Tag Archives: AI

Data Sovereignty

Lately I’ve been hearing a lot about data sovereignty. Indian Tribes want data sovereignty. Countries around the world and state and local governments in the U.S. are talking about data sovereignty. Corporations are talking about data sovereignty, even though they use different terms to describe it.

The concept behind data sovereignty is simple. It means keeping data internally and not sharing it with the outside world, which today means keeping it away from the AI companies that are training their models on every bit of data they can put their hands on.

Consider corporations. I’m positive that a huge amount of what happens inside corporations ends up in the AI cloud. It seems inevitable that employees in every company are relying on AI to help them compose emails, analyze budgets, and write memos. Employees use AI notetakers to capture every word said in meetings. Even if a company has a policy of not doing any of this kind of work using public AI, you know employees are doing so privately to make their jobs easier.

Entire countries are talking about data sovereignty, particularly in Europe. There is a quickly growing top-down movement on the continent to ban and replace large U.S. software tech vendors. We’ve recently seen the governments in France, Spain, Germany, the UK, and Switzerland begin the process to get rid of Palantir, a U.S. surveillance software vendor, and replace it with European software. Each country doesn’t only want to protect data from U.S. tech companies, but also from each other. There is a lot of discussion in Europe about replacing many other U.S. tech firms.

The data sovereignty effort in the U.S. is much more of a bottoms-up movement. Many city government CIOs are demanding to have local control of citizens’ data. This partly comes from a rash of cities and local governments that have been hacked by ransomware attacks in the last few years. But this is also in response to citizens demanding that local governments protect their data.

Of course, there is opposition to the data sovereignty idea. For example, there is a tech coalition that has been pushing the Administration to create “freedom cities” that are exempt from federal regulations and where tech companies would have free rein. An effort to do this in Toronto failed a few years ago when citizens rebelled against the idea of creating a high-tech surveillance community.

Indian Tribes might be ahead of everybody else on this issue, and there is a lot of discussion of keeping all tribal data sovereign from the rest of the world. Tribes have no interest in the federal government getting their local data and are even less interested in seeing tech companies profiting by selling the data to anybody willing to buy it.

The idea of data sovereignty runs completely counter to the idea of building huge data centers that amass all of the knowledge of mankind. Sovereignty is really about privacy and logically leads to an AI environment comprised of local AI servers for corporations, local governments, and Tribes that are not connected to the cloud or to the big AI data centers.

Interestingly, the data sovereignty movement is not anti-AI. It instead comes from those who are against having their data gobbled up by a handful of big tech companies that want to monetize their data with no care for what that means to the people, companies, and governments that generate the most useful data. It’s likely that most entities that shield themselves from big AI will use AI internally inside their own network. I think a whole lot of people have already seen the massive invasion of privacy that is an integral part of AI and are already looking for ways to opt out.

After the AI Crash

Everything I read about the AI industry leads me to think there will be an AI crash. Consider the following:

  • Unsustainable Capital Expenses. It’s hard to imagine there can ever be enough revenue to pay for the huge capital investments in data centers and electronics. Several analysts have estimated that it will take $2 trillion a year in revenue to pay for the infrastructure that has already been built, and there are no believable forecasts for generating even half that much revenue. The capital needs of the industry are relentless since expensive AI data center electronics have to be replaced within five years, or less.
  • Circular Revenues. A small handful of tech firms, chip manufacturers, and AI companies are propping each other up by investing and buying from each other. If one stumbles, they might all fall.
  • Huge Debt. Much of the industry is being funded through debt, which has to eventually be repaid, instead of through equity.
  • Public Pushback. Local governments and people are increasingly pushing back hard against the creation of new data centers. Most new technologies have been welcomed by the public with open arms.
  • Increasing Corporate Skepticism. The news is full of stories of corporations that are throttling the employee use of AI since the costs to use the software are a lot higher than expected. There are many companies having second thoughts about replacing people with AI. The AI industry needs complete corporate buy-in to have any chance of succeeding, and large companies are generally still on the sidelines.
  • Diseconomies of Scale. Every new technology I can think of thrived, in part, due to economies of scale, where the larger the industry grew, the more efficient it got. AI is going in the opposite direction, where every new AI model consumes more resources than its predecessors. This may turn out to be the fatal flaw – the bigger the industry gets, the more its operating costs increase.
  • Institutional Warnings. Moody’s recently warned that high AI infrastructure spending threatens the credit of AI companies and their large tech partners. I read recently that the number one question being fielded by investment advisors is people asking how to divest from AI.

