolean fields into a single bitflag. This reduced the surrounding padding, causing the struct to shrink by more than the size of the individual booleans.

Dropping the owner

Each DNS record has an owner, the domain the record belongs to. In many cases, this owner is identical to the domain being queried. For example, a query for example.com A returns two records with the same owner:

$ dig example.com A

;; ANSWER SECTION:
example.com.        300    IN    A        198.51.100.1
example.com.        300    IN    A        198.51.100.2

But when a CNAME is involved, for example, the record owner can differ from the queried domain:

$ dig example.com A

;; ANSWER SECTION:
example.com.        300    IN    CNAME    cdn.example.com.
cdn.example.com.    300    IN    A        198.51.100.1
cdn.example.com.    300    IN    A        198.51.100.2

The DNS wire format handles repeated owners using name compression, as defined in RFC 1035. Rather than encoding the same domain twice, subsequent occurrences store a 2-byte pointer to the first occurrence. A domain like www.example.com can encode just www followed by a pointer to where example.com already appeared in the message.

This works well on the wire, but in our cache we store the full owner name alongside each record. Following compression pointers during cache lookups is expensive on the hot path, so we trade memory for speed.

Most records, however, have an owner identical to the queried domain. For those, we can drop the owner entirely and infer it at read time. When the owner differs, such as the A records behind a CNAME, we store the full name.

pub struct Record {
    owner: Option<Box<Name>>,
    class: Class,
    ttl: Ttl,
    rtype: Rtype,
    data: RecordData,
}

When owner is None, response construction restores the queried domain from the cache key, avoiding a heap allocation. This means the record is no longer self-contained, but the cache key is already available during every lookup. When the owner differs, Some stores a pointer to the full name on the heap.

In practice, most cached records have an owner identical to the queried domain, so the majority require no heap allocation for the owner field.

Enum sizing

Rust enums are sum types: each variant can carry different data, but the enum is always the size of its largest variant.

pub enum Option<T> {
    Some(T),
    None,
}

Option is either Some and holds a value, or None and holds nothing. Both variants take the same amount of memory. The enum stores a tag indicating the active variant, followed by space large enough for the largest variant’s data. When the variant is None, that space is unused.

For record data, it seems natural to store each DNS record type as an enum variant:

pub enum RecordData {
    A(Ipv4Addr),
    Aaaa(Ipv6Addr),
    Txt(Txt),
    Naptr(Naptr),
    Svcb(Svcb),
    // ...
}

But the enum is always as large as its largest variant. In our case, that’s NAPTR at 136 bytes. It stores three variable-length text fields, a domain name, and two integers. As a result, the full enum, including the variant tag and padding, becomes 144 bytes.

An A record only needs 4 bytes, and an AAAA record needs 16 bytes. A and AAAA make up over 80% of our traffic, so most records waste over 120 bytes on padding. Since a single cache entry can store many records this quickly adds up.

Boxing the variants

To solve this problem, we can box the larger variants of the enum, moving them to a separate heap allocation. The enum then stores an 8-byte pointer to the heap, where the data takes up only the size it actually requires.

pub enum RecordData {
    // Small and common variants are stored inline
    A(Ipv4Addr),
    Aaaa(Ipv6Addr),
    // Large variants are stored on the heap
    Txt(Box<Txt>),
    Naptr(Box<Naptr>),
    Svcb(Box<Svcb>),
    // ...
}

For A and AAAA records, this saves 120 bytes per record. Smaller variant types like TXT and CNAME also benefit. They still occupy the 24-byte enum, but their heap allocation is sized to their actual data rather than padded to 144 bytes. NAPTR, the largest variant, actually pays slightly more. It now adds the cost of a heap pointer and allocation overhead. But NAPTR records are rare in practice, so the tradeoff is worth it.

6.png

But boxing the larger record variants introduces costs of its own.

The costs of boxing

Boxing has two costs. The first is allocator overhead. Each boxed variant becomes a separate heap allocation, and allocators round up to the nearest size class. Big Pineapple uses jemalloc, an allocator designed for multithreaded, allocation-heavy workloads. jemalloc groups allocations of similar sizes into fixed-size bins. A TXT record requests 32 bytes and fits exactly into a 32-byte bin, wasting nothing, but an MX record requests 40 bytes and rounds up to 48, wasting 8 bytes.

The second cost is poor memory locality. Without boxing, the record enum values for a cache entry sit in a single contiguous allocation. With boxing, data for each boxed variant lives in a separate heap region. Reading it requires following a pointer, and when that pointer lands far from the rest of the entry, the CPU has to fetch a new cache line. With millions of cache entries, boxed data ends up scattered across the heap rather than packed together.

7.png

Neither cost is catastrophic on its own, but eliminating both, as the next section shows, yields a measurable improvement in both memory usage and lookup latency.

Storing records in wire format

An obvious next step would be to store the full DNS response in wire format, patching only per-client fields like the message ID on each lookup. But this has drawbacks. DNSSEC records are only included when the client sets the DO (DNSSEC OK) flag. Storing a complete wire format message means either caching two variants, one with DNSSEC and one without, or filtering them out of an already-built message. There is also a cost to parsing the full message on every lookup, which the enum approach we just described avoids by storing already-parsed records.

As a middle ground, we store just the record data as raw bytes, while keeping the rest of the cache entry as structured fields. Instead of a list of parsed enum variants, we store the records as a single Box<[u8]> containing each record encoded as a 2-byte length prefix followed by its raw bytes.

8.png

This eliminates the per-variant enum overhead and the boxed heap allocations from the previous optimization. The data also becomes packed contiguously, which improves CPU cache locality. The tradeoff is that records can no longer be randomly indexed. We have to iterate through the buffer sequentially. This adds some complexity for features like round-robin rotation of A/AAAA records, but since record counts per entry are small, the cost is negligible.

When building a DNS response from cached records, most record types can be copied directly from the buffer into the outgoing message. Previously, each parsed record had to be serialized field by field back into DNS wire format. The new layout skips that work for A, AAAA, TXT, and all DNSSEC record types by copying their encoded bytes directly. Only records containing domain names, such as CNAME, NS, MX, and SOA, still require parsing so we can apply DNS name compression. Since records that support direct copying make up the vast majority of our traffic, this change reduces work on the lookup path. Combined with improved memory locality, this reduced cache lookup latency by 5% in our benchmarks.

To build the record data buffer, we write into a reusable scratchspace buffer that persists across cache insertions. Since previous writes have already grown it, the buffer rarely needs to be reallocated. Records vary in size, so we do not know the exact buffer size until they have been serialized. Once the records are in the scratchspace buffer, we allocate a Box<[u8]> and memcpy the data into it. This replaces the separate allocation for each boxed record with one allocation for all record data. It also avoids the waste from shrinking a Vec<u8>, where the allocator may not be able to reclaim the unused tail of the original allocation. In our benchmark, this change alone increased cache insert throughput by 13%.

The results

The production measurements show how the benchmarked per-entry savings translated to whole-process resident memory. The graph below shows p90, p98, and p99 memory usage across Big Pineapple instances. The first dashed line marks the start of the rollout on May 18, 2026, and the second marks its completion across all services on July 6, 2026. Each release introduced one or more of the optimizations described above, so memory usage dropped in steps rather than all at once.

As each release rolled out, restarted instances began with empty caches and consumed more memory as those caches filled. The stable plateaus therefore represent steady-state memory usage better than the initial dips.

9.png

Per-instance memory usage dropped across all percentiles. At p99, memory dropped from 9.3 GB to 5.3 GB, a 43% reduction in resident memory. At p90, memory dropped from 6.5 GB to 3.8 GB, a 42% reduction. Instances with fuller caches saw the largest absolute savings.

In our benchmarks, these five optimizations reduced the per-entry memory footprint from 953 bytes to 420 bytes, a 56% reduction. Per-entry allocations dropped from 1.1 KB to 461 bytes. The reductions measured in production are smaller because resident memory includes the cache alongside all other process data. After the rollouts settled, aggregate working-set memory across the fleet was roughly 100 terabytes lower.

Performance also improved. Cache insert throughput increased by 43%, while lookup latency dropped by 19%.

Metric	Before	After	Change
Per-entry net footprint	953 bytes	420 bytes	-56%
Per-entry allocations	1.1 KB	461 bytes	-58%
Cache insert throughput	625,000 entries/s	893,000 entries/s	+43%
Cache lookup latency	828 ns	670 ns	-19%

We plan to reinvest the freed memory into increasing cache capacity without increasing our memory usage, which improves cache hit rates and reduces upstream query volume. We're also exploring further optimizations to the cache itself.

To learn more about Big Pineapple, see How Rust and Wasm power Cloudflare's 1.1.1.1. If you work on DNS or other large systems, share the optimizations that have worked for you in the Cloudflare Community or on the Cloudflare Developers Discord.

For the past few weeks, I've been playing with gpt-5.6-luna. It is shockingly capable, fast, and smart. I regularly see it do ~100 tps, and rip around my codebase, email, and knowledge base.

Of course, the biggest thing with luna is the cost. I've tried running some fairly complicated research threads, and it's pretty tough to run up a large bill. Even having it search across thousands of emails, I end up with an API cost in the tens of cents.

Courtesy of artificialanalysis.ai

With GLM 5.3, we even have a new option at the Pareto frontier.

When doing coding work, I almost always reach for the most expensive and capable models (Fable 5, 5.6 Sol). So it's been easy to miss the progress the small fast models have made.

---

One thing a few investors I've talked with have mentioned: "It's weird we're not seeing more consumer AI companies. Why is that?"

There's a straightforward answer: token costs.

In the times before AI, the playbook for big consumer apps looked like this...

• create some sort of compelling website which is fairly cheap to run
• attract a bunch of users (typically with some virality)
• raise money, scale to more users
• create an ads marketplace

This roughly describes most of the big consumer companies (Google, Facebook, Snapchat, etc.).1

But what if you want to add AI to your product? Well, now you have some real inference costs on every request! Suddenly the amount of capital required increases dramatically.

A pet eval of mine is to build a daily news site, personalized to me:

research @calvinfo on the internet. figure out what news they might like. build a micro-site with today's top stories, personalized for them. search hn, reddit, twitter, etc.

With the previous generation of models (Sonnet class), you'd spend ~$1 to get anywhere. Charging $30/mo is untenable for a consumer app. There's obviously a lot we can optimize here, but if you're charging what the WSJ or The Economist charges, you'd better be delivering similar value.

But looking at luna, the results are pretty decent, and the average cost is ~$0.10. Now we're talking!

---

Where I think this gets even more interesting is in the world of business.

My Segment co-founder Peter and I were recently comparing notes on a hike. Across his various startups, Peter has seen two kinds of work:

1. the "IQ 180" work. some mad scientist genius type comes up with some crazy solution you've never thought of.
2. the "token spewer" work. being ultra responsive, pushing the ball forward across dozens of different fronts.

Peter runs multiple companies. Beyond Segment, he's raised $100m+ for Charm Industrial, and just recently closed a Series A for Revoy. He's incredibly organized and efficient with his time.

And yet, Peter mentioned that ~95% of the work he does falls into bucket 2. It's hopping on calls. Nudging people. Blocking and tackling.

To be clear, Peter says his companies would be dead-in-the-water today without an IQ 180 technical mind solving the deep problems. Just that most of his work falls in bucket 2.2

I think demand for "frontier-level" models is going to keep compounding. Especially for fields that require novel breakthroughs or discovery (engineering, hard science, model training).

But I also think the demand for "fast/cheap/good-enough" models is just about to take off.

Think of the people you interact with on a daily basis: coworkers, vendors, and customers. Nine times out of ten, you want someone who is super responsive, and just handles things for you. Most of the "human tokens" at companies today are spent this way — hiring skews heavily toward the fast/cheap/good-enough archetype.

There's a lot of work that needs to happen to make fast/cheap/good-enough models a reality for business. New harnesses, prompt injection safety, roles, and permissions. But I'm confident we'll figure that out.

If you're also experimenting with making small models useful, please drop me a line.

Footnotes

1. Amazon and Netflix are the notable exceptions ↩
2. Peter is also being modest here. He's sharp as a tack. ↩

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Graph of streaming speech recognition accuracy

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Image: Amanda Yanagawa

If you’ve ever ridden a train in Japan, there’s a good chance you’ve held a Suica card in your hand and tapped it against a station gate.

Even though it was developed back in the 90s, Suica’s technology is really impressive even by today’s standards. Every time you tap it, a complex transaction happens in the fraction of a second, without needing a battery or live internet connection.

It’s easy to take IC transit cards for granted now that they’re part of everyday life, but let’s not forget how much work went into engineering them several decades ago before cashless or contactless payments were common.

Here’s a breakdown of how these cards work, and the surprisingly bumpy story of how they came to be.

How the cards actually work

Every Suica card has a chip storing two things: a unique card ID, and your current balance. Both of those live on the card itself, rather than a central server.

When you tap your card on a terminal, it reads and rewrites the card locally in an instant, with the entire transaction processed in under 200 milliseconds. For each tap, the card and terminal mutually authenticate each other and generate a fresh encryption key to prevent spoofing.

The cards have no battery and aren’t self-powered in any way. Instead, the reader on the gate emits an electromagnetic field that powers the card just long enough to complete the transaction.

Why not sync with a central server each time? The reason is simply that it would take too long.

With the sheer volume of people passing through Tokyo’s stations each day, calling a server would be extremely risky. Transactions would take longer, causing people to slow down at the already-crowded station gates. And any network issues like latency, packet loss, or server downtime would cause the gates to shut down, which would be disastrous during peak commuting hours.

To prevent that, the card and terminal handle everything between themselves. The individual gates do sync with central servers to save transaction logs, but only periodically, rather than in real time.

How stations worked before Suica

For several decades, trains were operated by a government-owned entity called Japan National Railways. In 1987, it was privatized and split into the regional JR companies that we know today, including JR East and JR West.

