The decision comes after Alibaba sued the government for violating its constitutional rights.

A single chip bought on a whim during a late-night scroll turned into an excuse to rescue an old laboratory microscope and finally see what its silicon actually contains. The part in question is a Motorola MC68701, a microcontroller built in the early 1980s. It packs an enhanced 6800-family processor, 2 kilobytes of ultraviolet-erasable program memory, 128 bytes of RAM, a serial interface, a programmable timer, and 29 input/output lines all onto one piece of silicon. In its day that counted as a complete small computer in a single package, and it could even reach out to external memory to grow beyond its on-chip limits.

The chip’s ceramic container contains a small quartz window that directly covers the silicon. That window exists so that UV radiation can wipe the program memory as needed. It also allows anyone with the proper optics to see the die without having to open the packaging or use harsh chemicals. That feature was what made the entire endeavor possible.

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The person who ended up with the chip realized that a simple microscope would suffice to begin exploring. An affordable Nikon Labophot trinocular model appeared on eBay, complete with the original lighting system but in poor condition. Bringing it back to working order required numerous procedures. The power supply required maintenance since a transformer had become loose within its housing. Once fastened and tested, the optics were meticulously cleaned with high-purity alcohol and soft swabs to eliminate decades of dust and haze. A flexible LED light source was added to the top for reflected illumination, and a standard microscope camera required a special adapter manufactured on a small CNC mill to fit firmly over one of the eyepiece tubes.

He placed the MC68701 under the microscope. Even at low magnification, the bond wires that connect the silicon to the package pins were visible; they were simply thin gold strands arching from tiny pads on the die’s edge to the chip’s legs. Moving on to the 4x objective (approximately 40x total when you include the eyepieces), you can see the surface details. The largest visible structure is the program memory array, which is a vast, regular grid that covers a large region, with each little compartment looking nearly identical to its neighbor. Not bad for read-only memory, which is designed to be dense and simple.

There are line-driver transistors nearer the die’s edges, near the bond pads, because these are the circuits that generate the signals required to connect the device to the outside world. The transistor forms differ from the dense logic in the core, and faint squiggles of metal trace extend all the way up to the pads where the gold wires are connected. When you see those drivers in action, it becomes evident how the chip sends and receives information. Multiple layers of metal traces run over the surface, some horizontal, some vertical, and some on a higher plane, allowing signals to cross over without shorting. Even without delying the chip, you can see the stacking effect, demonstrating how meticulous they were in fitting everything into such a little space.

Other functional blocks can be found elsewhere on the die. There are three components: one for instruction decoding, one for arithmetic and logic tasks, and a small piece for the on-chip RAM. None of these blocks required labeling because their sizes and locations indicated exactly what they were intended to perform. Once the microscope is focused, the entire active surface fits comfortably into the field of view, but all of the features are so small that you need steady illumination and a bit of care with the focus knob to discern the minute details.

[BillPg] has been designing a fantasy 1980s-era home computer. As part of the exercise, he’s reevaluating all the assumptions that have grown organically over time in the small computer landscape. Hindsight is, so they say, 20/20, but sometimes hindsight can also be colored by modern thinking. Sometimes an idea that seems stupid today made sense in the context of its time. In particular, [Bill] has thoughts on the much-maligned 8086 memory segments.

If you haven’t run into it before, the 8086/8088 had a problem. It wanted to be more or less conceptually software compatible with the 8080 and Z80 computers, which had 16-bit addresses, leading to a limit of 64K of memory. When Intel was designing the next generation of chips, it knew that 64K had to go, but telling developers that code would require huge reengineering was a non-starter. So the idea was to provide multiple 64K spaces broken up into segments.

As with most things, there is theory, and there is practice. In theory, a 16-bit segment provided four extra address bits to add to the existing 16-bit address, producing a 32-bit address, even though the CPU only had 20 bits of address bus. Code that fit in 64K could pretend like that was the whole world, and a tricked-out system could have 16 worlds. Future systems could, in theory, have had more.