I don’t have a crystal ball to foresee the nature of the crash. It could be a total crash like the 2000 tech crash, where four out of five tech startups disappeared practically overnight. I lived in the DC area at the time, and I will never forget the rows of abandoned CLEC headquarters buildings in Northern Virginia. A crash could be milder, where a few firms disappear, with the outlooks for the survivors greatly diminished, and industry expectations are reset to something more realistic.

The reason I wrote the blog is to speculate about what happens after an AI crash. I foresee some of the following consequences of an AI crash.

  • An article in the Economist said a total crash would wipe out $20 trillion in U.S. wealth. That means wiping out the wealth of the investors in the new technology, along with a huge hit on the stock market.
  • Data center construction would stop dead, and unfinished projects would collapse. Communities that contributed to the costs of bringing data centers will end up eating those investments.
  • There will be stranded investments by electric utilities and water companies that built new infrastructure to support data centers. They won’t eat these losses, though, which will all be passed on to ratepayers in the form of higher electric and water rates.
  • A lot of vendors will be in big trouble. Companies that pivoted to supporting data center electronics, like Micron, might fold. But a lot of other vendors also would take a big hit. For example, Corning announced investments in three new fiber factories just to support data centers.
  • There have been some huge investments by carriers in middle-mile fiber to support data centers. The companies that made these investments won’t see the expected revenues.

The most interesting thing about a major crash is that it can do as much long-term good as it does short-term harm. I want to again use the analogy from the tech crash. I know of at least a half dozen CLECs that had business plans to capture 30% of the voice and data market in Atlanta. The crash cleaned them all out of the market, but without the crash they would have all failed more slowly. The tech crash brought a sense of reality to the telecom market, which still experienced phenomenal long-term growth after the original tech companies had died.

I don’t think there is any chance of AI failing as a technology. But that doesn’t mean the early developers are the ones who will see the ultimate success. Most, and maybe all of today’s players might be gone. A crash will bring financial constraints, which would mean that AI companies will have to figure out efficiency and economies of scale. If AI is ever going to be a viable technology, it has to control costs and be able to pay for itself. It’s hard to foresee today’s companies somehow reaching that point without some kind of market reset.

Is AI Changing Traffic Patterns?

Mitch Wagner of Fierce Network recently published an article that claims that AI is blowing up 30 years of traffic network assumptions. He claims that AI traffic is smoothing the daily peaks and dips in network traffic that all ISPs are familiar with. While every ISP is a little different, any ISP that serves a lot of end-user customers expects traffic peaks in the evening, smaller peaks during the daytime, and very low levels of network traffic at night.

Network engineers have always paid close attention to the peaks, which were the main factor in determining the size of network connections. Nobody wants to have a network that restricts bandwidth when customers want to use it the most. The cable companies learned this lesson the hard way during the pandemic when customers suddenly needed to work and school from home and found the broadband connections unable to meet their needs, particularly in homes where more than one person wanted to use the network at the same time. Every network engineer I know can cite the busy hour, busy day, and busy week on the networks they manage.

Wager says the peaks in traffic are evening out and that networks are seeing a more consistent demand throughout the day. Wagner cites Ed Fox, the CTO of MetTel, from an interview given for a Fierce Network Research report.

I have no reason not to believe Mr. Fox. However, MetTel is an ISP that operates in major urban centers and likely serves the kinds of businesses that have become heavy AI users. It’s not hard to imagine that an urban ISP serving businesses might be seeing a drastic change in traditional network traffic patterns. But I have to think that MetTel and other urban business-centric ISPs didn’t have the same traffic patterns as other ISPs before the advent of AI.

I work with a number of ISPs and I have not heard anybody talking about a big change in traffic patterns. My clients work in a variety of markets, from rural to urban, but none concentrate on the urban market business that MetTel is experiencing in places like New York City.