Back then, a lot of train stations in the Tokyo area didn’t even have automated gates. They were manned by staff who physically punched holes in passengers’ paper tickets with a hand clipper. As you can imagine, this wasn’t the most efficient setup and caused massive bottlenecks during rush hour.

Meanwhile, over in the Kansai region, several private railways (such as Hankyu) were using magnetic ticket gates—the same kind still used today, where you feed a ticket into one end of the machine and pick it up after it pops out the other side.

JR East, now in charge of train stations in the booming Tokyo metropolitan area, was thinking hard about what it could do to modernize. Two of their biggest goals were to:

• Relieve congestion at stations to make train travel faster and more convenient.
• Cut maintenance costs. The existing ticket machines worked well, but had tons of moving parts and sometimes suffered from jams and breakdowns.

So the company started researching a long-term solution. Several companies approached JR East with ideas, and one of them proposed something quite ambitious: using contactless integrated circuit (IC) technology.

That company was none other than Sony.

Initial resistance

Sony had actually started researching and developing IC cards in 1988, but not specifically for trains. Their main use case was for logistics. The idea was to attach wireless tags to boxes and parcels so that a reader could identify them from a distance, without needing to unpack or scan anything by hand. The technology behind this was FeliCa, Sony’s proprietary contactless system.

You may have heard of RFID, a more broad term for wireless technology that uses electromagnetic radio waves to identify or track objects. FeliCa is essentially a specialized branch within the RFID family.

Sony built FeliCa to process data quickly and securely over short distances. Seeing potential in their product, they pitched the idea of contactless transit cards to JR East.

JR East said no.

They liked the concept of contactless cards, but the actual technology at the time simply wasn’t advanced enough to meet the speed and reliability demands of Tokyo’s massive train network.

JR East also was hesitant to invest billions of yen into unproven IC technology when Kansai’s magnetic ticketing system was already proven to work. And that’s exactly what they ended up using instead: in the early 1990s, JR East installed the same magnetic gates in Tokyo, finally replacing the human ticket-punchers.

Meanwhile, Sony nearly gave up on FeliCa. They tried finding other uses like access-control systems for buildings, but didn’t have any luck. After a period of fruitless experimentation, an unexpected opportunity came along . . . from outside of Japan.

Success in Hong Kong

Over in Hong Kong, a conglomerate called Creative Star (known today as Octopus) was looking to adopt a fast, unified transit card that could work across multiple types of transit, including trains, metros, buses, and ferries. They were exploring contactless technology, and that’s exactly where Sony found an opening.

Sony set an ambitious goal of building a card that could be read from 10 cm away, with a 100 millisecond response time. With a dedicated team of engineers, they pulled it off and won the bid.

The Octopus card launched in 1997 and was a massive success. Three million cards were issued within the first three months, and the technology proved to be reliable enough to handle millions of passengers every day.

While all of this was going on, JR East had been running its own internal trials, and was gradually starting to warm up to Sony’s now-proven technology.

Back to Japan: Upgrading the card

Eventually, Sony and JR East officially partnered up. But even after Octopus’s success, there was still lots of work to do.

Sony needed to upgrade the technology to meet JR East’s requirements and adapt it to the realities of Tokyo’s rail network. As you can imagine, Tokyo’s sheer scale and density entailed quite a lot of technical challenges.

The biggest challenge was speed. JR East’s gates handled absolutely ridiculous passenger volumes during rush hour. To avoid dangerous bottlenecks, the company set a non-negotiable rule: the entire transaction had to happen in under 200 milliseconds.

At first glance, that might sound less impressive than Octopus, which used a chip that could respond in under 100 milliseconds. But the two numbers are actually different benchmarks.

Octopus’s 100 millisecond target referred only to the radio communication between the card and the reader. It didn’t include the subsequent fare calculation, as that depended on the rest of the transit system. For the record, Octopus’s full end-to-end transaction reportedly takes around 300 ms.

On the other hand, JR East’s 200 millisecond window had to cover everything, including the card-to-reader communication and the fare calculation. So Suica’s target was actually tighter.

Hitting that target was no small feat. It involved detecting the card, authenticating it, calculating the fare based on entry and exit data, and then deducting the balance—all made much harder due to Tokyo’s sprawling web of JR lines, subways, and private railways all tangled together.

To hit the 200 millisecond target, the card and reader had to handle the entire transaction locally, rather than trying to reach a backend server.

Think about the two different approaches. If every single tap required Gate → Network → Database → Approval → Return, the results would have been disastrous at Tokyo’s scale, due to the extra time required and the potential for network errors. So instead, the card was built to simply do this: Gate ↔︎ Card. Done. Server sync later.

This was especially tricky to implement because the card had no internal battery, and was powered entirely by the electromagnetic energy from the gate. So everything had to happen during the tiny window when the card is tapped on the reader and receives power for a split second.

Sony also gave the reader itself a communication range of 85 millimeters, which was double that of rival contactless systems at the time. This might sound like a minor tweak, but it actually mattered a lot. A longer range meant that the transaction could start earlier and finish faster, letting passengers walk straight through the gate without having to stop and carefully line up their card perfectly.

Critical performance issues

After all that hard work, the project encountered a dire situation. When they started testing prototypes of the card-reading touch panels, the performance was abysmal, with an error rate significantly higher than the original magnetic tickets.

Shinji Yamanaka, an industrial designer who worked on the panels, mentioned retrospectively in his blog how severe the problem was. He wrote that “Nearly half of the testers couldn’t get through the gates,” and quoted an executive who complained, “It only worked for me once every five tries. A 20% batting average.”

The failure rate was so high that leadership was ready to pull the plug on the whole project. Yamanaka and his team were brought in as part of a last-ditch effort to save it—and they delivered. It turned out that the problem wasn’t really with the technology itself, but rather with how people were using it.

The original readers were completely flat and didn’t clearly signal to passengers what they were supposed to do. Many users treated them like a barcode scanner. They would swipe the card too fast, or hover it too high above the pad, causing the reader to throw an error.

One idea was to add an explicit “tap here” message on the panels, but this alone didn’t actually solve the problem. After developing four new prototypes and having users test them at a public exhibition, Yamanaka and his team analyzed the footage and found the brilliantly simple solution: to tilt the reader at a slight angle.

They found that tilting the reader exactly 13.5 degrees made it match the natural ergonomic motion of a hand moving forward. This caused users to instinctively rest their hand on the reader for a split second, which kept the card in the reader’s field just long enough to finish the transaction. It also made it easier for approaching passengers to see the reader from a distance, much more so than the original flat design.

With the redesign, the error rate plummeted to under 1%, and the project was saved. That design was later patented and is still the standard used for IC card readers today.

Naming the card

JR East held a public competition to decide on the new card’s name and branding. The winner was none other than Suica, the name that we all know and love today.

Most users get that it’s a cute reference to suika (スイカ, “watermelon”), which inspired the green color scheme. But it actually also stands for “Super Urban Intelligent CArd”, on top of being a reference to the word sui sui (すいすい) which means “smooth” or “effortless.”

The iconic penguin mascot was an existing character created by picture book illustrator Chiharu Sakazaki, and it was adopted to help make the new and unfamiliar technology more friendly and approachable.

JR East backed it all with a series of quirky ads and commercials.

Launch day

Project leader Akio Shiibashi originally set a symbolic launch target: January 1, 2001, the first day of the new millennium.

But as it often happens with big technical projects, preparations weren’t finished on time, so the launch shifted to November 18, 2001. When the day finally arrived, 3,200 gates across 424 different stations in the Tokyo metro area went live, all at once.

Suica was a massive, smashing success. Within just 19 days of launch, one million cards had been issued. Two months later, that number doubled to two million. And by October 2002, it had climbed to five million.

One small mystery with Suica’s launch is that JR East manufactured a huge number of physical cards ahead of time and stockpiled them in warehouses, but did not pre-issue them before the launch. There’s no official statement about why this happened, but there are some plausible explanations given how the cards work.

Each physical card has its own ID stored on the card itself, and each card needs to be activated before it can be used. When you buy a physical Suica card at a ticket machine, activation happens during the purchase. But that activation requires the actual network to be live. Although JR East could manufacture and store the cards ahead of time, they couldn’t pre-activate them because the system wasn’t running yet.

It’s also worth noting that while they did do field testing via employee trials and controlled passenger trials, there was never a public soft launch. The whole system essentially went live in one big move.

What happened next

In the beginning, Suica was solely a transit card, usable only at stations operated by JR East. But it didn’t take long to turn into the multifunctional Suica that we know today.

In 2002, Suica became usable on the Tokyo Monorail and Rinkai lines. In 2004, it received e-money functionality, transforming it into a flexible prepaid card that could be used at shops, restaurants, and vending machines. Naturally, this came with some more lovely commercials.

In addition to being one of the first successful cashless payment methods for consumers, it became a major source of revenue for JR East. When you pay with Suica, the merchant pays a transaction fee of between 2 to 3.2%, similar to a credit card.

A few years later, in 2006, Mobile Suica was launched. This was ahead of its time, letting passengers load Suica onto their phones even before the smartphone era. It worked thanks to a system called osaifu-keitai (おサイフケータイ, wallet phones), which was supported on many flip phones back in the day, and was also powered by the same FeliCa chips.

Not stopping there, JR East developed its own line of credit cards called View. These can be linked to a Suica card to enable automatically charging it when its balance drops below a certain amount. This was also revolutionary for the time, as it completely removed the need for cash, ticket machines, and unneeded mental bandwidth.

From there, Suica’s underlying technology was adopted by the rest of the country, leading to the launch of new IC cards like PASMO and ICOCA.

Initially, each card was locked to its own network:

• Suica worked only on JR East
• PASMO worked only on Tokyo’s subways and certain private railways and buses
• ICOCA worked only on JR West

But eventually, nationwide mutual usage was implemented. Now, any card can be used on almost any train or bus across Japan, with only a handful of exceptions. Nearly everyone using public transportation in Japan is now using IC cards or their smartphone equivalents, and we have the hardworking engineers from the 1990s to thank for it.

Fun fact: Akio Shiibashi, the JR East engineer who led Suica’s development, went on decades later to become the head of the agency issuing Japan’s digital My Number Cards.

Today, all modern iPhones around the world support FeliCa out of the box, letting travelers add Suica cards to their Apple Wallets. Android is a bit trickier since FeliCa is only supported on phones sold in Japan, or on select models. For better or worse, this means that many international travelers still have a reason to get the physical card.

Speaking of which, there are signs that the industry is moving away from dedicated IC cards. Several rail operators around the country, including Tokyo Metro, Toei Subway, and Hankyu, have rolled out credit card tap-to-pay terminals. JR East itself hasn’t joined, but is working on its own gate-less walk-through system using the Suica app and location data. Neither of these means that the physical cards will be discontinued, but they do point to a future where the card itself becomes just one of several options.

Final thoughts

Suica’s launch is a story that spanned well over a decade, involving rejected pitches, an unexpected detour in Hong Kong, and many other trials and tribulations.

Perhaps the most impressive part of the story is that it wasn’t just about that card. The entire system, including JR’s network, infrastructure, and hardware, was all developed and rolled out in one go across one of the world’s busiest rail networks. The 200 millisecond response time is still incredible by today’s standards, and Suica remains one of the fastest transit cards in the world more than 20 years later.

On a sadder note, the beloved Suica penguin will soon be retiring. After 25 years, the mascot will be phased out in spring 2027, and the successor will be chosen by public vote.

So the next time you tap your Suica card at a train station, take a moment to think about all the hard work that went into it, and also be sure to thank the penguin for its many years of service.

On August 26, 2026, the United States government classified the Italian collective Autistici/Inventati as a “Specially Designated Global Terrorist,” citing the collective’s “far-left” politics to justify the designation. They announced similar designations of the British group Palestine Action and the transnational Palestinian movement Masar Badil in the same statement.

Founded in 2001, Autistici/Inventati is a volunteer-run provider of privacy-oriented email, websites, mailing lists, blogs, and communications tools for users who prefer not to rely on corporate platforms. According to the US State Department, Autistici/Inventati hosts “roughly 16,000 mailboxes, 1,500 websites, 5,500 mailing lists, and 10,000 ‘blogs’ on its custom-built platform.”

Autistici/Inventati explain their values and goals in their manifesto:

We believe that communication must be free—and for free—and, therefore, universally accessible.

We try to accomplish all this by offering internet services (web sites, e-mail, mailing lists, chats, blogs, newsletters, and more) using our best skills and knowledge to defend both individuals or groups sharing our same aims or ideals. […]

We believe that media and communication should not be the exclusive domain of information professionals. We believe in the value of self-management: this is why we have no sponsors or funding of any kind, apart from voluntary donations from those who believe that our project is important and must survive. None of us earns a cent from this project (in fact, quite the opposite).

Many book fairs, radio programs, and other cultural projects rely on the Autistici/Inventati platform noblogs.org for website hosting. According to one anti-fascist research group, “Some of the most well-known NoBlogs sites belong to researchers who solely report on far-right hate and radicalization, never doing anything aside from showing you what exists.” Such researchers helped to identify the fascists who participanted in the “Unite the Right” rally in 2017, during which one Neo-Nazi murdered Heather Heyer in an actual act of terrorism.

The US State Department is rationalizing this designation by alleging that Autistici/Inventati “provides encrypted tools and services to numerous violent far-left extremist groups,” including Portland-based Rose City Antifa. Their claim about Rose City Antifa seems to be false, however, conflating Rose City Antifa with another project called Rose City Counterinfo.

On the basis of this error, the State Department rushed to proclaim an alleged “Portland Antifa connection to Hamas” and “Iran’s IRGC” [Islamic Revolutionary Guard Corps], with the sole justification being the allegation that an unnamed “extremist media group”—which they do not allege had any relationship to Rose City Antifa—had republished statements from Hamas and the IRGC on a website hosted by noblogs.org. This is representative of the dishonesty and erroneousness of the State Department press release as a whole.