In practice, Intel made the segment the top 16 bits of a 32-bit address and then added it to the ordinary 16-bit address. So address 0000:0010 (segment=0, address=10 hex) is the same memory location as 0001:0000. Address 0010:0010 is the same as address 0000:0110 and 0001:0100. This wasn’t really the intent, just a byproduct of how the chip worked.

Eventually, the segments would become indices into a table (like the title graphic), but by then, bad practices wiped out a good idea. It is doubtful that the original designers thought anyone would take advantage of the overlapping address, but, of course, they did.

By the time the 80286 and beyond produced segments that were really keys which defined a block of memory, everyone was already in the mode of using the segment and offset as a large pointer. C compilers even had “modes” that let you treat the segment as just more address bits. Because of that, even on newer processors, people had a tendency to build a “flat” segment and use it. That is, make a segment that starts at 0, ends at the end of memory, and then forget about segments.

In fact, many people independently discovered that you could define a flat segment in protected mode, return to real mode, and then enjoy a flat address space. This was later christened unreal mode, and a topic we’ve covered a few times before.

We agree with [Bill]. Segments were a good idea at the time and might have been more important if people had used them the “right” way. Of course, there would have been ups and downs. Proper segments might have allowed for easy virtual memory, for example. But at the price of possibly swapping in and out huge segments instead of relatively small pages. Today, most of what segments were supposed to do is part of the memory management unit and is mostly hidden from the application developer. Still, interesting to reflect on why Intel made that choice and how we got to where we are today.

5G telecommunications, according to industry hype when 5G first launched in 2019, was going to be all about buzzy applications like mobile augmented reality and autonomous vehicles. But the surprise plot twist came when replacing home cable internet turned into 5G’s most widely adopted new application.

Fixed wireless access (FWA) now serves over 14 million U.S. customers, and contributes 28 percent of worldwide wireless traffic. Fixed wireless access is what the term sounds like: broadband internet delivered over a cellular radio link to a stationary location—no cable, no fiber, no trenching, no satellite broadband antenna pointed at the sky. What makes FWA distinctive is that it repurposes the same towers, spectrum, and 5G infrastructure that was built for mobile devices.

One U.S. Federal Communications Commission commissioner has called FWA 5G’s killer app. And that’s true not just in the United States either. Jio, India’s largest carrier, is also one of the world’s largest FWA providers, with over 9 million customers as of last year.

Carriers discovered they could repurpose surplus 5G capacity, while also exploiting a usage pattern quirk: mobile traffic starts to drop after 8 p.m., just when home internet usage peaks. The result is broadband, delivered via traditional cellphone towers, at a lower cost than fiber deployment. For these reasons, FWA provides real price competition to cable broadband, while reaching underserved rural and suburban communities.

Fixed Wireless Access Repurposes Ambitious 5G Infrastructure

FWA is cheaper to deploy than fiber, and for most homes and small businesses, fiber’s gigabit speeds are overkill anyway. And since FWA uses the same wireless networks built for cellular service, FWA works anywhere that receives a steady cellular signal.

As cellular networks extend into areas with minimal service, FWA’s coverage map expands with them. In these remote locales, the other main viable broadband alternative typically comes from satellite services like Starlink—which are, compared to FWA, more expensive, with higher delays, and lower bandwidth.

While most FWA deployments use currently underused microwave bands, some FWA deployments use electromagnetic spectrum that 5G launched but that mostly failed with mobile users. Millimeter waves operate at frequencies 10 to 40 times higher than 4G’s spectrum, offering high data rates from their wide available bandwidth.

However, there are good reasons 5G mobile users today don’t generally use millimeter-wave spectrum. Millimeter waves can’t penetrate buildings. Plus, they lose signal strength within a kilometer or two of the transmitter. Millimeter-wave antennas are also a real drain on cellphone batteries compared to microwave and radio-wave tech.