However, I appreciate the article, because if it’s true, then most ISPs, except for fully rural ones, might eventually see some shift in traffic patterns due to AI. I am curious to get feedback from this blog from IPS to hear if anybody is seeing anything like the big changes being experienced by MetTel.

The Wagner article also claims that upstream bandwidth is growing faster than downstream, reversing a 30-year design assumption built around heavy downloads. This is something that has been well documented by OpenVault. They’ve been shown that upstream usage has been growing faster than download usage starting in 2024. In the first quarter of 2024, national average upload usage increased by 13.2% compared to 7.7% for download. In the first quarter of this year, average upload growth was at 19.8% while download growth was at 7.8%. OpenVault credits most of the growth in upload usage to computer software synching with data centers.

Wagner claims the upsurge in upstream usage comes from AI inference traffic moving towards the edge, particularly for video processing. What he means by that is big growth coming from uses of video cameras for functions like AI-driven video surveillance at retail locations, camera-equipped wearables, and cloud-based operational technology in applications like oil and gas asset management. I hope that the big national companies that monitor traffic begin tracking this issue. I’d love to hear more about the trends in specific traffic, like video surveillance and wearables.

Network engineers all understand that upload traffic is usually a tiny fraction of download usage, with average download usage a dozen times more than upload. With the possible exception of cable companies, upload usage is largely an afterthought for network engineers, who barely consider it when sizing and designing networks.

I have no doubts that there are localized situations where AI traffic is making a big difference in network traffic. But for ISPs that mostly serve residential and small business customers, I still have to think it’s a tiny, possibly unnoticeable blip.

Is There an AI Divide?

I recently attended and spoke at an AI conference. One of the things that became clear to me is that we are probably headed for a new digital divide related to AI. What do I mean by that?

In that short period of time, AI has touched a large majority of computer users. A survey from Pew in September 2025 showed that 95% of adults had heard of AI. At the time of the survey, 47% of people had heard a lot about AI, up from 26% measured in a 2022 survey. That’s bound to be a higher number in the summer of 2026. As you might expect, people with a high awareness of AI tend to be younger and better educated. For example, 62% of adults under 30 had heard a lot about AI, compared to 32% of those 65 and older. 60% of adults with post-graduate degrees had heard a lot about AI, versus 38% of those with a high school diploma or less.

Only about 3% of U.S. households pay for a consumer AI subscription. There is a lot of expectation that AI companies will be forced to greatly increase the costs of monthly access, which will undoubtedly lower the percentage of folks who are comfortable working directly with AI. Most people who use AI today interface through some tool like virtual assistants, GPS navigation apps, streaming algorithms, or weather forecasts. According to a survey from Quinnipiac University from March 2026, 51% of respondents say they use an AI tool, up from 37% a year earlier.

Another Pew poll showed that 46% of Americans hold a negative view of AI, while only 26% view it favorably. 57% believe the risks outweigh the benefits, and 41% actively distrust AI systems.

All of these statistics point to a possible AI-divide, separating those who believe in and use AI, from those who don’t trust AI and refuse to use it. This divide already exists, and the future question that will  have to be answered is if those who distrust AI will eventually be won over. It’s easy to forget that AI has only been available to the public since the end of 2022, which, for a new technology, is still in the infancy stages.

There will be consequences if there is a permanent gap between those who use AI and those who don’t or won’t. If AI brings measurable advantages to those who use it, then, over time, non-users will be at a disadvantage in many ways. There is also the possibility that those who distrust AI will be able to thwart its expansion. There are already numerous communities creating barriers to new data centers.

It’s easy to conclude that using AI is a choice, and that anybody who doesn’t use AI has to live with that decision. But consider a few statistics from the National Center for Education Statistics (NCES). It reports that 21% of adults are functionally illiterate, meaning they cannot complete basic reading tasks, such as filling out job applications, reading medicine instructions, or helping with children’s homework. 54% of adults have reading skills below a sixth-grade reading level. Less than half of adults demonstrate strong reading comprehension skills.

