Reed's Ruminations: A Blog by Dan Reed

Upgrading the Future: House Science Committee Testimony

N.B. I am extremely grateful to the National Science Board Office (NSBO), and its dedicated and talented cadre of professional staff, who helped prepare my testimony.  These include, but are not limited to, Elizabeth Jeffers, Elise Lipkowitz, Nadine Lymn, Andrea Rambow, Amanda Vernon, and John Veysey.

In my role as chair of the U.S. National Science Board, which oversees the National Science Foundation (NSF), I recently testified to the U.S. House Committee on Science, Space, and Technology, along with the NSF Director, Sethuraman Panchanathan.  The hearing was entitled “An Overview of the National Science Foundation Budget Proposal for Fiscal Year 2024.”

These hearings typically follow a standard script, with initial statements from the committee chair and the ranking member.  You can read Chair Lucas’ statement here, including his important observation:

I strongly believe that the nation that leads in science and technology will shape the world order for the next century. I’d like that nation to be ours, and I’d like for emerging technologies to be developed with our values of transparency and fairness.

Opening statements from the committee chair and ranking member are then followed by oral statements from the witnesses, typically five minutes each, with a subsequent question and answer period.  There, each member of the committee is allotted five minutes to ask questions.  The fact that the hearing lasted two and a half hours, with thoughtful questions, speaks to the level of interest from both political parties. You can find the hearing details here, including the written statements from NSF Director Panchanathan and me (Download Reed Testimony), as well as a video of the oral statements and the subsequent question and answer period. 

I have spent decades talking to U.S. Senators, Representatives, Congressional staffers, and policy groups. (See Working the Hill.) Despite the rancor that often exists in the U.S. Congress, the House Science Committee continues to operate in a bipartisan way, strongly supporting investment in basic research.  I am grateful to both Chair Lucas and Ranking Member Lofgren for their strong support for the CHIPS and Science Act, in both the previous Congress and in this one.  This hearing was a continuation of that support.

My Written Testimony

My extended written testimony, entitled Upgrading the Future: Realizing Better Dreams, outlines the issues in greater detail, but the gist of my argument is simple. The United States is losing ground in science and engineering.  The country needs to increase investment in basic research – both people and leading-edge research infrastructure – expand the numbers and diversity of the STEM workforce, while continuing to be a magnet for global talent, and more aggressively translate research ideas into practice. The latter is the focus of the NSF’s new Technology, Innovation, and Partnerships (TIP) Directorate.

I also called for a more coherent, strategic plan for U.S. science and engineering, one that engages “all of government” in a collaborative, coherent way. To be clear, I was not calling for a top-down government plan, with all its attendant bureaucratic burdens, but rather one that lays out the key components our system needs and rewards systems thinking. I hope the upcoming Quadrennial Review, called for in the CHIPS and Science Act, moves the nation in this direction. I know the National Science Board is eager to partner with the Office of Science and Technology Policy (OSTP) on this project.

In my written testimony, I also drew an analogy between operating systems and our science and engineering ecosystem as an operating system. As we have become more complacent in our approach, we have resorted to patching “holes” in the code. As any computing expert knows, it is expedient in the short term, but in the long term, it is a risky strategy. Fixing one piece of code can break another, and a multitude of patches creates a brittle system, making it difficult to add desirable new features or respond to new threats.

Simply put, we are running a patched, 20^th century innovation ecosystem in a 21^st century world. We need to upgrade our entire operating system: expanding and diversifying the STEM talent pipeline, accelerating the delivery of research benefits, upgrading our research infrastructure, and elevating our commitment to basic research.

With that backdrop, what follows is my opening statement, as delivered, with a few additional annotations for context. Again, you can watch the video here.

My Opening Statement

Chairman Lucas, Ranking Member Lofgren, and Members of the Committee, thank you for the opportunity to speak with you as Chair of the National Science Board. Thank you as well for the CHIPS & Science Act and its bold blueprint for a brighter future.

One of my favorite science fiction authors, Neal Stephenson, once wrote, “if we want to create a better future, we need to start with better dreams.”  The CHIPS & Science Act is the stuff of better dreams, and it arrives at a critical juncture for our nation.  (See For Science and Society, The Future Begins with Better Dreams.)