When a military force carries out an attack on a village, the first thing they do is attempt to cut the communication lines that connect it to the outside world. We have to understand the attack on Autistici/Inventati the same way. Now that tech corporations from Meta to the platform formerly known as Twitter have showed that they are willing to ban whoever the US government instructs them to, the US government is going after groups that provide web services on a non-commercial basis.

Because there is no “domestic terrorist organization” designation in the United States, the Trump administration began its criminalization of anti-fascists by designating European anti-fascist groups as “terrorists.” They are targeting an Italian tech project for the same reason: they aim to put the pieces in place to criminalize domestic protesters by associating them with designated “Global Terrorist” groups.

According to the United States Department of the Treasury, starting on September 25, 2026, any transactions with Autistici/Inventati will be “prohibited by the Global Terrorism Sanctions Regulations.”1 The press release from the Treasury stipulates:

Violations of US sanctions may result in the imposition of civil or criminal penalties on US and foreign persons.

Autistici/Inventati have released a short statement about the designation:

We deny all allegations included in the statements, while we strongly affirm our dedication to providing a platform of tools for digital self-defense, addressing the need of free communication for activists and other individuals, groups and associations.

We will not back down, we will keep doing what we have been doing all these years and we will do whatever is in our possibility to counter the false allegations made by a politically desperate administration with the sole intention of swaying people and media attention away from their own violence and warmongering.

Anti-fascism and anti-capitalism are not terrorism. Protesting is not terrorism. And everyone has the right to speak out and to struggle for humanity.

Building on the social media bans of the preceding years, the terrorist designation of Autistici/Inventati is an effort to suppress dissident media and a significant step towards state repression on the basis of ideology alone. This will not stop with anarchist projects, but will eventually extend across the political spectrum to everyone who does not fall in line with the administration, unless the administration is removed from power. The danger to all will increase until that occurs.

Of course, Donald Trump’s successors could keep repressive policies like this in place, much as Joe Biden did. We must make sure that there is enough political pressure on any political formation that could succeed the Trump presidency to compel them to roll back the repressive measures that Trump‘s administration introduced.

---

Further Reading

For a legal analysis of this designation, you could start here.

• The Server Called Paranoia
• US Sanctions Three European Activist Networks, Including Italian Privacy Collective
• Make Ready: Safeguarding Our Movements against Repression

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1. The specific threat from the Treasury Department reads as follows: “As a result of today’s action, all property and interests in property of the designated or blocked person described above that are in the United States or in the possession or control of US persons are blocked and must be reported to OFAC [the Office of Foreign Assets Control]. In addition, any entities that are owned, directly or indirectly, individually or in the aggregate, 50 percent or more by one or more blocked persons are also blocked. Unless authorized by OFAC, or exempt, OFAC’s regulations generally prohibit all transactions by US persons or within (or transiting) the United States that involve any property or interests in property of blocked persons.” ↩

If you've just discovered aphantasia and feel lost in understanding it, we made this Aphantasia Guide just for you.

Oh hello there curious mind! Welcome to the Aphantasia Beginner's Guide. Grasping the concept of a mind without images can be super baffling - but this aphantasia guide is here to illuminate the path for you. Dive on in!

How To Get The Most Out Of The Aphantasia Guide

See the table of contents below for everything in the Aphantasia Beginner's Guide. Here, you'll find assessments and experiments to pinpoint your experience, answers to those burning questions, communities to connect with, and a treasure trove of scientific insights. Each section is a revelation, so embark at your own pace!

So I Just Learned I Have Aphantasia

You know that great musical number "Does anybody else have this?" or the popular internet meme, "Can you picture a red apple in your mind?" Well, I just found out that most of you are seeing a juicy, shiny apple floating in your mind's eye and my mind is blown. I don't see any images in my mind. Yep, I have aphantasia. And if you're wondering, "Aphant-what now?" keep reading.

What Is Aphantasia?

Aphantasia is the inability to visualize. Otherwise known as image-free thinking. In simpler terms, if you ask someone with aphantasia to imagine a beach, we think of the concept of a beach. We know what a beach is and can describe it, but we can't "see" it in our mind's eye. It's not just the visual sense that's impacted. Aphantasia can affect all or some of the other senses in your imagination...more on this later.

Do I Have Aphantasia? The Apple Test

Here's a simple and helpful test to determine if you have aphantasia. Imagine a red apple. Try to visualize it in your mind's eye. What do you see? Some people will conjure up clear images of an apple, some might see blurry ones, and a few can't see anything at all.

If you find yourself in the latter category, understanding that you've always interpreted phrases like "mind's eye" or "counting sheep" as mere metaphors, you have aphantasia.

The term "mind's eye" often gets thrown around, but for many, it's taken quite literally. When someone says they're "counting sheep" to fall asleep or talks about their "imaginary friend" from childhood, they are actually picturing these scenarios. For those with a highly visual imagination, these aren't just metaphors; they're real mental images.

To determine where you are on the visual imagination spectrum, the VVIQ is a recognized test that can help identify where you might fall on the spectrum.

What's It Like To Visualize?

Experiment with this apple illusion to see for yourself. Keep your gaze focused on the center point without moving your eyes for about 30 seconds, then turn your gaze to the blank white space and blink several times. You'll briefly see an afterimage of a red apple.

This afterimage illusion, while a product of perception, serves as a bridge to understanding visualization. For those with aphantasia, it's a rare opportunity to "see" what visualization might be like for some visualizers.

Learn more about the afterimage apple illusion.

What Do Most Visualizers "See"?

People's experience of visual imagination can range from subtle to incredibly realistic. For instance, when asked to imagine a seashell, some might see a simple white or pink curve, while others might visualize a detailed, photorealistic seashell with intricate patterns lying on a sandy beach. This ability to form mental images isn't just limited to objects; it can extend to people, places, events, and even abstract ideas.

The article "Visualizing the Invisible" delves deeper into the nuances of visual imagination. It highlights that the vividness of one's mental imagery isn't just about clarity or detail. Some people might only visualize things they've seen before, while others can conjure up entirely new images. The presence, arbitrariness, or controllability of these images can also vary.

For example, some individuals can easily create, hold, or modify a mental image, while others might find this challenging. Emotions, recent media consumption, and even whether one's eyes are open or closed can influence the type and clarity of our mental images.

How Do We Know People Are Actually Visualizing?

People have taken various physiological and behavioral tests in a research lab. Researchers have observed that people who report vivid imagery respond differently to these more objective measures than aphantasics. Although this doesn't let us know definitively that they vividly see an image, it all points to the fact that something is truly different between people who claim to see vividly and those who don't.

We can also see activation in the visual cortex during fMRI studies, the area in the brain that processes images from the eyes, further suggesting actual visualization. For more on the known neurodifferences in imagery experiences, check out these shocking insights. For a deeper dive into how this brain activation can be used to decode mental imagery, you might find this study enlightening.

How to Tell The Difference Between Visualizers And Conceptualizers

While visualizers "paint" vivid pictures in their minds, those with aphantasia lean more towards conceptual thinking, diving deep into ideas and concepts rather than visual scenes. Discovering the differences between visualizers and conceptualizers can be both enlightening and entertaining. Want a quick and fun way to spot the difference?

Try the Ball on the Table experiment. In under two minutes this playful experiment can shed light on the distinct thinking styles of visualizers and conceptualizers, offering a glimpse into the fascinating differences in our inner worlds. Credit to u/Caaaarrrl for this experiment.

Are You a Visualizer or Conceptualizer?

When you ask a visualizer about the ball on the table, most will immediately have answers to all of the questions. They are likely to provide extra details you didn't ask for. This is the first clue the individual may be picturing the scene in their mind. For example, a visualizer might say, "The ball looks like the Pixar Ball. It's yellow and features a blue strip with a red star. The ball is about the size of a baseball. It's on a wooden, oval-shaped table with scratches on top, etc."

Conceptualizers, on the other hand, approach this task differently. To them, the ball on the table is an idea. While they can anticipate the possible outcome – a ball, when nudged, might roll and likely fall off – many specific details, like the ball's color, its size, the material of the table, or the gender of the person, remain elusive to them. It's possible that they only acknowledge or consider these details when directly questioned about them. If you're more of a conceptualizer you grasp the core idea, but likely do not form a detailed mental picture. This is one of aphantasia's unique strengths, thinking more conceptually.

How Do I Explain Aphantasia To My Family And Friends

Breaking the news about your discovery of aphantasia to friends and family can be a daunting task, especially since it's a topic that's not commonly discussed. It's natural for them to have questions or even doubts (after all, with ~4% of the population having aphantasia, it's more likely that they can visualize).

To help you navigate this conversation, here's a sample dialogue that addresses some typical questions and concerns you might encounter.

Common Questions About Aphantasia

When you first tell someone you have aphantasia, a flurry of questions often follows. "Can you dream?" "How do you remember things?" "Do you recognize faces?" Dive into this section to arm yourself with answers and insights to these common curiosities. It's a journey of understanding, one question at a time.

Do you Dream?

How do you remember things?

Do you recognize faces?

Can you picture your loved one?

Does aphantasia affect daily life?

Can you imagine sounds or other sensory experiences?

Were you born this way?

Is aphantasia hereditary?

Common Misconceptions About Aphantasia

Understanding our cognitive differences is crucial, yet it's all too easy to apply the "aphantasia stamp" to every challenge we face once we uncover this facet of our minds. But resist the temptation! Not every hurdle is tied to our unique way of processing. Let's debunk some common misconceptions together.

Aphantasics have difficulty with navigation.

Aphantasics dislike fiction.

Aphantasia can be cured.

Aphantasics can't be artists.

Aphantasics are unable to meditate.

Aphantasics can't be hypnotized.

I'm Struggling With My Aphantasia Discovery. Where Can I Go?

It's important to note that aphantasia is not a disorder. Yet, for ~35% of those with aphantasia, its discovery can be a significant psychological stressor. If you suspect you have aphantasia, it's always good to read more about it. If you find yourself struggling with your aphantasia discovery, consult a mental healthcare professional to understand it better.

Be aware, however, that many practitioners have yet to learn about aphantasia. Here's a resource on how to talk to a therapist about aphantasia, and a database of professionals we can vouch for.

Online Communities Where Aphants Hang Out

Discovering aphantasia can often feel like you've been handed a puzzle piece you didn't even know was missing. Suddenly, you realize that many people around you have been secretly visualizing their entire lives! Those movie "flashbacks" that seemed so dramatic? They're a real experience for some. And police sketch artists? They aren't just a figment of Hollywood's imagination. It's undeniably a lot to take in.

The initial shock can be overwhelming, and it's completely natural to have a strong reaction. But remember, you're not alone in this journey. Once you've had some time to process this newfound understanding of your mind, connecting with an online community can offer support, insights, and a sense of belonging. We're here to help you navigate this revelation and celebrate the unique way your mind works!

🌐Community-Organized Groups

1. r/Aphantasia on Reddit
2. The Aphantasia Artist (Art Group for Image Blind Creators) on Facebook
3. Aphantasia! on Facebook

🏠Aphantasia Network Community

1. Aphantasia Discussion Forum
2. Aphantasia Network Membership Community
3. Aphantasia Community Meetups

Venture Deeper Down the Rabbit Hole of Aphantasia

Aphantasia is more than just a different way of thinking; it's a journey into understanding the intricacies of the human mind. For those intrigued and wanting to delve deeper into this invisible difference, there's a whole world waiting for you down the rabbit hole of aphantasia. Sign-up for our aphantasia newsletter to get weekly insights into the aphantasic experience.

By now you’ve almost certainly heard the news that Meta has settled with 52 state and local Attorneys General who had sued the company in some form or another over child safety on Meta’s platforms. The headlines are all covering the basics: the years-long case these states filed against Meta ends, and Meta pays somewhere between $12.7 billion and $18 billion, depending on which document you read (the consent judgment itself caps the total at $16,680,647,753.21; Meta’s press release rounds it up to “approximately $18 billion”). Also Meta will implement a bunch of changes to its platforms with the aim of improving child safety on those platforms. It will also “encourage” YouTube and TikTok to enable the same safety features even though (bizarrely), if YouTube and TikTok follow suit, then Meta will have to pay more.

You can read the details of the proposed settlement here.

Notably, the whole point of doing this as a “settlement” is that everyone involved knows full well that no government could mandate these feature changes without violating the First Amendment. But now that it’s in a “settlement” the courts may need to explore if these choices — which Meta could make freely on its own — suddenly have become a “state action,” implicating the First Amendment.

As with the various rulings against Meta over the last few months, people are cheering this on, without realizing the damage it will do. We’ll explore why this is problematic in a moment, but just to highlight that I’m not alone in thinking so, both EFF and Fight for the Future are warning how bad this settlement is. Here’s EFF:

Under this settlement, young users will now have less access to Meta products, and a lesser ability to exercise their rights to speak, access information and art and culture, associate and form communities, and play. The settlement also embeds age assurance into every product, mandating the collection of even more personal information from users of all ages; this enshrines Meta’s harmful surveillance into law, and it will compromise users’ privacy and anonymity while increasing their exposure to data breaches and government data requests. And the data minimization and security measures don’t keep states from using data collected under the agreement for other law enforcement purposes – which could include things like criminal investigations of abortions or gender-affirming care.

And here’s Fight’s emailed statement:

Big Tech does pose harm to our kids through its business practices and exploitation, but pushing for more censorship, age-gating, and surveillance of young people at the hands of the same Big Tech companies that have already harmed young people is not the answer. Online ID checks when implemented put vital information behind age-gates, stamp down teenagers’ right to speak, and expose all of us to even more of our data being collected, hacked, and leaked. Meta knows that managing this amount of personal information and enforcing these agegates will be messy and that’s why they are seeking to offload the burden to anyone but themselves, while being seen to comply by the public and lawmakers. Instead of actually damaging their exploitative business model, this result allows Meta to bring everyone else down with them, from app stores to other social media companies. We feared that these lawsuits would manufacture consent for invasive age verification and content controls and our fears have been proven correct. We will continue to oppose online ID checks everywhere and be on the watch for more censorship creeping into Meta’s platform.