These statistics are relevant when talking about using and benefitting from AI, which is ultimately a language model. Literacy is far less of an issue when talking about the use of broadband, because it doesn’t require advanced reading skills to watch videos and engage in other forms of online entertainment and social media.

It’s way too early in the life of AI to draw any conclusions about a possible divide, but it’s not hard to foresee a likely divide between those who benefit from AI and those who can’t or won’t. In a decade, this might become the most important digital dividing line, more so than the digital skills divide we talk about today.

An AI Digital Inclusion Research Tool

In many ways, we are at the heyday of broadband. Huge numbers of households are being reached by new broadband networks funded by numerous state and federal grants. Many non-profit groups are working hard to make sure people have the computers and other devices needed to take advantage of connectivity. Many other people are teaching people how to use computers in order to navigate the online world. Digital navigators are helping folks find broadband connections they can afford.

Unfortunately, big changes in the federal government have meant that the funding for many of these activities is quickly evaporating. The administration killed the $2.75 billion Digital Equity Fund, which would have funded digital inclusion work around the country. There is still $21 billion of potential BEAD non-deployment funding, but that money seems to be stuck in limbo, and the general feeling is that even if some of these funds are released, they will only be available for specific purposes that might not include digital inclusion.

It has always been clear that the big federal funds aimed at digital inclusion work were only going to carry the national digital inclusion effort for a few years, but that funding would have given the digital inclusion practitioners the time to mature and be ready in a few years to self-fund and stand on their own. Folks involved in digital inclusion are now scrambling to survive after the abrupt end of funding.

One way forward is for digital inclusion efforts to become more efficient and better organized. That’s not as easy as it might sound. The digital inclusion ecosystem is still relatively new, and there are a lot of different groups in any state with different approaches for tackling digital inclusion solutions. Becoming more efficient might mean digital inclusion practitioners joining forces to gain efficiency. Being efficient with less funding will mean choosing projects where digital inclusion work will produce the best local results.

I’m working with some folks in North Carolina who have developed a tool that can help digital inclusion efforts be more efficient. The core of the tool was developed by Brian Rathbone of Broadband Catalyst. He’s named the new tool Marco Map AI, which is appropriate because it’s a gateway to discovery. This tool starts by gathering every possible data source and database available that has information that could inform the digital inclusion effort. In North Carolina, that means dozens of different data sources.

Some of the data is what everybody would expect, like the FCC broadband maps and U.S. Census data. But there is also a wide range of data available for things like health care data, housing, the location of anchor institutions, and education.

When Brian first brought the data together, it quickly became apparent that the sheer magnitude of the data could be overwhelming, and even a good researcher has a problem making sense of multiple disparate datasets. Brian’s first step to make it easier to understand data was to map everything. It turns out that the human mind can more quickly grasp a map of data points than tables of facts. But even mapping is overwhelming when there are dozens of attributes being mapped.

It turns out that the missing tool to make sense of the huge pile of data is AI. AI can be used to find patterns in complex data sets that a researcher would probably never find on their own. Perhaps the best way to explain how this works is with an example.

Let’s say that a hospital wants to do a better job on improving outcomes for diabetes patients in the region it serves. Research and hospital experience show that enrolling diabetes patients in home monitoring while staying in touch through telemedicine can greatly improve patient outcomes – and hospitals know that proactive care saves money for patients and the hospital. Hospitals already know the patients they are connected to, but how do they find the other people in the region who could benefit from proactive care?

The Marco software with the AI assistance can answer this question in a way that I don’t think a human researcher can easily do. The system can cross-check all of the factors related to given question. It would start with a map of areas that have a higher prevalence of diabetes. It would overlay this with factors related to broadband and telehealth. For example, what areas have good or poor broadband technologies? What is the level of home broadband subscriptions and computer ownership? Where is cellular technology adequate to handle the monitoring needs? How do households incomes and poverty levels impact the ability of homes to afford a broadband connection?

The AI system can answer incredibly detailed questions in a way that I’ve not seen done by other research. For example, you could use the software to identify the neighborhoods that are within twenty miles of a hospital or clinic, where the prevalence of diabetes is higher than average, where people have broadband or good cellular coverage, and where incomes are high enough that many people can afford broadband. After answering this kind of question, it’s easy to quickly modify the parameters of the questions to fine-tune the results until it provides what the hospital system wants to know. There are endless questions that could be posed.