To ensure future breakthroughs and innovations are “Made in America,” we must continue translating the Act’s vison into action – advancing scientific frontiers, developing STEM talent, expanding the geography of innovation, and delivering benefits to society.

Fully funding the Administration’s FY24 budget request will help make the vision a reality. But, we must to do more.

Let me be clear: U.S. leadership in science and technology is in peril. China is charging ahead, and absent further action, it is not a question of if but when the United States loses its STEM leadership, with deep consequences for our country.

Here’s why we must act – now.

FIRST. China’s announcement that it is ramping up government investment in basic research as “the only way to build a world scientific and technological power” both validates our strategy and highlights our challenge.  

It’s time for us to “double down” – expand investment in basic research and cutting edge scientific instruments – nationwide – and unleash American innovation.

SECOND. China continues to invest heavily in building its homegrown talent and now produces more STEM PhDs than the United States.

Meanwhile, we face a STEM talent crisis.  Students at all levels and all backgrounds are struggling in STEM – and COVID made it much worse. This crippling situation is even more acute for students from lower socioeconomic standing or underrepresented backgrounds. We are simply not producing enough STEM workers – at all levels – to meet the needs of a 21st century economy, and we are leaving millions of talented individuals behind.  (See Missing Talent, Missing Opportunities, and Losing Ground.)

For our STEM workforce to be representative of the U.S. population in 2030, the number of women must nearly double, Hispanic or Latinos must triple, Black or African Americans must more than double, and the number of American Indian or Alaska Natives must quadruple.

Forty-eight years ago, I was a poor, first-generation college student from the Arkansas Ozarks, studying computer science. I was fortunate to graduate debt free, thanks to academic scholarships, a Basic Educational Opportunity Grant (now Pell), and my paltry savings.  While I am extraordinarily grateful, I am alarmed that my educational path is no longer widely available. (See Transforming Lives via Public Higher Education.)

To grow our STEM talent base, we must do more to make higher education affordable and STEM graduate work more financially viable.  Hence, NSF’s FY24 budget request emphasizes broadening participation in STEM education and turning STEM career dreams into realities. (See Public Higher Education: A New Social Compact for Innovation.)

THIRD AND FINALLY. We must deliver research benefits to society.

NSF’s new Directorate for Technology, Innovation and Partnerships – TIP – will accelerate innovation by supporting use-inspired research and translation, building institutional and regional innovation capacity, and enhancing academic, government, and industry partnerships.

At the end of World War II, the compelling rationale for federal government research investment was to advance the national health, prosperity, and welfare; and to secure the national defense.

Almost eighty years later, it still is.

I dream historians – and our children and grandchildren – will mark now as the time we not only embraced better dreams, but put aside our differences, committed to our common goals, and acted – with compelling vision and unwavering resolution – to create a better future – for our country and for the world.

Thank you. I look forward to your questions.

Concluding Perspectives

While we in the United States continue to debate and dither, the time for concerted action grows ever shorter and the stakes grow ever higher. Having watched and learned, other countries have been investing heavily in their own innovation ecosystems, cultivating human talent, expanding their knowledge workforce, and constructing the advanced infrastructure needed to facilitate discovery, economic growth, and defense capabilities.  

It’s time for the United States to relearn and heed Vannevar Bush’s important lesson, delivered in his seminal essay, Science: The Endless Frontier, at the end of World War II:

… since health, well-being, and security are proper concerns of Government, scientific progress is, and must be, of vital interest to Government. Without scientific progress the national health would deteriorate; without scientific progress we could not hope for improvement in our standard of living or for an increased number of jobs for our citizens; and without scientific progress we could not have maintained our liberties against tyranny.

Science – it’s about a better future – for everyone.

On WSPRs, Geeks, and Luftballons

A few weeks ago, a large, high altitude balloon carrying a large payload of Chinese surveillance equipment was shot down by a U.S. Air Force F-22 Raptor using an AIM-9X Sidewinder missile.  The F-22 was scrambled from Langley Air Force Base in Virginia, the downed balloon landed offshore in South Carolina, and an intensive intelligence salvage operation began immediately.  This was a large, likely multiple ton, balloon with a substantial array of solar cells, navigational and directional capability, and electronic packages. Because my wife and I own a beach house in North Carolina, near the South Carolina border, I received local warnings to report and deliver any floating debris to the authorities.