We’ll get into the specifics of why this settlement is so bad, but first some important background. For a few decades now, when basically all Attorneys General would get together to threaten and/or sue tech companies, it was almost always over bullshit headline grabbing claims where the AGs either had no jurisdiction or ability to legally do anything. Sixteen years ago, we wrote one story about an account written by a CEO of a company who faced down dozens of state AGs who were way more concerned about the headlines they generated than actually making platforms safe.

It was similar to other stories that we’d heard, where no matter what companies did to explain to the AGs what steps they were taking to keep a platform safe the AGs would simply turn around and misrepresent what they were told, out of context, to make the platforms look worse and worse until they agreed to some sort of settlement. It happened with Craigslist. It happened with ISPs being forced to kick their users off at the behest of the recording industry. Even John Oliver has covered how grandstanding state Attorneys General will target just about anyone they want to shake down in some form or another.

That’s not to say that there aren’t righteous cases brought by Attorneys General, but there are so many examples of them being much more about getting headlines than actually making people safer. And the simple fact is that these efforts are so resource intensive, so expensive, and so draining that it’s no surprise that most companies end up “settling” by agreeing to do things that the government simply cannot force a company to do. But because it’s a “settlement” people act like it’s not the government doing it.

In this case, given some of the recent court decisions, it’s no surprise that Meta would strike some sort of settlement. As these cases continued, the headlines would only get worse for the company. And Meta deserves some bad headlines, but as I’ve discussed, many of the bad headlines in these cases involved lawyers and the media taking things way out of context. The classic case with Meta is that many of its efforts to study how to make its platforms safer were used against the company as proof that “they knew!” their platforms were unsafe!

The lesson for the rest of the tech industry is grim and unambiguous: never study whether your own platform is causing harm. The mere existence of the research will be turned into Exhibit A that “they knew,” both in the court of public opinion and in actual courts.

The other bit of background worth understanding here is that Meta has been desperately seeking a path to regulatory capture for quite some time now. It’s been practically begging for Congress to pass child safety legislation that only the largest companies (like itself) could comply with. Indeed, Meta has done this before. It went against the rest of the internet industry in embracing FOSTA, again to try to create a regulatory moat. So this shouldn’t be surprising.

Meta’s failed forays into the “metaverse” and AI have shown that it’s been pretty consistently losing the innovation race, and the government granting it a regulatory moat that smaller competitors can’t cross would be a godsend.

And it’s even better when it can be done in a way that looks like Meta “losing” a lawsuit.

So that’s what Meta gets here. They “settle” the lawsuit so the AGs and Meta haters can all claim that they’ve “protected the children.” Meta pays out over a decade — enough that it’s taking a $10 billion legal charge in Q3, which stings for a bit but will mostly be forgotten by next year. Meta can easily eat the cost. And then Meta agrees to implement a bunch of kid safety features, most of which we have no idea whether they actually protect any kids. Notably, a legislature could not have mandated most of these features without running straight into the First Amendment — but coming out of a settlement, they carry the imprimatur of law anyway (more on that in a moment), and the structure of the agreement makes it so that Meta has to actively encourage Google and TikTok to take identical steps, thereby setting in concrete what steps any platform will have to take to be considered following “best practices” and therefore acceptable to most of the country’s Attorneys General.

The specific features don’t even matter that much, but for the record:

• Time Limit: A default two-hour daily time limit that teens can only turn off with a parent’s permission. This limit is cumulative across Facebook and Instagram, and time spent scrolling on both apps counts toward the total, including if we detect that someone has multiple accounts.
• Night Mode: A default block from our apps between midnight and 6am. This means teens will not be able to post or view their Feed, Stories, Explore, or Reels, for example.
• School Mode: Notifications will be muted by default between 8 AM and 3 PM. During those hours, teens will no longer receive push notifications, except for direct messages and alerts about their account security or safety.
• Regular Prompts: Teens will receive prompts after every 15 minutes of continuous screen time on Facebook or Instagram. They’ll also receive prompts when their total daily usage hits 60 minutes and 90 minutes. These prompts are designed to encourage intentional use.
• Algorithmic Feed Control: Teens will be able to choose a non-algorithmic feed — one that isn’t personalized by our recommendation systems — as their default. We will periodically remind them of this option, and parents can choose to adjust their teen’s default experience to require this setting.
• Autoplay Control: Teens will be able to turn off autoplay, so that content no longer automatically plays. Instead, they’ll need to take a deliberate action, like a tap or swipe, to see more. Parents can choose to adjust their teen’s default experience to require this setting.
• Hidden Likes: Teens won’t see the number of likes and reactions on posts — both their own and those from others — by default.
• Disabling cosmetic surgery and extreme makeup filters: In addition to our existing policy to block teens from using cosmetic surgery filters, we’ll now block teens from using extreme makeup filters.
• Age Assurance: We work hard to find and remove underage accounts from our apps and, as part of our agreement, we’re investing in even stronger technology to proactively catch accounts that may belong to under-13s. We’re also strengthening the technology we use to identify accounts that may be between the ages of 13 and 17, so we can ensure those accounts are placed in experiences designed for teens, even if they give us an adult birthday. However, to ensure teens are consistently protected across the many apps they use, app stores must provide developers with verified age information. This will allow platforms to put age-appropriate protections in place for as many teens as possible. That’s why we’ll continue to advocate for legislation that empowers parents by requiring app stores to verify age and obtain parental approval before a teen downloads an app.
• Age-appropriate content restrictions: We will maintain our current content standards so that, by default, teens are placed into 13+ content settings, inspired by movie ratings criteria and parent feedback. We will also continue to prevent teens from following or interacting with accounts we consider age-inappropriate. We will work to continually improve these systems to ensure age-appropriate content experiences for teens.
• Unwanted contact from strangers: We will maintain our current practices of defaulting teens into private accounts on Instagram and private default settings on Facebook, and we’ll continue to restrict potentially suspicious adults from contacting them. We will also strengthen our efforts to make it harder for those adults to find, follow, or interact with teens.
• Reporting and ongoing protection from harmful content: We will continue to give teens easy ways to report content that concerns them, and we’ll work to improve our response times. We will also continue our work to protect teens from potentially harmful experiences by regularly evaluating how often teens are exposed to them. We’ll draw on research and expert input to improve our work.
• Strengthening our parental controls: We will encourage parents to set up our supervision tools and give them new controls and insights. This includes notifying parents when a teen links a secondary account, alerting them to interactions with potentially suspicious accounts, and providing periodic updates on their teen’s usage and any changes their teen attempts to make to their protective settings.

Some of those might be good features. Some of them might not be. Some of them might be good for some kids, but very bad for other kids.

Part of the problem is we really don’t know.

There is something of an accountability structure here too. Meta and the states will appoint an “independent” auditor for five years, and the age assurance system gets tested annually to meet certain thresholds. But it’s important to look at what’s actually being audited here. It’s whether or not Meta is implementing the things it’s promised to do, not whether any of those things actually work.

But now these are, effectively, mandated by law. Even though if Congress or the states had passed a law requiring these, it would almost certainly be thrown out as unconstitutional under the First Amendment.

The weirdest part of the agreement is that Meta has to try to convince Google (YouTube) and TikTok to implement some (but not all?) of these same features. Indeed, Meta has already put up a settlement-mandated open letter to those two companies asking them to implement those features.

What’s so weird is that if YouTube and TikTok agree to do this and to voluntarily throw billions of dollars at the states, then Meta also needs to pay more. The breakdown of the money Meta owes is partially dependent on them arm-twisting those two companies to do the same things:

The agreement includes a payment of approximately $18 billion, which can be used to fund youth online safety initiatives, among other state priorities. The payment will be distributed in annual installments over a 10-year period. Participating states will receive approximately 70% (approximately $12.7 billion) of the allocated payment over the decade. The remaining 30% (approximately $5.3 billion) will be released only after two specific conditions are met.

1. YouTube and TikTok implement a one-hour Daily Limit, Night Mode, and age assurance measures.
2. YouTube and TikTok each pay an amount matching the 30% figure, with half of the remaining funds tied to YouTube’s payment and half tied to TikTok’s.

You can argue that Meta might not actually want YouTube and TikTok to do this, so they won’t have to pay that extra $5.3 billion, but from a competitive standpoint, you have to think that Meta absolutely needs to have YouTube and TikTok implement these features or its already somewhat dwindling market share will dwindle faster.

It’s quite possible that YouTube and TikTok will go along with this, rather than get bogged down in a similarly costly legal fight. But, again, that would create many problems. First, we still don’t know if those feature changes are actually helpful or effective. But now they’re effectively government mandated.

In theory, this could open up room for other platforms to come in and sweep up the youth market by not implementing these same features. But the nature of this agreement is that if the state AGs suddenly feel like any platform is becoming too popular with the kids, it can point to this agreement and call it “industry standard” or “industry best practices” to insinuate that other companies not doing the same are deliberately choosing to keep kids unsafe.

Indeed, within the agreement there’s a bit of weirdness, in which Meta has to push for “industry wide adoption” which is currently defined as YouTube and TikTok, but which the agreement makes clear could include any new social media platform if such a new platform meets the thresholds. In other words, Meta is basically being forced into guaranteeing this settlement creates an industry-wide standard.

And that’s a real problem when we still don’t know how to actually help keep kids safer online. So if a web service comes up with a unique or innovative or different idea that works differently than what Meta has agreed to do, then that may be too risky to even try. Better to just follow what the AGs have “blessed” in this settlement.

As noted, we already know that some of these things are directly harmful. Age assurance is a privacy nightmare. Enshrining it as the industry standard means the end of meaningful online anonymity, and it “forces” Meta to collect more data about all of us — including adults — while handing the states a pipeline to that data for whatever else they decide it’s useful for.

That’s bad.

Also, there are some oddly specific requirements:

Meta SMPs will disable Teen Users from applying Cosmetic Procedure Filters to their content.

The agreement clarifies that this means:

… any digital filter or augmented reality (AR) effect that distorts, sculpts, redefines, or idealizes a user’s face in a way that cannot be achieved without cosmetic surgery or extreme makeup techniques.

And, sure, I can understand why such content might be unhealthy for teens. But it is, in fact, Constitutionally-protected speech. Meta could decide internally to block that speech specifically on its own platform (that’s its own editorial right). But now that it’s being done at the behest of government pressure, it almost certainly violates the First Amendment.

Also, somewhat oddly, some of the rules appear to only apply to content in English or Spanish:

With respect to Potentially Harmful Reported Content submitted in English or Spanish, Meta SMPs shall maintain processes designed to permit Teen Users to receive a response indicating Meta’s decision on the report within 6 hours in at least 90% of cases.

The implication is obvious: those are the languages most reports come in, and Meta is expected to staff up enough to clear them fast. But it also means the government has just negotiated a moderation service level that varies by the language you happen to speak — English and Spanish speakers get a six-hour guarantee, Tagalog and Mandarin speakers get whatever Meta feels like. That’s a strange thing for a state to be dictating at all.

This is also a perfect example of the kind of standard that only a giant can meet. A six-hour turnaround on 90% of reports is achievable when you have thousands of trust & safety staff and a decade of tooling. For a startup with four employees and a Discord server, it’s a fantasy — and now it’s the benchmark against which every AG will measure them.

So what happens now? The judge will need to review the settlement, but I’m actually wondering if some teenage users would have standing to challenge this. Meta is clearly restricting First Amendment protected speech under this agreement. It is free to do so on its own if it chooses to do so, but this is different. Here it’s doing so because it’s being forced to by various state AGs, making it a state action.

Under the Supreme Court’s recent (unanimous) Vullo decision, that seems pretty clearly unconstitutional. In that case,the justices said, quite clearly:

[A] government official cannot do indirectly what she is barred from doing directly: A government official cannot coerce a private party to punish or suppress disfavored speech on her behalf.

That seems like it should be the whole ballgame, because that’s what’s happening here.

One other point on all of this. Here’s the list of 52 Attorneys General that have agreed to this settlement:

Alabama, Alaska, American Samoa, Arizona, Arkansas, California, Colorado, Connecticut, Delaware, District of Columbia, Georgia, Hawaii, Idaho, Illinois, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maine, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri, Montana, N. Mariana Islands, Nebraska, Nevada, New Hampshire, New Jersey, New York, North Carolina, North Dakota, Ohio, Oklahoma, Oregon, Pennsylvania, Puerto Rico, Rhode Island, South Carolina, South Dakota, Tennessee, Texas, Utah, Vermont, Virginia, Washington, West Virginia, Wisconsin, and Wyoming.

Notice anyone missing? Yup. There’s no New Mexico. Remember, New Mexico won its initial case against Meta recently, enabling the judge to force a different set of feature changes on the company. So… now Meta may have certain features for New Mexico, and different features for everywhere else?

None of this is to say that Meta shouldn’t do a better job trying to protect kids on its platform. Obviously, it can certainly do more. But this settlement seems much more like Meta using this case as a way to force the industry into a set of required steps (which might not help much, and may do real harm in some cases), making it difficult for smaller competitors to enter the market, and giving them a bit of regulatory capture through mass lawsuit settlement.

As law professor Jess Miers wrote, this settlement is about Meta selling out the entire social media industry, forcing them to embrace impractical and unhelpful features that serve only to lock in giants and lock out upstarts:

Meta sold out the entire social media industry today by signing all UGC services up for standards that are neither practical nor something the government actually has the right to mandate. This is precisely why I am not confident in them defending 230 at SCOTUS either in the Nevada case.

— Jess Miers 🦝 (@jmiers230.bsky.social) 2026-08-26T20:55:11.766Z

But for all the people “celebrating” this as a win “against” Meta, you’ve been fooled. Meta just cut a deal to put itself in charge of how social media works going forward. As Justin Maurer wrote on Bluesky, this is Meta taking a “please regulate me Daddy” approach to the government, and getting exactly what it really has been asking for.