This is amazing tool for policy makers and those who are trying to tackle broadband access or digital inclusion issues. I highly recommend anybody interested in this tool to contact Broadband Catalysts.

The AI Boom and Broadband

I’ve written several blogs recently about the impact of AI on broadband. For example, it’s becoming clear that a lot of AI applications will require better upload broadband speeds. ISPs that haven’t yet upgraded upload speeds will likely find themselves at a competitive disadvantage, similar to what happened to cable companies during the pandemic, when it became clear that upload speeds were inadequate to support multiple folks working and schooling from home. I also wrote a blog that discussed the big increase in web traffic that has sprung up from AI web crawlers that constantly search the web for new content.

There are other, more subtle, impacts from the AI boom. One of the unexpected issues is the sudden shortage of electricians in regions where AI data centers are being constructed. Electricians are needed during the massive effort to wire a new data center used for AI. What was unexpected is that data centers permanently employ multiple electricians to maintain the cooling systems and other ongoing needs at an AI data center. Data centers are offering attractive salaries and benefits and luring electricians who would otherwise have been busy building new houses or maintaining existing ones.

I’ve been reading stories about parts of the country where there is a sudden shortage of electricians. This can impact any ISP or other business that wants to build or upgrade a building. Folks might be surprised to know that ISPs use electricians regularly when they add new equipment or build new outdoor huts, cabinets, and repeater sites.

A more subtle impact of an electrician shortage is that it makes it harder to train new electricians. Most new electricians begin their career by being an apprentice to a licensed electrician, and if those electricians are taking full-time jobs at data centers, they are no longer working with new apprentices. Admittedly, a shortage of electricians is mostly a local issue, but it can be a real concern to ISPs who are trying to quickly expand networks.

Of more consequence is the impact that rapid data center expansion is having on the global market for memory chips. The memory market is under huge stress. While this market has historically seen boom-and-bust cycles, this time is different.

AI servers require far more memory per system than consumer devices, so the AI build-out is snagging a disproportionate share of global chip capacity and creating shortages, as suppliers prioritize orders for hyperscalers and vendors specializing in AI servers. This sudden market shift means fewer memory chips for consumer devices. For now, that is triggering higher prices for smartphones, computers, automobiles, and other consumer electronics.

But industry experts say this is not a cyclical shortage caused by a temporary mismatch in supply and demand. This is instead a permanent reallocation of the world’s silicon wafer capacity. For decades, the companies that made DRAM and NAND Flash for smartphones and PCs were the primary drivers for chip production. Today, the huge demand from hyperscalers like Microsoft, Google, Meta, and Amazon has flipped the market. In the last year, we’ve seen companies like Samsung Electronics, SK Hynix, and Micron Technology leave the consumer chip market. These manufacturers, and others, have pivoted to making higher-margin enterprise-grade components. Every wafer allocated to Nvidia is no longer available for a smartphone or consumer laptop.

Manufacturers of consumer electronics live on thin margins. This includes vendors like TCL, Transsion, Realme, Xiaomi, Lenovo, Oppo, Vivo, Honor, and Huawei. There are already rumors of these companies discontinuing lines of electronics that they don’t think are marketable at higher prices. Anything they continue to make will be sold at a much higher price since memory is 15% to 20% of the bill of materials for most electronics. Worst are the stories leaking out from these vendors that they are being asked to pre-pay for multiple years of chips to even be included in the supply chain.

Telecom companies need a lot of memory devices. The electronics used in networks are packed with chips. A more subtle impact over time will be the many broadband customers who find themselves unable to afford a new computer or tablet. This is also going to puta huge crimp on firms who have been refurbishing computers to support low-income homes. We’re just starting to see the impact of this change today, and chip experts say the full impact won’t be felt by the end of this year.