Yet none of these challenges applies to a fixed station with a clear line of sight to a nearby tower. FWA home units (called customer premise equipment or CPEs) outperform 5G handsets by a significant margin. That’s mostly because of hardware. CPEs carry larger, more sensitive antennas than a typical cellphone, paired with more capable transceivers. CPEs also tend to be plugged into wall outlets, making battery concerns a nonissue.

Another 5G technology that did not gain traction in mobile wireless is multi-user multiple-input multiple-output (MU-MIMO). A base station with MU-MIMO uses an array of antennas to serve multiple users on the same frequency simultaneously.

However, maintaining a MU-MIMO signal involves tracking each user individually—a problem that quickly becomes overwhelming with enough mobile users. FWA is different, however. Static CPEs, with their steadier downlink traffic loads, are an ideal match for MU-MIMO technology.

So, FWA internet service not only uses mostly fallow spectrum but also uses 5G spectrum more efficiently than do 5G mobile users—for whom, of course, these 5G technologies were originally designed!

How FWA Became 5G’s Surprise Killer App

Not long ago, the high-bandwidth use cases for 5G made for an impressive list: millisecond latency for autonomous vehicles, mobile augmented reality headsets with extensive high-speed data needs, and massive machine connectivity for an expanding internet of things (IoT).

These applications have all stalled. Autonomous vehicles pose challenging—and still unsolved—problems unrelated to spectrum allocation. Augmented and virtual reality technologies have yet to create meaningful spikes in bandwidth demand. And the IoT has, to date at least, fragmented across an array of competing standards.

Mobile carriers had built dense 5G networks for mobile customers whose needs rarely saturated the network’s capacity. Home broadband usage peaks in the evening hours, precisely when cellular networks are quietest.

FWA sits at cellular networks’ crossroads of supply and demand.

The Advent of 6G Will Only Expand FWA’s Reach

In December, the telecom standards body, the Third Generation Partnership Project (3GPP), issued its latest 5G specification—Release 20, the final “5G only” update. So, although 6G is still years away (its first specifications are expected in early 2029), engineering decisions that will define 6G are being made today. And FWA is not on the margins of that conversation; FWA is currently considered an established day-one use case.

6G wireless technology promises to expand FWA’s reach—not only via spectrum but also via geometry. Instead of following 4G and 5G’s connectivity model—strong signals near towers and weak signals far away—future 6G networks will let homes connect to multiple towers simultaneously, using a technology called distributed MIMO (multiple-input, multiple-output).

Where 5G’s version of MIMO (a.k.a. massive MIMO) concentrates user communication with dozens of antennas at a single tower, distributed MIMO uses antennas across multiple base stations and coordinates them to deliver signals to your home from multiple directions simultaneously.

The practical result: Because no single tower is responsible for any given connection, the “edge” of a cell network—that outer boundary where signal strength falls off and service degrades—no longer represents a hard limit on who gets well served. A home that would once have been too distant from a tower, or blocked by terrain, could now be within reach of several base stations working together.

6G may eventually adopt distributed MIMO technology for mobile users, when synchronization challenges and other signal engineering hurdles are solved and deployed for real-world cellular networks. The jury, as of 2026, is still out on whether the full distributed MIMO problem will be solved once the 6G standards start to be set in place, within three years.

As demand for FWA grows, carriers will also deploy increasingly capable millimeter-wave infrastructure for fixed customers first—the stationary CPE use case that millimeter wave best suits. The dense millimeter-wave antenna infrastructure that FWA requires is the same infrastructure that future mobile applications will eventually inherit. AR glasses, AI-powered wearables, and other bandwidth-hungry applications originally promised for 5G are not canceled—they are waiting for the infrastructure to arrive.