We still don’t have any actual evidence that this will help anyone, let alone every kid. The state AGs didn’t have to prove how this would help kids. Meta didn’t have to prove it. The judge won’t be asked to. It’s just taken on faith. Meta offered this up, the AGs okayed it… and it all becomes a grand experiment on kids.

You can argue that these feature changes sound like they should help kids. Limiting access to two hours a day (unless parents grant more, which many will), lights out at midnight, disappearing like counts — these all sound like they’ll help some kids. But if it turns out that locking kids out of these systems actually pushes the most vulnerable ones to darker places with no trust & safety team at all, you won’t hear about that from Meta or the AGs.

We just spent three years teaching the entire industry that if you do research on child safety, you’ll have it held against you. Do we really think that all of this is going to actually enable anyone to figure out what works to help actual kids?

Meta bought itself a moat. The AGs bought themselves headlines that will be useful next election season. And every teenager in the country was just automatically enrolled in an untested experiment. There’s a five year independent auditor requirement to confirm that Meta follows the rules. But not to see if the rules work.

MIT’s Ad Hoc Committee on
AI Use in Teaching, Learning, and Research Training

1. Introduction

This report is a call to action.

Through five intense months of meetings, research, and outreach across the MIT community, the Ad Hoc Committee on AI Use in Teaching, Learning, and Research Training sought to understand the role of generative AI in the life and educational mission of the Institute and recommend how to navigate its challenges and opportunities.

In January 2026, Chancellor Melissa Nobles, Provost Anantha Chandrakasan, and Faculty Chair Roger Levy charged us1 specifically to:

• Assess current AI use at MIT.
• Identify innovations in teaching and student assessment.
• Propose an AI use policy.

However, what we learned as a group quickly convinced us that the Institute community, particularly the faculty, must tackle a set of deeper questions about the structure, meaning, and value of an MIT education in an era in which AI is one of several factors complicating the Institute’s mission.

Our committee consisted of undergraduate and graduate students, faculty from every school, and staff from relevant units, including the MIT Libraries and the Teaching and Learning Lab. Though we brought to the assignment a broad range of experience and no fixed thesis, our brief but intense explorations led us to a strong shared view.

In this report, we:

• Highlight key aspects of the current educational landscape at MIT.
• Share eight principles we relied on and that we hope will guide the Institute in the work ahead.
• Recommend immediate and long-term actions for both instructors and the administration.

As an institution deeply identified with the birth of AI and known for its distinctively rigorous, hands-on education, designed to produce graduates unafraid of the world’s hardest problems, MIT has a unique role to play in this moment. We believe it also has a responsibility to lead.

We hope our report can help the Institute lean into the spirit of Mind, Hand, and Heart as it continues to define and foster the highest-quality residential education – of humans, by humans, in support of human flourishing, and for the betterment of humankind.

Eric Klopfer, co-chair Sam Madden, co-chair

On behalf of the Committee on AI Use in Teaching, Learning, and Research Training

1.1 The Landscape

This report’s recommendations reflect the following insights about the educational landscape at MIT.

Generative AI is everywhere already, spurring an assortment of views:

• MIT students use AI frequently and pervasively – with strongly mixed feelings, from curiosity, creative inspiration, and gratitude to resignation, concern, and anxiety.
• Instructors’ attitudes range from enthusiastic exploration and growing reliance on AI to skepticism, suspicion, and “AI refusal”– and there’s a widespread desire to share experiences, ideas and techniques.

While AI is allowing instructors to develop exciting new learning experiences and enabling students to learn and experiment in innovative ways, there have been a number of concerning effects on the life of the campus, including signs that AI is:

• Upending foundational elements of the MIT educational experience, especially for undergraduates, from the p-set, the take-home exam and UROPs to office hours and the study group.
• Increasing isolation.
• Undermining student mastery and confidence.
• Eroding the “social contract” between instructors and students.
• Making it much harder to assess student progress.
• Challenging decades of distinctive MIT community norms and values about rigor, the creative friction required for learning, collaborative problem solving, and personal integrity.

AI is generating both immediate rapid changes and long-term tectonic disruptions – and MIT needs to respond:

• Students are confused and concerned about a lack of clarity, consistency and justification about the use of AI, within a given subject and across the curriculum.
• Every subject taught at MIT will likely need to be reexamined and potentially revamped to make sure that how students are being taught, what they’re learning, and how they’re assessed are “AI-aware.”
• AI is changing what students need to know and know how to do.
• Beyond its implications for specific subjects and disciplines, the advent of AI demands a broader, holistic reassessment of the nature, scope and purpose of higher education today.

Finally, AI presents substantial practical concerns for our community, from data privacy and confidentiality to questions around disparate access, bias, fairness, and accountability. AI’s explosive growth and ascendance also raise important questions for society, from the environmental impact of AI data centers, to the use of intellectual property and training data, to the overall human impact of the technology and the industry – and MIT needs to engage with those questions too.

Other educational institutions are grappling with similar questions around AI and generating interesting ideas – but no one seems to have it all figured out.

Three notes on the words we use:

• N.B. In this report, “Instructors” includes faculty and everyone else engaged in teaching at MIT.
• Most references to “students” apply to both undergraduate and graduate students, except in a few obvious places, as when we refer to the General Institute Requirements (GIRs) or to participants in the Undergraduate Research Opportunities Program (UROP).

“We,” “us,” and “our” sometimes refer to the members of the committee, and sometimes the whole of MIT. The difference should be clear from the context.

2. Guiding Principles

We start by defining eight principles we relied on and that we hope will guide the Institute in the work ahead.

2.1. Be humble

Some technological innovations emerge gradually: As society and technology evolve in concert, mutual adaptation softens the impact. The computer – AI’s precursor and key enabler – fits this pattern. Other innovations land more abruptly, becoming socially consequential before individuals and institutions have time to adapt.

Society tends to peg the “birth” of a new technology as the point when it becomes readily usable. By that measure, generative artificial intelligence was “born” with the release of ChatGPT in late 2022. Public engagement with generative AI is therefore less than four years old. In that time, it has amassed more than a billion users, and the companies selling AI technology have come to dominate the headlines, the stock market, and public consciousness.

In other words, AI is progressing across almost every domain and on a timescale too compressed for society to properly observe and analyze its impacts and then gradually adapt.

This suggests our first guiding principle: We offer our proposals in a spirit of humility. Course corrections – perhaps even major ones – will be inevitable as the technology continues its relentless evolution and the Institute experiments and learns.

2.2. Be bold

Yet uncertainty can’t be an excuse for inaction. This is not a moment for patches and duct tape. The challenges AI presents in teaching and learning call for a bold strategic response – everywhere, and especially at MIT. With our Social and Ethical Responsibilities of Computing program2 completing its seventh year, we are uniquely positioned to find ways to employ this new technology for the benefit of society, and for our students in particular.

AI also presents extraordinary opportunities, from unprecedented possibilities for individualized tutoring and coaching to a dramatic acceleration and revamping of research in many disciplines. Seizing these opportunities deserves and demands boldness too.

Bold thinking is especially important because our students will go on to help shape the intellectual, ethical, and technical direction of our society – and soon. We owe them a deep engagement in rich and constructive uses of AI, and a sophisticated understanding of its potential and its drawbacks. Their MIT experience should prepare them with the wisdom to help determine how and where AI is used for the betterment of society and the world at large.

2.3. Put humanity front and center

Facing a technology that already has such immense capabilities – built on, modeled after, and in many ways now exceeding human powers – the Institute’s fundamental challenge and most important goal must be to nurture and protect our shared humanity, and to value the MIT community, its members, and their flourishing above all.

In a listening session with instructors, we learned that some were considering using AI agents as research assistants instead of hiring undergraduates as UROPs. One can see the case for speed and efficient use of resources (especially now, when research resources are so constrained). But if those criteria come to dominate our decisions, we all have to ask, “What is it that we are here together to do?”

As a community, we need to keep in mind that although research is central to MIT’s mission, it’s more than an end in itself; on our campus, research is also an apprenticeship, a means of training the next generation of researchers to continue our work and drive our fields of inquiry forward. Using research as an opportunity for learning-by-doing may produce seeming “inefficiencies,” but that’s a feature, not a bug.

A focus on our humanity should also alert us to the fact that common current remedies for the problems AI creates can risk damaging the relationships between and among teachers and students. For instance, many instructors recounted that having to “police” unauthorized AI use was harming their connection to students (a dynamic made worse by the fact that, as we learned, AI detection software is quite unreliable). For their part, students fear being wrongly accused of AI cheating and are increasingly frustrated by instructors’ use of AI in areas that demand a human touch, such as grading, assignment creating, and feedback. Such an underground river of mutual suspicion is no foundation for a healthy classroom.

2.4. Lean into learning

Many problems and assignments used in MIT classes to reinforce learning and assess student progress can already be accomplished by AI. Unrestricted AI use by students can make some traditional assessments less reliable as indicators of individual learning, potentially weakening confidence in grades and credentials. For educators, this radical shift in norms and expectations can feel profoundly disorienting.

But as a community, what should worry us most is that many uses of AI deprive students of the opportunity to learn.

The threat AI poses to familiar ways of teaching and testing may be a blessing in disguise – because the changes are too sudden and severe to ignore. As the faculty on our committee can attest, for at least two decades, educators have lamented the strain on teaching and learning from fragmented attention, ubiquitous devices, a narrow preoccupation with grades rather than learning, and a weakening sense that students and teachers are joined in a common intellectual project.

As an institution that prides itself on the power of its distinctive educational recipe, it is up to MIT to seize the opportunity of this moment: to make sure that the undeniable changes imposed by AI become a tipping point, forcing us to deal decisively with the forces eroding our shared educational mission.

Leaning into learning means creating a new “social contract” between teachers and students. All of us who teach at MIT will need to be prepared to help students understand both that the process of education is necessarily a productive struggle, and that the most important product of their education is not a GPA or a diploma but themselves: their personal growth and intellectual maturity and the development of their own imagination, insight, and judgment.

Instilling these attitudes needs to become a central task for every educator, so that our students know not only what they should learn, but also how they should learn and why learning matters. We need to help them develop metacognitive abilities to think about thinking, to engage in reflective practices, and to enhance their sense of personal agency. This will require both dedication and fresh preparation on the part of instructors.

2.5. Teach with intentionality

Most classes taught at MIT will require careful review and, in many cases, substantial adaptation to survive an encounter with AI without serious disruption. AI will undermine the pedagogical assumptions that guided the choice of content, the methods of instruction, and the ways of assessing student performance.

In short, it’s time to reflect deeply and extensively on what we teach and how we teach it. Future class structures, assessments, and policies must be built with high intention and a clear sense of purpose, not merely tweaked in reaction to the immediate realities of AI. This does not mean, however, that every course must change in the same way.

The time-tested technique known as “backward design3” offers a useful framework here. For instance, rather than beginning by asking whether AI should be allowed or prohibited in a specific subject, educators would begin by defining the purpose of the learning experience itself: what students should come to know, be able to do, and learn to value. Assignments and assessments can then be designed to measure and encourage those outcomes, using AI if it’s helpful but not if it isn’t.

The instructor time and effort required to redesign, reassess, or intentionally preserve existing structures is likely to be significant. But that process of pedagogical reflection and reasoning is valuable in itself. Intentional teaching promotes intentional learning; when instructors make clear why AI is permitted, limited, or required, students are more likely to understand the learning that’s being protected or developed.

In this way, backward design can help MIT preserve the purpose of its education while adapting honestly to the realities of AI. (Encouragingly, MIT’s Teaching and Learning Lab is well-equipped to help instructors master this tool.)

2.6. No one size fits all

No single approach to AI in education will serve MIT well. A poetry seminar, a mathematical proof course, and an architectural design lab logically entail different relationships to AI. In some, using AI to generate creative work or solve a problem defeats the purpose of the exercise; in others, AI fluency is already part of professional practice, and avoiding it would leave students shortchanged.

Similarly, a first-year student building foundational skills and judgment stands in a different relationship to AI than doctoral candidates accelerating literature review in a field they deeply know. In short, a uniform Institute-wide rule would inevitably be too permissive for some contexts and too restrictive for others.

This is not to say that every instructor should have to invent AI policy from scratch. MIT should provide a shared framework – including a common policy menu, disclosure expectations, and accountability standards – within which departments and instructors can make choices. Departments are a natural unit for this work, since students experience a major as a coherent progression and would benefit from consistency about when AI is welcomed, when it is restricted, and why.

2.7. Augmentation not automation

As AI-enabled technologies become more capable, it will be possible – and tempting – to offload more “thinking” tasks to them; not surprisingly, students told us the temptation was greatest when they feared they would miss a deadline. There are early signals, however, that overreliance on chatbots can have a range of significant negative consequences – diminishing critical thinking, weakening memory, eroding confidence, and undermining mastery.

Getting the right answer from a chatbot can create the illusion of learning – but it can also trigger “cognitive surrender”4, where students fall back on AI at the first hint of struggle. In a recent paper5, three noted MIT economists – Daron Acemoglu, David Autor and Simon Johnson – argue for what they call “pro-worker AI.” They make the case that instead of designing AI systems to replace workers, businesses should develop AI that allows human beings “to be more effective at their existing tasks, tackle new tasks, and acquire new expertise.”

In the same spirit, collectively, we need to shape MIT’s use of AI in education to be “pro-learner” – not to replace the “hard fun” of thinking, learning, creating, and problem solving, but to expand what’s possible for students to think about, learn, and solve. In short, AI should be used to augment and enhance curiosity, creativity, and learning, not automate them.

Importantly, this issue is social as well as psychological. Just as automation threatens job loss, deskilling, and the increasing isolation of workers, so too the reliance on chatbots in education displaces peer-to-peer connections like study groups and decreases interactions between students and instructors. If we want augmentation to carry the day instead, we need to see it as more than an abstract, “hoped-for” ideal; it’s a concrete practice that our pedagogies can help promote.

This leads to our final principle.