AI Needs Quality Upload Speeds

The pandemic exposed a huge weakness in cable company networks when it became clear that their networks did not have enough upload capacity to support people working and schooling from home. That period when people struggled to work from home might have been the trigger to convince millions of people that fiber was superior to cable technology. The cable companies reacted quickly and goosed upload speeds to the range of 30-40 Mbps. Since then, they have slowly been upgrading to much faster upload speeds using mid-splits and DOCSIS 4.0.

A recent article from Ookla suggests that the same need for faster upload speeds might be coming for cellular networks due to the way that people are starting to use AI in daily life. The article provides some examples of ways we might use AI in the near future. A person might scan a menu in a restaurant, and AI can provide real-time feedback to estimate the calories in each dish or highlight foods that might trigger an allergic reaction. This would require quickly uploading a picture of the menu to provide quick feedback. That’s not a data-intensive transaction, but consider instead using AI to provide real-time feedback to somebody walking around in a foreign city. AI could translate signs and describe the nature of stores or shops as they come into view.

 

U.S. cellular companies have allocated the smallest percentage of bandwidth to upload compared to the major cell providers around the world. AT&T, T-Mobile, and Verizon have allocated between 6.6% and 7.1% of total bandwidth capacity to upload. In contrast, China Telecom and China Unicom have allocated over 16% of bandwidth to upload.

In writing this blog, I took a speed test on AT&T and got a speed of 381/11 Mbps on my cellphone. I note this is the fastest download speed I’ve ever received on AT&T, by a lot, and shows the impact of the AT&T’s recent introduction of the spectrum acquired from EchoStar. I took several other tests with similar results, and at my house, the upload speeds are only about 3% of total bandwidth.

American cellular carriers seem to be in a race to claim the fastest network for bragging rights, and this has led them to maximize download speeds to an extreme degree. I doubt that many people are complaining except for folks who are trying to stream video from their phone. When I swap my phone over to WiFi, the upload speed in my Charter connection is more than 10 times faster than the AT&T cellular upload connection.

The article points out that carriers have options to boost upload speeds. The one that is discussed the most in the article is to convert cellular networks to dynamic TDD (time division duplexing), which would allow the phone to assign bandwidth available to the phone to either download or upload, according to the immediate need.

But that fix alone wouldn’t solve the problem, because a carrier would need to beef up the entire network in the upload direction to handle larger volumes of uploaded data. There are other interesting limitations. For example, if a carrier uses shared spectrum like CBRS for uploading, then setting a faster upload would have to be coordinated with the other major users of the spectrum to synchronize the network clocks.

The Ookla article also demonstrates that handsets can be a limitation by showing the upload speeds that can be achieved on different generations of Samsung Galaxy phones. with lower upload capability on older phones.

The slow upload speed on my tests might be an anomaly, but before AT&T introduced the new spectrum, my upload speeds were rarely faster than 5 Mbps. Ookla says that median upload speeds in the second half of 2025 were 18 Mbps for AT&T, 21 Mbps for Verizon, and 27 Mbps for T-Mobile – all slow in comparison to fiber and upgraded cable technologies.

AI and BEAD Non-Deployment

Yesterday, President Trump signed an Executive Order that gives the federal government the sole authority to regulate AI. The EO provides three justifications for asserting federal authority.

United States AI companies must be free to innovate without cumbersome regulation.  But excessive State regulation thwarts this imperative.  First, State-by-State regulation by definition creates a patchwork of 50 different regulatory regimes that makes compliance more challenging, particularly for start-ups.  Second, State laws are increasingly responsible for requiring entities to embed ideological bias within models.  For example, a new Colorado law banning “algorithmic discrimination” may even force AI models to produce false results in order to avoid a “differential treatment or impact” on protected groups.  Third, State laws sometimes impermissibly regulate beyond State borders, impinging on interstate commerce.

Within 30 days, the U.S. Attorney General is required to establish an AI Litigation Task Force with the sole responsibility to challenge State AI Laws. It seems likely this will result in a series of federal lawsuits trying to preempt any State AI regulations.