In response, Secretary of State, Antony Blinken, canceled his planned trip to Beijing, and Sino-American geopolitical relations, already strained, sank even further, with public accusations and recriminations flying fast and furiously. The resulting consternation ignited political posturing by pundits across the political spectrum, which, in turn, led to strident calls for a public statement from the U.S. President.

How could this happen?  Why wasn’t the balloon detected earlier?  Why was it allowed to drift across the continental United States before being shot down? Members of the U.S. Congress wanted answers, and they got them via classified national security briefings. Meanwhile, NORAD increased its aerial and near-space surveillance, given heightened national awareness and concern about other objects in the sky. 

International law is clear that satellite overflights of a country (i.e., from space) are allowed.  Conversely, countries have the complete right to control the airspace over their territory, though international law is a bit uncertain about the rules at very high altitudes. As a practical matter, might makes right; if you can shoot it down over your territory, you have the right to do so. (Remember the history of the U-2 and SR-71 spy planes and note that the aerial photograph of the balloon above was taken from a U-2.) Relatedly, radio transmissions are controlled by International Telecommunications Union (ITU) treaty, as part of the United Nations.

Putting aside the very real and important national security questions behind the Chinese balloon and the calculations and mis-calculations that led to its path over the United States, the public hysteria and political ennui brought to mind the West German band Nena’s song, Neunundneunzig Luftballons (99 Air Balloons), which became an international hit in 1983 as an anti-war protest. It’s a poignant and melancholy Cold War story of a group of air balloons accidently triggering a 99-year global war, leaving only devastation in its wake.  (You can find it on YouTube, and the German lyrics are just as haunting today as they were forty years ago.)

            Original German                                          English Translation
            Neunundneunzig Jahre Krieg                        Ninety-nine Years of War
            Ließen keinen Platz für Sieger                       Left no room for winners
            Kriegsminister gibt's nicht mehr                    There is no longer a Minister of War
            Und auch keine Düsenflieger                        And also no jet planes
            Heute zieh' ich meine Runden                      Today I'm doing my rounds
            Seh' die Welt in Trümmern liegen                 See the world in ruins
            Hab' 'n Luftballon gefunden                          Found a balloon
            Denk' an dich und lass' ihn fliegen               Think of yourself and let him fly

An English version of the song, entitled 99 Red Balloons, was released shortly thereafter, with somewhat different lyrics, changed to scan with the original melody, and with a more American-centric story. However, many felt it lacked the punch and ennui of the German original, and it failed to achieve the same chart success. My always limited German language skills have atrophied with time, but I am inclined to agree.

But Wait, There Are More!

In the wake of the balloon shootdown, questions were asked; fingers were pointed; answers were demanded. Were there other, heretofore undetected, balloons floating over the United States and Canada? It turned out the answer was yes, and UFOs, literally – unidentified, flying objects – were back in the news. Soon, an unidentified balloon was reported to be drifting over Alaska and heading into the Canadian Yukon, followed by another over Lake Huron, the latter described as “small, metallic balloon with a tethered payload below it.”

After diplomatic consultations between the U.S. and Canada, both were shot also down by the U.S. Air Force. (Technical aside:  One should be impressed at NORAD’s surveillance capability, which can identify extremely small objects in the large airspace over North America.)

Were aliens in flying saucers involved?  Was North America under threat?  Was there a new Cold War “balloon gap,” analogous to the infamous “missile gap?” Inquiring minds wanted to know.

In a “you can’t make this stuff up” farce, a comedic mashup of fiction and reality – Dr. Strangelove (“You can’t fight in here.  This is the War Room!”), Neunundneunzig Luftballons, and national security fears – the news was soon filled with speculation that the United States may have used a $100M advanced fighter jet and $500K missile to shoot down a $250 amateur science project, a pico-balloon carrying a ~10 gram, extremely low power amateur “ham” radio transmitter. (See Hobby Club’s Missing Balloon Feared Shot Down By USAF.) 