2.8. Think beyond the classroom and the campus

AI should be integrated into MIT education in ways that enhance, rather than diminish, students’ capacities for growth as whole human beings. In The Culture of Education6, Jerome Bruner, a leading psychologist of education and a pioneering figure in cognitive science, argued that learning is not simply a technical matter of transmitting content from teacher to student, but a process through which students learn how to interpret the world, find their place in society, and develop a sense of themselves as capable persons.

If learning is (mis)understood primarily as content delivery, then highly responsive AI systems may seem capable of replacing teachers, mentors, peers, collaborators, and the interpersonal dimensions of learning altogether. But while this attitude may be increasingly common, it’s shortsighted. Education is a cultural practice through which students learn to make meaning, exercise judgment, form identities, and participate responsibly in community.

Much of what our students gain from MIT is never spelled out in a syllabus or an assignment; it’s what they learn from living and working on our campus in each other’s company – the tacit expectations, habits, relationships, and values that inform how they learn to solve problems, exercise judgment, persevere through difficulty, and become members of an intellectual community. “Residential education” is powerful in part because it happens everywhere: in residence halls, living groups, sports teams, arts groups, clubs, and so on.

A fundamental danger, as we’ve discussed, is that AI can allow students to bypass learning. Equally concerning is that students may internalize a transactional model in which assignments are outputs, teachers are evaluators, peers are optional, and knowledge (or an MIT degree) is an optimizable commodity to be acquired or produced as efficiently as possible.

Such a mental model will not remain confined to the classroom. It will shape how students come to understand work, collaboration, and social responsibility, and they will carry that mindset with them out into the world. However sociable or responsive AI systems become, they cannot substitute for the relationships and practices necessary to grow and mature as a human being.

MIT should therefore approach AI not simply as a tool for improving educational efficiency, but as a force that must be governed, to support the broader purposes of education. Students should learn to use AI productively and fluently, but also in ways that deepen their capacity to participate well in their classrooms, laboratories, workplaces, families, and communities.

The goal is not to shield students from AI, nor to preserve older educational forms for their own sake. It is to ensure that AI use supports the development of people who can think critically, act with initiative, work productively with others, and understand the consequences of their choices in a world shared with nine billion other human beings.

3. Recommendations

This section details the changes we see as necessary for MIT to prepare our students for a new world. Based on the Institute’s longstanding values, our recommendations are not a checklist of individual initiatives that can be implemented one at a time, bit by bit, but rather a set of substantive changes that must be undertaken in concert.

We recognize that serious change takes time. Given the impacts already affecting our community, however, the required changes should be implemented on two timescales: those that happen immediately, and those that begin immediately, but require further study and planning.

Artificial intelligence – in the form of LLMs and other generative AI technologies – presents MIT with profound challenges and intriguing opportunities.

Already these technologies can produce credible solutions and provide reasonable responses to almost any written assignment in our undergraduate curriculum, including essays, math and science problems, proofs, and coding assignments.

Concerning impacts

Because many students are choosing or feel pressure to shift to learning and problem-solving with AI, in less than three years these technologies have driven major shifts in campus culture, including decreased attendance at office hours, reduced participation in online discussions, and, as we heard anecdotally, a drop in in-person study groups in dorms, libraries, and other study spaces. These issues have presented themselves suddenly and dramatically, creating a clear sense of urgency. They also land at a time when higher education is facing other challenges, and MIT itself is considering broad curricular changes emerging from the findings and recommendations of the Taskforce on the Undergraduate Academic Program (TFUAP).

Intriguing opportunities

While these challenges are pressing, AI also offers exciting opportunities for learning and for creating. Many instructors told us that AI tools help them develop customized, interactive learning tools that allow students to explore subject content with more depth and for instructors to create learning experiences for their students that are new or newly tailored to each student. With a little guidance, even instructors who are not proficient in software development can customize AI agents to support a subject or research project. Students can create large-scale software projects with the limited timeframe of classes that would not have been remotely possible before. They can also use AI to analyze data, conduct research, and build tools to amplify their expertise and pursue projects that benefit them and society at large. The potential of these technologies to augment work across campus is immense.

The recommendations we offer below aim to help the MIT community navigate AI’s challenges and seize its opportunities, while reflecting the principles laid out in Section 2. For instance, Humility means that we must create administrative processes for continuous evaluation and revision rather than assuming any change we make today will be adequate in the future. Boldness requires us not simply to try to patch the existing system to limit or counteract the impacts of AI on our students and systems but instead to redesign learning experiences, assessments, and curricula in ways that help redefine the future of education. Putting humanity front and center means that we will seek uses of AI that strengthen rather than weaken the value of an MIT residential education. Leaning into learning means teaching with intentionality, revisiting what we need students to learn, and aligning assessments with those desired outcomes, to preserve the productive struggle essential to a learning-by-doing education. No one-size-fits-all means that we should develop tailored frameworks for adapting to AI rather than uniform AI rules for all students, instructors, or departments. Augmentation over automation means that we prepare students to use AI fluently and in ways that preserve agency, judgment, integrity, and human connection. Thinking beyond the classroom and the campus requires that we anticipate the skills that our students will need for success in work and life, as community members, future leaders, and creators of the next generation of AI technology.

Three broad areas of work

We believe the circumstances call for a substantial reshaping of the student experience. While the life of an MIT student will no doubt look different in 10 or 15 years, as a community we have a responsibility to make sure that it remains immediately recognizable as an MIT experience. Paradoxically, preserving what’s most important will require deliberate change.

We group our recommendations into three sections, briefly summarized below. Broadly speaking, we recommend that, through the combined efforts of the faculty and the administration, MIT:

Adapt educational processes for an AI-aware world (Section 3.1)

• Campus-wide and as soon as possible, review what our students need to learn. Led by instructors and departments, this “AI-aware” process will inform the design of classroom activities that are more social and experiential, and in tune with innovative assignments and assessments that provide feedback on those key student outcomes. This will also require an investigation into the nature of grades and grading.
• Facilitate this process by providing instructors with dedicated resources (for example, additional TAs) and by adapting physical spaces for experimentation, community-building, and AI-free interaction and assessment.
• Create “communities of practice” at MIT that allow members to share challenges and findings, as well as tools and techniques to improve teaching and learning.

Center people, community, and the residential experience (Section 3.2)

• Reinvigorate and reclaim the residential experience based on shared experiences, transparency, and a focus on in-person activities.
• Thoughtfully integrate AI literacy and responsible and ethical AI use as a foundation and throughline for student work, attending closely to how it connects to specific disciplines. This will demand technical proficiency, of course, but it hinges on adopting a human-centered approach to AI as an augmentative tool.

Build processes, teams and tools to enable continuous reflection, iteration and improvement (Section 3.3)

• Establish roles, processes, and ongoing committees to address these challenges.
• Collect feedback and data, stay apprised of research and best practices, connect with peers and learn from them.
• Provide technical and educational support to enable all of this.

In addition, we identify a set of concerns about the wider impact of AI on society, jobs, the environment, and more, that also bear on our on-campus AI policy.

3.1. Adapt educational processes for an AI-aware world

Generative AI affects what our students need to know and be able to do (our learning outcomes), how our students achieve those outcomes, and how we evaluate and assess what they have learned.

Because AI’s ability to competently complete MIT-level assignments makes it difficult to assess student progress based on out-of-class work, instructors urgently feel the need for new assessment strategies.

While we make several recommendations about assessment, we urge instructors to do more than simply try to “AI-proof” their classes. In particular, the ways that AI changes the social aspects of learning – such as how students interact with TAs and engage in office hours and study groups – present an even deeper challenge.

3.1.1. Revisit course goals

Before considering how to construct AI-aware assessments, instructors should reconsider their goals for student learning7 in every subject they teach. What should students know or be able to do by the end of the course?

The learning goals for a given subject may or may not be affected by the availability of AI, but they should be “AI-aware”; that is, they should recognize that AI exists in the world, that instructors may direct or permit it to be used in or out of class in structured ways – and that students may also seek to use it without permission.

The greatest concern for many instructors: When AI makes it possible to offload the cognitive work of learning, how can we assess what students actually know and understand?

In the era of AI, some traditional learning goals may merit rethinking; for example, do the majority of our students need to be able to write complex programs by hand? Individual instructors and departments will need to assess whether longstanding goals are still important, and if so, develop AI-resilient ways to evaluate them.

While learning goals that involve lab, project, or performance-centered work may not require much adjustment, generative AI may offer novel ways to help students reflect on, practice, or prepare to perform. For instance, an instructor reported to us that, for students learning to serve as mediators, providing personalized, course-specific AI coaches eliminated the awkwardness of practicing public speaking in front of others, which substantially increased students’ willingness to practice, which in turn increased their skills.

AI may also enable instructors to devise learning goals that were previously impossible, such as understanding or working in new ways with very complex texts, engineering artifacts, or large software systems.

3.1.2. Ensure durable learning through new course policies, structures, and forms of assessment

MIT’s curriculum is famously demanding. On top of that, many students pack their schedules with high unit loads and multiple extra curricular activities. These competing demands on their time drive students to prioritize efficiency – and nothing could be more efficient than automating work through AI. But if students give in to that tempting option, they cheat themselves of the cognitive friction and productive struggle necessary for actual learning.

AI has both created and revealed a mismatch between established learning objectives and familiar forms of assessment. At MIT, we have long used problem sets, long-form written assignments, take-home exams, and projects completed outside of class to allow students to solidify their knowledge through practice and demonstrate what they’ve learned. AI saps these tools of their value for both teaching and assessment.

Instructors already sense it: In the face of AI, they have to change how they evaluate students. Already, many are increasing the weight of exams in their grading or asking students to write or code during class time. However, these tactical solutions come with a cost: For instance, overemphasizing in-class evaluations means reducing students’ incentive to invest themselves in the difficult, time-intensive p-sets and projects it takes to build the ladder to mastery. By definition, shifting assessments to time-limited class periods limits how much thought and deliberation students can put in. If we want students to care about and know how to create and recognize worthy work – work of scope, rigor, creativity and thoughtfulness – quick, high-stakes evaluations embody the opposite of the signal we want to convey to them right now.

Rather than simply “AI-proof” current methods of assessment, instructors need to revisit what they really want students to know and devise assessments that foster, or even include, the kind of productive struggle that builds durable understanding and mastery.

We urge instructors to consider forms of assessment that are less vulnerable to AI, and more valuable for learning, such as oral exams, semester portfolios, and out-of-class assignments paired with in-class conversations. This likely means resources such as TAs and class time will become more central to evaluation.

As described in Section 3.3 below, we also hope that the Institute will create efficient ways for instructors to share new assessment ideas and best practices that may emerge.

3.1.3. Emphasize experiential and project-based learning

To match the shift towards assessment methods that aren’t vulnerable to AI, instructors need to increase the role of experiential and project-based learning. To encourage this kind of creative teaching, MIT should support the development of teaching skills and practices for all instructors, and recognize contributions in this domain.

Ironically, in some cases AI itself can help open the door to experiential opportunities. For instance, since AI excels at tasks like coding and some types of design, instructors can now assign projects that are much more ambitious. For example, in MIT’s capstone level software engineering class, projects that students could reasonably undertake in a semester often had to be limited in scope and unrealistic in key respects; today, it’s entirely reasonable to expect students to use AI coding tools to build near production-quality software artifacts in a single academic term. This in turn can make room for deeper experiential learning: students can explore how different designs work in the real world, reflect on cases where certain designs outperform others, and explore how the systems they build work in realistic settings.

Opportunities extend well beyond computer science: Architecture students are using AI to experiment with new ways to visualize and rapidly test their ideas, beyond what’s possible with traditional representational skills. This expands the range of creative projects they can undertake (though it only increases the importance of building fundamental conceptual and technical skills, judgment and social reasoning, to ensure students remain in charge of their ideas.)

In a recent Wall Street Journal interview, Harvard economist Rafella Sadun observed that workers who will succeed in the AI era are those “who can creatively think about ways in which AI can generate value, quickly translate ideas into action, and who are ready to revise their intuition through experimentation and validation. Tinkerers and doers thrive in this environment” – an obvious match with MIT’s hands-on ethos and focus on societal impact. As AI enables our students to tackle more sophisticated and challenging tasks, one way to live up to our Mens et Manus motto will be to expand the scope and real-world application of the projects and endeavors we assign.

Finding new opportunities for collaborative, in-class projects provides significant opportunities for growth beyond mastering the particular content. Properly structured and supported, such projects can build students’ strength in collaboration, communication, problem-solving, and emotional intelligence. These durable human skills remain among those that employers prize the most. We would do well to provide more opportunities for our students to build them.

AI can also enhance these experiences, offering students new ways to analyze data, conduct research, and build tools to amplify their expertise and pursue projects that benefit them and society at large. The potential of this technology to augment work across campus is immense.

3.1.4. Build structured in-person social learning into subjects

Because AI tools are disrupting longstanding patterns of social learning at MIT, every subject should include a regular in-person social component (not just sitting in lecture or recitation and quietly taking notes). Instructors should intentionally structure such interactions to achieve desired learning objectives and maintain quality, even in large classes.

Examples of structured social learning include:

• Group projects with weekly staff check-ins and deliverables that assess both individual and collaborative contributions.
• Group problem-solving sessions guided by a lab assistant or TA.
• Feedback discussions structured around a class rubric.
• In-class discussions facilitated by a trained TA or professor, where individual participation is graded.

To get the most value from in-person interactions like these, instructors should clearly explain on the first day of class why they are important for individual learning and building community. Courses that include extensive group projects should offer practical guidance and structures for how to successfully work together.

Although the presence of AI is disrupting social learning on campus, AI tools can also offer real benefits for student learning, including timely help, individualized feedback, accessibility, and support for students who might otherwise be stuck, as well as just-in-time ways to help groups collaborate.

Unfortunately, these benefits become counterproductive if they allow students to bypass the human settings where they would learn how to work with others, communicate their ideas, receive criticism constructively, build confidence, develop judgment, and act as members of a community.