Of concern to the broadband world is that the EO includes specific language that singles out BEAD grant funding. The EO says:

Within 90 days of the date of this order, the Secretary of Commerce, through the Assistant Secretary of Commerce for Communications and Information, shall issue a Policy Notice specifying the conditions under which States may be eligible for remaining funding under the Broadband Equity Access and Deployment (BEAD) Program that was saved through my Administration’s “Benefit of the Bargain” reforms, consistent with 47 U.S.C. 1702(e)-(f).  That Policy Notice must provide that States with onerous AI laws identified pursuant to section 4 of this order are ineligible for non-deployment funds, to the maximum extent allowed by Federal law.  The Policy Notice must also describe how a fragmented State regulatory landscape for AI threatens to undermine BEAD-funded deployments, the growth of AI applications reliant on high-speed networks, and BEAD’s mission of delivering universal, high-speed connectivity.

In case you are wondering the extent of State AI regulations, the following map comes from BCLP, which is accompanied by a description of existing and pending AI regulations, by State. As this map shows, over half of the States already have some form of AI regulation, and only three states don’t have existing or pending AI regulations.

It’s been clear that NTIA has been seeking a mechanism for denying non-deployment funds, which are the portion of the $42.5 billion in BEAD that is not being spent on infrastructure. Current estimates are that non-deployment funds will be more than $21 billion. These funds are supposed to be distributed to States under the IIJA legislation. This EO gives NTIA the grounds for denying non-deployment funds for a lot of States.

If you read through the existing AI regulations, most are of two types. Many States have enacted legislation that makes it illegal to use AI to defraud people, adding AI to laws that already forbid using emails, telephone calls, and other forms of communication. There are also States that have legislation that tries to protect citizens privacy. There are a few States with other restrictions.

State Broadband Offices in States that have AI regulations do not have the power to overturn AI regulations, and State legislatures must act if they want to cancel AI regulations to preserve non-deployment funds. That may be a futile effort, because my best guess is that we haven’t seen the end of attempts to deny non-deployment funds and that this is only the first volley. For what it’s worth, there is opposition to overturning State regulation of AI in Congress, but it would be extraordinary for this Congress to override an Executive Order with legislation.

Misaligned Priorities

We have several sets of broadband priorities at odds with each other in the country. The federal government is on a big push to move all transactions with the government to digital. The example that got a lot of press was when FEMA said it would only communicate with disaster victims through emails and its online portal. But government agencies across the board are pushing folks online to communicate.

The government is also clearly supporting an AI revolution where AI is supposed to revolutionize the way we work and live. According to federal government rhetoric, we are a little bit ahead of the Chinese in terms of AI development, and politicians seem to support the idea of doing whatever is needed to make sure that the U.S. wins the AI race.

At the same time that we are prioritizing AI and moving everything online, we seem to be deprioritizing broadband. NTIA cut the BEAD program funding in half to save money, at the expense of building new networks that would provide solid infrastructure for the next fifty years. The Administration outright killed the Digital Equity Act, which had the goal of getting computers into people’s hands and training them how to use them.

These goals are clearly at odds with each other. Consider the Digital Equity funding. There is a huge lost opportunity cost for not giving people the tools to enter the digital world that the government wants. What is the cost to society for people who aren’t given the tools to enter the digital world? Digital equity folks can rattle off tons of stories of folks who were given help with broadband who then went on to work in a tech field, start a business, become teachers, or otherwise thrive and contribute to society.

The disparity between these policies makes no sense to me. It looks to me like the Digital Equity Act was killed for the simple reason that it had the name ‘equity’ in its title. But digital equity never had any of the connotations that politicians classify as DEI. Digital Equity has always been an effort to help people learn more about and master computer technology and broadband. It makes no sense not to have digital equity as a goal if we want everybody to be able to use AI or communicate with the government online.

The BEAD grants were trimmed back for one reason only – to save money. The new Administration sent folks into every nook and cranny of the government to find ways to save money. On the surface, this isn’t a bad thing, and I have to think that many of the cuts to government expenses are good in the long run. But BEAD was never about spending money. BEAD is an infrastructure bill. There are reams of economic studies that show that spending money on infrastructure always returns more to the economy than the cost.