The pathway to FWA is being prepared at lower frequencies, too. There is growing interest today in the largely unoccupied FR3 band, which spans roughly 7 to 24 gigahertz, situated between crowded low/mid-bands and the much higher millimeter-wave frequencies.

Recent field trials by Nokia have demonstrated FR3’s viability for both cellular and FWA applications. FR3 is emerging as one of the more promising near-term frontiers for extending FWA coverage beyond its current footprint.

None of this was the plan. No carrier executive in 2020 stood on a stage and announced that 5G’s defining achievement would be delivering living room broadband to rural homes and suburban subdivisions underserved by cable.

FWA became 5G’s killer app because the engineering economics made it happen. Surplus wireless capacity met unmet consumer broadband demand, with the physics of a stationary receiver doing the rest.

That is not a criticism of the engineers or the carriers. It is simply how technology sometimes advances—sideways, through gaps nobody was trying to fill.

But FWA’s model of prioritizing unconnected users may in the end prove to be telecom’s on-ramp to everything else. Fix the digital divide first. Tomorrow’s sci-fi future appears set to follow close behind.

The AI story has mostly been told through chips, data centres, and the companies building the models. It is now being told through the shampoo aisle.

The world’s largest makers of everyday goods, the businesses behind the bottles and packets in most kitchens and bathrooms, say they are using artificial intelligence to design products and run the campaigns that sell them, turning a technology associated with software into a fixture of the consumer-goods lab.

It is the same wave of enterprise adoption that has pulled AI tooling into corporate software stacks, arriving now in categories as unglamorous as body wash and biscuits.

Procter & Gamble offers the clearest example of what this looks like inside research and development.

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The company says it used AI to screen tens of thousands of peptides in developing a formula for a Pantene product, drawing on an internal database of more than 8,500 formulations to predict how a mixture would feel on skin or hair before anyone mixed it.

The point is not novelty for its own sake. It is time. Steps that once required rounds of physical testing can be narrowed down computationally, which pushes candidates toward consumer trials faster.

Mondelez, the snacking company behind a long list of familiar biscuit and chocolate brands, describes a similar shift on the food side.

It says an AI product-development tool has helped it generate dozens of new formulations, and that the software lets developers move between two and five times faster than conventional methods.

The same generative systems are being pointed at marketing, producing personalised images, text, and video at a pace traditional studios cannot match.

Unilever has leaned hardest into the campaign side. Its Dove brand ran a cookie-scented body-care line in partnership with Crumbl, with AI involved across the effort, from product direction to the selection of influencers and the creative itself.

The company reported the campaign drew billions of impressions and brought a large share of new buyers to the brand. Whatever one makes of a cookie-scented soap, the mechanics are instructive: a single AI-assisted pipeline running from formulation to feed.

What ties the examples together is compression. In consumer goods, the traditional cost of experimentation is measured in months of lab work and test batches, and the traditional cost of a campaign is measured in agency hours. AI attacks both.

Reformulation becomes a search problem over known ingredients, and content becomes something generated and varied on demand, an approach that mirrors the advertising ambitions on display when OpenAI pitched AI-made ads at Cannes.

The claims deserve some caution. Most of the specific figures come from the companies themselves, and consumer giants have every reason to present their AI programmes as further along than they are.

Product development still ends with human tasting panels and dermatological testing, and a formula an algorithm likes is not the same as one a shopper buys twice.

The industry’s own researchers have flagged that AI-generated marketing often drifts toward the generic, missing the brand-specific character that makes a campaign land.

Still, the direction is consistent across firms that rarely agree on much. The reallocation of enterprise budgets toward AI agents and tooling has become a general feature of large companies, from Tencent’s enterprise agents to the consumer-goods R&D described here, and the packaged-goods sector is not sitting it out.

For shoppers, the visible result will be mundane: more variants, faster refreshes, scents and textures that arrive and vanish more quickly than they used to.

The machinery behind the shelf is changing even where the products look the same. A bottle of shampoo is, increasingly, the output of a search.