MIT should therefore treat AI not only as a pedagogical or technical challenge, but as a test of whether residential education can be renewed around human presence, shared work, and meaningful mentorship.

3.1.5. Preserve and expand out-of-class research and career experiences

With its launch in 1969, MIT’s Undergraduate Research Opportunities Program8 (UROP) broke new ground by creating a systematic way to give undergraduates hands-on experience with frontline faculty-led research. Today, UROP is a signature part of the MIT experience, directly engaging 93% of undergraduates and 58% of faculty.

UROP and other out-of-class research and learning experiences offer students obvious practical benefits such as academic credit and paid work. But the broader benefits – including personal development, connections with mentors and peers, and career exploration – are even more important. UROP makes an eloquent case for the purpose and value of residential college education.

MIT should preserve, renew and ideally expand its commitment to UROP and other learning experiences that emphasize mentorship, collaboration, and learning by doing; find ways to make them more accessible; and consider extending them beyond the lab.

We also encourage the Institute to explore related efforts to enhance the value of the MIT residential experience, such as establishing co-op programs9 (which the Institute is actively exploring), allowing UROP-like experiences to satisfy degree requirements, or expanding industrial/co-op based graduate research programs. This should include working with organizations on campus that already support experiences like these, including the PKG Public Service Center and MISTI.

UROPs, research assistantships (RAs), and other forms of student participation can give students entry into new intellectual communities: joining a lab, learning from graduate students and postdocs, collaborating with peers at different career stages, presenting work in group meetings, contributing to papers, traveling to conferences, and gradually coming to see themselves as members of a field. Students learn not only methods and technical competencies, but also how research questions are formed, how judgment develops, how mistakes are constructively interpreted, how credit is shared, how disagreement is handled, and how knowledge is collectively produced.

One note about UROP in particular: While undergraduates can provide faculty with useful research labor, that is not the point of the program. It exists to educate. This is why the possibility that faculty may find it advantageous to replace novice researchers with AI agents warrants special concern.

As AI systems become a cheaper or more efficient replacement for UROPs or RAs, students could lose access to the relationships, practices, and shared forms of work through which belonging, confidence, judgment, and professional identity are formed. The danger is not only that students would have fewer opportunities to conduct research, but fewer opportunities to become participants in research communities.

3.1.6. Reconsider grades and incentives

Several peer institutions recently imposed limits on the number of A’s instructors can give in a subject. We do not advocate this kind of grade rationing, in part because we expect it to be counterproductive in helping the Institute navigate the challenges of AI. Responding to an intense societal focus on credentials and grades, students who are set on maximizing their GPA have a strong incentive to use whatever means they feel are most effective to achieve that goal. Rationing top grades would intensify the temptation to cut corners on actual learning by increasing reliance on AI.

As instructors explore new forms of assessment and experiential learning activities, MIT should take this opportunity to consider what role grades play in our overall system, and if the current approach could be improved. For instance, if instructors mainly respond by putting more weight on in-class exams, there’s a risk of narrowing what the MIT credential has long signaled: that MIT students are capable of difficult, independent, and thought-intensive problem solving, not just acing exams on paper.

The committee encourages MIT to explore alternative systems of grading and assessment, including those that work in other countries (such as the UK system, which uses percentages to express relative mastery against an expert standard), and newer paradigms such as competency-based and mastery-based assessments. As one thought experiment, the committee discussed the idea that if MIT did not have grades, many of the incentives around AI cheating would disappear. Note that MIT already refrains from the common practice of awarding summa/magna/cum-laude diplomas, because we believe that earning an MIT degree stands as distinction enough.

Grades are far from the only way for students to demonstrate mastery or gauge their own progress; real-time feedback during an oral exam or presentation, or written comments reflecting on a project submission, likely give students more useful information about their mastery of concepts than they could glean from any grade.

With an increased focus on projects and experiential learning, instructors should consider whether grades are the only, or even the best, mechanism for signaling mastery in all contexts. Alternatives could include elevating the standing of portfolios of projects as a showcase of student achievement, in every field. Already, many employers are less focused on applicants’ grades than on their performance on internal assessments, such as the ability to answer difficult interview questions or demonstrate problem solving and mastery through custom exercises.

3.1.7. Expand in-person spaces for labs and in-person evaluation

In several ways, our recommendations call for more face-to-face activities, from hands-on learning and collaborative lab projects to in-person assessments. All of them require physical space.

Several instructors expressed a desire for collaborative spaces where students can complete extended assignments or exams. These might be fully analog spaces or spaces with AI-free (or AI-limited) computers. Many departments have lab spaces that could support collaborative activities but lack resources to staff them full time. (In fields that use expensive and possibly hazardous equipment and materials, self-service labs are often not possible.)

Collaborative hands-on spaces are useful for teaching, learning, and community building across the disciplines, not just in the traditional lab sciences. We urge MIT to invest in creating and staffing such spaces.

3.1.8. Provide AI use policies, with justification

Students report that the AI guidance they receive from instructors is often confusing and unclear. What’s more, the rules and the way they’re conveyed vary widely from instructor to instructor.

While we recommend against imposing a one-size-fits-all policy on AI use, students are anxious for clarity about AI use: in any given course, they want to feel sure about when, where, and why AI is prohibited, allowed, or required.

Therefore, instructors, and perhaps departments, should make sure that every MIT subject has a clear policy about the use of generative AI, posted prominently in the syllabus and on the course website.

To make this workable, as soon as possible MIT needs to develop a clear and consistent menu of guidelines for instructors and departments to choose from and adapt as necessary. Departments will likely want to take a coordinated approach so that guidelines and rationales are well understood and largely consistent across a given major.

We encourage instructors to use a standardized format to communicate this information, so students can easily understand what is and is not permitted, course by course. Appendix B below provides an example policy.

AI policies should include a rationale. While many courses already have an AI use policy, we recommend that all instructors make sure that their policies include a clear rationale, tied explicitly to the learning goals of the given course, for why AI must, may, or may not be used.

For example, if an instructor wants to ban the use of generative AI tools, instead of simply declaring that AI use is a form of cheating, it’s more effective to explain how generative AI tools shortcut students’ ability to learn the fundamentals of the course or to practice solving the kind of problems they will encounter in exams, future classes, and the real world. This approach is more likely to foster self-awareness and a healthy culture around AI use.

Similarly, instructors who want to encourage AI use in a particular assignment should make it clear why using AI is important to the educational experience. For assignments that feature AI, instructors may wish to include exercises in which students reflect on when and how AI helps or harms their learning, thinking, and morale, and how it changes or expands the work they produce.

Finally, when allowing or encouraging AI use, instructors should make sure students understand that LLMs can fabricate facts and citations, code generation tools can produce incorrect or insecure code, and image generation tools can produce biased, offensive, or otherwise inappropriate outputs.

AI is only a tool, and students are responsible for all work they submit, including any inaccurate, biased, offensive, or unethical content produced with the assistance of generative AI.

3.1.9. Exercise caution with AI detectors and online exam platforms

Many tools purport to detect AI use, and some are reasonably accurate when given a piece of purely AI-generated text. However, most real-life situations are more nuanced: If a student uses AI to complete an outline or edit a section, these tools are unlikely to uncover it.

No doubt the tools will improve. Nevertheless, we recommend against relying on AI detectors, as it risks an arms race in which students respond to automated detection by using increasingly powerful “AI humanizers” to remove signals that AI detectors are cued to catch. The result: a lot of effort on both sides that in the end serves no one.

AI detection systems may also mistake the writing of non-native English speakers or neurodivergent students for text generated by AI. Even low rates of false positives can put students on edge and cause serious individual consequences. More generally, stepping up “policing” around AI use builds an adversarial atmosphere of distrust between instructors and students, which understandably hurts students’ motivation and morale.

Some instructors have asked if MIT will provide so-called “lockdown” browsers for conducting evaluations of students. These are online testing environments that take over the computer during an exam, preventing access to any online resources outside of those provided as a part of the test. The committee recommends that MIT study such tools, but notes that the current generation is buggy, error-prone and feels like surveillance. At least for now, in-person proctored exams – the current norm at MIT – are a better choice in most cases, though they will require appropriate in-person spaces.

Alternatives to these heavy-handed technological solutions are more likely to build trust and support student learning. Options include working on writing in class by hand for early ideation/drafts, creating regular project deadlines or meetings to show project progression, preventing massive amounts of work from piling up near the final deadline, and allowing instructors to give feedback at various stages (not just on the end product).

Other technical tools can also be helpful. For example, instructors can require students to do their work on platforms that capture a history of versions, and to submit the history along with their work. This can provide useful process evidence – for example if a student were to submit an assignment within a few minutes though comparable work would typically take hours. And students themselves often find these tools useful for reflecting on how their ideas have evolved.

Preparing our disciplinary system

While we hope students will follow AI policies for their own benefit, MIT needs to be better prepared to handle serious violations. MIT’s existing academic integrity framework, and the Committee on Discipline (COD) processes that enforce it, were built on a model of authorship in which the work students submitted was either their own or someone else’s. Generative AI collapses that simple binary. A student who uses an AI tool to brainstorm an approach, debug a function, tighten a paragraph, or generate a full draft is not “copying” in any traditional sense, and faculty in our listening sessions consistently reported uncertainty about where the line now lies, both in formulating their own course policies and in deciding which cases to refer to the COD.

MIT needs to make sure that Institute policies clarify what evidence would be required to bring an academic integrity case forward when AI is involved; the COD itself does not consider AI detector output alone sufficient.

Students should not feel policed. Durable change will require instructors to be as clear as possible about their expectations and students to understand AI misuse as an unacceptable deviation from shared peer norms and community values rather than a violation of an arbitrary bureaucratic rule.

3.1.10. Support responsible experimentation in the curriculum

There are many unknowns about how best to integrate AI into the curriculum and how to design experiences to avoid its pitfalls. We encourage instructors and departments to pursue coordinated curricular experimentation in both these areas. It might also make sense to offer variants, for example by developing both rigorous “AI Light” or “AI Heavy” classes or pathways through the curriculum.

Some students and instructors prefer not to use AI (for a variety of reasons). We encourage instructors to take such student reluctance seriously and, where the subject matter allows it, to suggest a pathway that keeps the use of AI to a minimum.

Creating learning experiences that meet clear but perhaps evolving learning objectives, incorporating AI where applicable, and always acknowledging its presence – all while maintaining high standards – won’t be accomplished by superficial tinkering. Instead, it will require bold, imaginative systemwide experimentation and evaluation. Our work on this committee demonstrates the shared will among students, instructors, and staff from every corner of the Institute to creatively address this epochal challenge together.

The committee heard from many instructors that the administrative process for making even small changes to curriculum requirements is time-consuming and burdensome, making it hard for MIT to respond to, let alone anticipate, fast-moving, far-reaching disruptions.

We urge MIT to revise its governance processes to promote more rapid curricular exploration, paired, of course, with thorough evaluation of the results. Departments need to be empowered to explore AI-aware substitutions and alterations to their curriculum on a regular basis without fixed, multi-committee, year-long review processes – or we will be left behind. At the same time, of course, it’s essential to maintain the kind of long-term coherence across our curriculum that has been a hallmark of MIT education (see §3.3 below).

3.2. Center people, community, and the residential experience

AI is disrupting the social fabric of our campus – and the wider world. The backlash against AI in commencement speeches in the summer of 2026 was just one expression of the discontent many feel about AI itself and the role Big Tech now plays in our society.

An MIT education has always meant being challenged by a deep intellectual community that champions creativity and rigor. We must preserve the transformative power of that experience for our students.

AI has arrived at a moment when the social foundations of campus life are already under strain. Across higher education, students report10 high levels of anxiety, depression, loneliness, and disconnection. Confidence in institutions has weakened, political polarization has made campuses more fragile, and the post-pandemic recovery of in-person communities remains incomplete. AI is therefore not arriving in a socially neutral environment. It is imposing new pressures on a community still working to restore the foundational habits and attitudes that residential education depends on: showing up, building trust, enthusiastically joining shared efforts, and living with an easy sense of mutual obligation. AI could accelerate this erosion. Or it could be the impetus for a deliberate rebuilding.

The challenge for MIT is therefore neither to preserve existing practices unchanged, nor to reject AI tools that may support learning and research. It is to act decisively to ensure that AI augments the human communities through which education happens, rather than quietly replacing them – so that the machines serve us and not the other way around.

MIT should use this moment to ask what forms of presence, collaboration, mentorship, and shared intellectual life are essential to residential education – and then design AI policies, pedagogies, and research practices that strengthen them rather than allow their erosion, replacement, or collapse.

3.2.1. Define and communicate the value of residential education

As a residential institution, MIT needs to clearly define the value of bringing students to campus and engaging avidly in face-to-face discussions, dialogue, and problem solving. No one reading this report was educated without these basic human interactions. But we cannot take for granted that incoming students will appreciate their value.

This is the core of MIT: In the spirit of “Mind and Hand,” we work together on real projects that require deep thought and careful, rigorous work.

It’s clear that unthinking, “opportunistic” use of AI tools to complete assignments diminishes what students learn and damages the learning communities they belong to. Learning works when it’s both challenging and social; knowledge is built through cognitive friction, whether that’s disentangling the steps of a mathematical proof with your study group, adjusting an experiment over and over until it works, or having a spirited argument with a peer (rather than getting “the” answer from AI). That’s why it matters for students to go to college!

Students need to believe that doing the work themselves is in their self-interest. It will take more than top-down AI use policies to inspire that conviction. As a community, we need to cultivate a campus culture in which (1) AI restrictions that instructors impose clearly support student learning, personal growth, and professional success; and (2) students have a shared culture that values and fosters residential learning and discourages excessive reliance on AI.

MIT’s official mission11 and values12 unite us all. But our daily decisions, conscious and unconscious, are largely shaped by the unspoken norms and values of our immediate communities – classes, study groups, departments, labs, offices, teams, clubs, living groups and more.