Just in my part of North Carolina, there are a bunch of counties where all of the BEAD awards went to satellite broadband. Set aside that Western North Carolina is mountainous and heavily wooded, and there will be homes that won’t be able to get adequate broadband from the satellites. Set aside that many of these counties have low overall incomes and many folks won’t be able to afford the satellite broadband.

The bigger issue is that building fiber is about a lot more than just bringing broadband to homes. When counties get a fiber network, they can start to get creative to find ways to leverage a new network to improve the local economy. Satellite broadband is finally starting to deliver the broadband that the average home needs to join the modern world. But satellite broadband isn’t going to support schools. It’s not going to enable a county to attract a new factory. Satellite is not going to enable a county to seek ways to improve cellular coverage. Fiber is the infrastructure needed to help the overall community, while satellite broadband just helps customers who can afford it.

I know this is probably coming across as another rant, but I know I’m right. BEAD and the Digital Equity Act were tools that could have made a big difference in rural communities. I’m pretty sure that by killing broadband programs that AI will not be coming to the rural counties in Western North Carolina. Folks here are going to fall through the cracks because they will be unable to communicate with FEMA and other government agencies. It feels like the government is making a conscious decision to exclude Western North Carolina. I don’t think this is deliberate, but unfortunately, by pursuing misaligned priorities, that’s exactly what is happening. The current government is making far too many decisions in a vacuum without considering the bigger picture.

Where is Congress?

One of the things that mystifies me this year is how many federal elected officials have disappeared in terms of supporting broadband. For example, there has been little talk of elected officials openly trying to stop NTIA from gutting the BEAD grant program. There’s no news about trying to force NTIA to go ahead and award grants from the Digital Equity Act. This may be happening behind the scenes, but there’s no big public news about supporting better broadband.

This is not intended as a political blog. I am truly puzzled by this big change. I fully understand that politics in DC is a mess right now. But the sudden indifference to broadband is a huge shift from just the recent past. Broadband has always been one of the few topics that has had bipartisan support from rural legislators, because they all knew that this was important to their constituents. Over the last five or ten years, I’ve heard from dozens of County governments who have said that the lack of good broadband was the number one issue for their constituents.

That message has always carried upward to federal legislators, particularly in the House of Representatives. Over the years, I’ve talked to a number of House members, or their staffs, who wanted to know more about the broadband market in their district. I can’t think of an ISP preparing to ask for a broadband grant that was unable to get a letter of support from their House member. And House members always turn up for ribbon cuttings for the launch of a new broadband network. Getting better broadband in rural communities has always been big local news, and elected officials have always participated in trying to get better broadband and celebrating it when it shows up.

The most visible political support for better broadband has come at the County level, and I know many County governments are confused and dismayed by the sudden retraction of the BEAD grant program. A lot of County Boards put a lot of effort into the BEAD process, because BEAD grant scoring rules in a lot of states rewarded ISPs that got real commitments from local politicians instead of the more common generic letter of support. County Boards were led to believe that they had some say in choosing the ISPs they wanted, since BEAD scoring gave extra points for such effort. A lot of County Boards even made tentative grant awards as matching for BEAD using local or ARPA funds, because that supported the local ISP they favored. Unfortunately, the sudden push to award BEAD to the lowest bidder means a lot of those local grants will go unused, and the ARPA funds will evaporate.

One of the most perplexing aspects of cutting BEAD funding is that the federal government is making a massive push for AI. Bringing AI into everyday life can only happen if everybody has access to good broadband. I’m truly perplexed how the government and the tech companies that are supporting them are for AI but not for spending the BEAD funding that is already in the pipeline.

A good compromise to support AI would be to let States have some or all of the non-deployment funds. It looks like that is going to be roughly half of the $45 billion allocated to BEAD. States could do a lot with that money if they were free to use it in ways they choose. It might make sense at this point to redistribute the non-deployment funds – the allocation of BEAD funds to States was based on faulty FCC maps and clearly gave some States too much money, and others not enough.

Lack of any vocal bipartisanship for broadband probably also doesn’t bode well for other needed legislation, like reforming the Universal Fund. I’m not hopeful that much will change with the BEAD funding or USF reform unless federal politicians speak up and remind NTIA and the administration that good broadband is essential for the American economy and a future that includes AI.