For our students, it’s these in-person communities that foster the social relationships – formal and informal – that allow them to grow, thrive and prepare for their lives beyond MIT.

Employers prize MIT graduates for excellent problem-solving and technical skills. But increasingly, they also want people with the skills of human connection. By prompting students to turn to AI instead of to their fellow students, TAs, or professors, these technologies offer the illusion that human interaction is inconsequential. But learning with and from other people simultaneously supports learning and builds the human skills that make the world go ’round.

3.2.2. Strengthen social connection and personal wellbeing

AI threatens the shared social fabric of MIT and creates new risks to the well-being of our students. MIT needs to invest in creating shared norms, establishing new community rituals, and addressing new academic, social, and emotional challenges introduced (or exacerbated) by AI.

While our core recommendations center on policies for the classroom and AI training, we encourage the Institute to take many other steps to build the kind of community that AI can’t replace. (The recent report of the MIT Task Force on the Undergraduate Academic Program13 includes similar recommendations.)

In the past, the technical General Institute Requirements (GIRs) provided not only a shared academic foundation and problem-solving skill set for first-year undergraduates but also an intense bonding experience around a common core. But this core is fracturing as students rush to take major-specific classes in their first year and put off some of the GIRs until senior spring. Students’ other shared rituals – campus preview weekend, orientation, convocation, the brass rat, and commencement – are widely spaced milestones. Outside of their research groups or labs, graduate students have even fewer shared rhythms and rituals.

Anecdotally, it appears that the rapid adoption of AI may already be affecting student well-being and creating new long-term challenges. Some students are becoming dependent on AI for schoolwork or emotional support; losing their sense of agency, purpose, and confidence; and worrying about their career prospects in an AI-saturated world. Instructors and other academic staff should be prepared to recognize AI-related academic, social, and emotional challenges and to direct students to appropriate support.

We also need to engage many more people across campus in conversation about shared norms and why community matters. One example could be a series of panels that bring together students, people from industry, instructors, and staff, to talk about where they come from and what they love: not just what they do, but how they have handled obstacles and made hard choices in life and career. Other ideas include campus-wide celebrations of human skills and accomplishments, “Tech Free Times” (periods during which MIT would not schedule classes, office hours, or meetings and instead support activities centered on in-person connection), and broadening MIT Reads14, perhaps at the department or residence level, to encourage more people to participate in collective reading, reflection and discussion.

3.2.3. Encourage instructor disclosure around their own AI use

Just as MIT needs norms around students’ AI use, the community also needs to agree and adhere to norms on the appropriate use of AI by instructors. If instructors plan to present students with content substantially generated by AI, or to use AI for some aspect of evaluation, grading, or feedback, we strongly recommend that they be transparent with their students about how and why AI is being used.

While AI can be a remarkably useful tool to help instructors improve lecture notes, generate slides, and create animations or simulations, students made clear that its over-use in the classroom can make them feel that teaching is not a priority for their instructors. A common refrain: “Why should I bother coming to class or doing the work if the teacher is just going to give an AI-generated lecture?” In a poll, most students told the Tech they are uncomfortable with the idea of AI teaching assistants.

Our listening sessions reinforced how alert students are to instructors’ behavior: Students notice when instructors are clearly using AI for slides, feedback, or grading while imposing restrictions on student use, and they perceive it as a double standard. This points not only to a risk but also to an opportunity: by modeling responsible use of AI, instructors can help reinforce the new social contract our community will need to adapt to an AI-infused world.

With respect to evaluation, while AI can be a good way to check assignments against a rubric or automate some aspects of evaluation, many students also have a justifiably strongly negative reaction if, after investing their own time in an assignment, the only feedback they receive is from a machine. An alternative way to use AI graders is to provide them directly to the students along with the assignment, as a way for students to get useful feedback on their performance rather than a way to determine final grades. If we view assignments as a critical tool for learning, both the instructor and the student need to invest their own thoughts and efforts in the process.

3.2.4. Teach effective, responsible, and ethical use of AI

In the fall 2025 Tech Survey, more than two-thirds of students who responded felt that AI would be important in their careers, yet only 25% felt that MIT was adequately preparing them to use AI.

The effective, responsible, and ethical uses of AI are distinct – but interrelated. Students need ways to build strength in each area. We recommend immediately integrating these topics into undergraduate and graduate orientation, and infusing them throughout the curriculum as soon as possible, so community members can build a shared sense of competence in assessing and evaluating AI systems.

• To use AI effectively, students need to learn how to specify a problem or prompt, how to verify an output, when a model is likely to hallucinate, and when not to reach for AI at all.
• Using it responsibly begins with grasping the difference between augmentation and automation when it comes to education: when AI helps a human do better work, versus when it replaces the human (and thereby eliminates the learning that would come from the human doing the work). Responsible use also entails knowing how to disclose AI’s contribution honestly across coursework, theses, internal reports, and published research.
• Ethical use requires each of us and all of us together to explore issues our community has not yet worked through, including judgments about the provenance of training data; the potential for bias; the risk of homogenization in outputs; environmental and resource cost; intellectual property issues; and the line between assistance and misrepresentation of authorship.

These themes can be instilled through the curriculum in a range of ways: Capstone projects in a given major could include AI-related components, such as opportunities to reflect on the limits of AI and its appropriate use within the discipline. Departments may also choose to develop “AI-I-M” (AI Intensive in the Major) courses, and similar foundational courses for grad students, to teach students how the use of AI meshes with practices in their discipline. Communication Intensive subjects should also provide instruction and practice around how to use and when to avoid AI in writing, including the risks of hallucinated citations and of the homogenization of language and ideas. Graduate advisors and research groups should incorporate discussions of appropriate use of AI in conducting and publishing research.

3.2.5. Recognize and mitigate negative impacts of AI

As we learned from both listening sessions and survey results, many people at MIT object to the way most AI models were trained – by reading vast amounts of data from the internet, often without the authors’ explicit consent. Some community members decline to use AI at all because they object to how the data has been harvested without benefit or attribution to its creators.

MIT needs to recognize the broad range of attitudes and perspectives around AI, from enthusiasm to skepticism to outright rejection, and create educational experiences that will allow all students to thrive academically and personally. MIT should also encourage research and policy efforts to mitigate the negative impacts of AI.

While corporations aggressively push their AI products in the education sector, including on our own campus, people are becoming more concerned with the growing size and power of leading technology companies. This is particularly true for students, who have grown up under the sway of powerful social media firms. They would like to see both greater responsibility and greater transparency around the societal, environmental, and social impact of AI tools.

3.2.6. Acknowledge AI use in theses and other research work

Student research is a hallmark of an MIT education. Many of our students contribute to research publications, and student theses, made publicly available through the MIT Libraries’ MIT Open Scholarship15 repository, share our research with the world. Because these publications not only disseminate knowledge but also launch careers, they should reflect the value and integrity of an MIT education.

All theses should include a statement about how AI was used in the production of the thesis. AI should never be listed as a co-author. Most journals and conferences also require acknowledgment of AI use, and some prohibit certain use cases, so researchers should check guidelines before preparing manuscripts for publication. The MIT Libraries provide guidance on how and when to cite AI tools16.

Just as in the classroom, norms play an important role in how AI is used in research, particularly in the preparation of manuscripts and theses. What uses of AI in research are acceptable? How do we educate researchers in the practice of discerning when AI output is useful and factual? Standards and practices in different communities will vary. But we believe that all MIT researchers should view AI as a tool under the direction of a human who is responsible for verifying the accuracy of its output.

3.3. Build processes, teams and tools for continuous reflection, iteration, and improvement

The previous sections outlined many changes, both immediate and longer term, that MIT will need to make to adapt to AI. Given the startling rate of change in AI, however, the solutions we offer today must continuously evolve. Our recommendations in this section are intended to address this need for deliberate ongoing adaptation.

3.3.1. Establish an ongoing AI and education committee

It was clear to all of us on the committee that, although our report is finished, MIT’s work on this subject has only just begun.

To put our recommendations into practice, MIT should establish an ongoing committee. It will serve to monitor the on- and off-campus impacts of AI; evaluate changes in AI models and technology; and incorporate evolving evidence from the economy, learning sciences, and campus experience into policies and practices.

The committee will be the primary group responsible for strategy, monitoring, evaluation, and policy coordination, working closely with the AI Leads, the AI Fellows, and the AI Implementation Team (whose roles we describe in the sections below).

3.3.2. Create school/college- or department-level AI Leads

We recommend appointing AI Leads in each school and the college (or possibly each department) to help with local AI curricular planning, adaptation, and adoption.

Their responsibilities could include shaping tailored AI policies, planning how to adapt each unit’s course offerings in light of AI, and developing new unit-level education and assessment models such as competency-based learning, portfolios, or both.

3.3.3. Fund AI Fellows and an AI Implementation Team

We also recommend that MIT fund an AI Implementation Team and AI Fellows – staff members, postdocs, UROPs, and graduate students with the skills to help instructors adapt their courses to AI in ways that make the most of the latest AI technology and are backed by the principles of learning science.

Including experts in technology, AI, and learning sciences, the Team and the Fellows would work at three levels: supporting individual instructors; developing campus-wide tools; and serving as implementation experts with an “ear to the ground” to study how these tools are being used across campus and to what effect.

Transitioning from longstanding classroom practices and assessments to a new set of AI-aware or AI-enabled practices and assessments presents a daunting task for any individual instructor. Doing this at scale across the Institute will be a massive undertaking.

If we expect instructors to make more than incremental improvements, we need to offer them guidance, direct support, and community. Given that existing instructor and curriculum support units like the Teaching and Learning Lab are already at capacity, the success of this endeavor will require expanding the pool of expert staff.

3.3.4. Create an AI Pilot Fund

Curricular changes are not easy, and many of our instructors, particularly in the largest classes that most immediately need to adapt to AI, already feel overworked and unsure of how to proceed. To support AI-aware revision of course objectives and methods across campus, MIT needs to provide substantial resources.

We recommend that MIT and departments/schools/the college create an AI Pilot Fund that instructors can apply to for resources (such as AI credits, TAs, UROPs, and summer support) to do AI-enabled projects, explore the impact of AI on pedagogy, and create deliberately AI-free experiences.

3.3.5. Provide ongoing training and instructor support

In addition to material resources, instructors need ongoing opportunities to learn how AI is changing teaching, learning, and their own work. Many instructors described having relatively limited experience with AI and feeling unsure about how students are using it and how to use it themselves. Others told us that using AI has helped them develop interactive learning tools, create animations for slide decks, polish lecture notes, and reduce various aspects of course management busywork, while also creating new learning experiences for their students. The tools have already progressed to the point that even instructors with no background in developing software can produce impressive results.

To help instructors navigate both the risks and the opportunities, we recommend that MIT follow the lead of MIT Sloan17 and develop a set of resources for educators, including:

• Holding regular “lunch-and-learn” style seminars where instructors can hear from colleagues about how they are using and adapting to AI. Instructors across the Institute were eager for such “communities of practice” both within and across disciplines.
• Creating online and in-person training on the use of AI for teaching and administrative tasks, such as how to develop animations and simulations, use coding tools, build task-tracking systems, and so on. While these might incorporate components on ethics and AI safety, we urge MIT to avoid “checking the box” by adopting generic third-party AI training programs and instead invest in ways for our own community to provide these resources.
• Institute-sponsored annual or bi-annual workshops on AI and Education for both the MIT and wider academic community to convene and discuss developments and best practices.

Some of these changes can be facilitated by the AI Implementation Team and Fellows mentioned above. The Teaching and Learning Lab, which focuses solely on supporting residential education, can play a key role in helping to organize and conduct some of these activities.

3.3.6. Develop metrics

To understand how AI is reshaping our community and classrooms, MIT should begin tracking metrics around AI use, campus engagement, student satisfaction, and post-graduation feedback. This could include additional AI-focused questions in the biannual MIT Quality of Life survey, or perhaps other metrics, such as additional questions in course evaluations about student/staff engagement and AI use.

3.3.7. Ensure equitable technology access

MIT should continue to provide a “model agnostic” AI platform such as Parley, that does not tie our community to one model provider, and ensure that this platform provides the level of access that our community needs.

Different generative AI models vary significantly in their performance and behavior on different tasks. Commercial AI providers like OpenAI, Google, and Anthropic all offer for-pay hosted commercial services, with the highest tier of plans costing as much as $200/month (as of June 2026). Some researchers reportedly spend thousands more per month on pay-per-invocation requests (“API Access”) for coding tools and other use cases. These commercial providers continually renew their models to offer state-of-the-art performance.

For the MIT community, the result is an uncomfortable inequity: Many students here can spend $200 or more per month out-of-pocket to access cutting-edge AI systems; many of their peers cannot. Some students can thus access tools that can complete substantial course projects or solve very difficult math and coding exercises overnight; others must make do with less advanced models, even if course policy allows unrestricted AI use. This imbalance has the potential to create enormous disparities in course performance.

Similar concerns apply outside the classroom, to administrators and researchers whose productivity is being measured against colleagues with access to more or less powerful models.

As an important first step in addressing access and privacy concerns, IS&T offers the Parley System18, which gives everyone at MIT access to a range of different models, both commercial and open, and provides each user with up to $30/month of free credits to use them. API access for use with coding tools was released in the Summer of 2026.

The access Parley provides is very helpful, but it may not be sufficient for all MIT classes, particularly in software engineering where industry is rapidly adopting these tools. Some users have also expressed concern that the $30/month “budget” for each user is too low. We recommend that MIT continue to survey the community and consider providing expanded access for certain types of uses (e.g., specific AI-focused classes) where students need additional resources. Additionally, we have heard from several groups that even now, they still pay for commercial models because subscription plans effectively subsidize token usage relative to Parley’s per-token pricing.

Because agentic systems and tools are increasingly part of AI practice, whatever systems we employ should provide access to agentic coding and working tools, in addition to chat interfaces. We also need to ensure that we provide adequate access to computing resources that students require for classes and projects where they