The group potentially responsible for a balloon traversing America’s airspace is – wait for it – the Northern Illinois Bottlecap Balloon Brigade (NIBBB), a group of pico-balloon amateur radio  and science enthusiasts whose club was named in homage to the 2009 animated movie Up. If you are thinking these people hardly look like a threat, you would be right; the NIBBB notes that the club’s youngest member is only eleven years old.

One of their balloons, with amateur radio call sign K9YO-15 (balloons use the FCC call sign of the owner and a distinguishing number), was launched from Libertyville, Illinois on October 10^th, 2022 and had circumnavigated the Earth multiple times before the club lost contact with it.  The balloon’s last reported position was at 38,910 feet off the coast of Alaska on February 10^th, 2023.  The NOAA HYSPLIT wind model projected the balloon to be over the Yukon on February 11, 2023.

On that same day, NORAD downed an unidentified airborne object in the Yukon. The Pentagon issued a press release, and the Canadians searched briefly for the downed fragments but found nothing. 

However, given the small size of the balloon and the remote environment, it is unlikely that any debris will ever be found, leaving it uncertain if K9YO-15 was indeed the object shot down.  The NIBBB responded with equanimity as well, noting:

As has been widely reported, no part of the object shot down by the US Air Force jet over the Yukon territory has been recovered. Until that happens and that object is confirmed to be an identifiable pico-balloon, any assertions or claims that our balloon was involved in that incident are not supported by facts.

Even as the klieg lights of sudden and unexpected fame can be harsh and unforgiving, the NIBBB website shares the group’s passion for pico-balloons and amateur radio, including detailed construction details and a bill of materials.

That’s quite the story, with lots of jargon.  Let’s deconstruct this just a bit, starting with pico-balloons.

That’s My Science Project

What’s a pico-balloon, you say?  As the name suggests, it is a very small balloon, such as the Mylar kind filled with helium one might purchase from a hobby store or a florist shop. As anyone who has brought one to a birthday or graduation party knows, they can be purchased for a few tens of dollars, and are at most a few feet in diameter.

Most such balloons are soon discarded, though a few escape, intentionally or unintentionally, and float into the sky, never to be seen again. How high can they go?  The answer depends on the characteristics of the balloon material, gravity, and the Archimedes principle.  Scientifically, a balloon rises until it reaches neutral buoyancy (i.e., the balloon’s average density equals that of the surrounding air and the buoyant force balances gravity).

Simple Mylar party balloons are unlikely to rise to altitudes beyond 6-9 kilometers (20,000-30,000 feet), limited by either helium leakage or material stresses. In contrast, hardier balloons made of polyethylene or latex are neutrally buoyant up to fifteen kilometers (~50,000 feet). Bear in mind that these pico-balloons are much, much smaller than weather balloons ,and are often less than one meter in diameter. As such, their payload is also quite small, only 10+ grams (0.02 pounds for those of us in non-metric countries), and they are easily affected by weather, even frost.  For this reason, experienced pico-balloon aficionados prefer balloons that can rise to higher altitudes above most weather.

Under Federal Aviation Administration (FAA) 14 CFR Part 101, such small balloons can be launched by anyone, anywhere in the United States, without the need for FAA approval or reporting.  The only requirements are that the balloon’s payload weighs less than six pounds, and the balloon is launched in an unrestricted area. Given the low weight of pico-balloons, the jet stream can easily carry a balloon around the world, and there are many tracked and reported cases of such circumnavigation.

Why might anyone want to do this?

The truth is that people do such things because they are possible and they are cool. After all, humans did not build the Gossamer Albatross to usher in a new era of human powered aviation. They did it because building a human powered airplane that could cross the English Channel was the union of technological prowess and human inspiration; it was the epitome of cool. (See Science: It’s About the Wide-Eyed Wonder.)

Amateur radio pico-balloon enthusiasts are no different. Yes, the technical answer is that amateur “ham” radio enthusiasts often use pico-balloons to study the atmosphere and radio propagation characteristics, but the joy is in the construction and the operation. In such cases, the payload consists of a GPS tracker for location, a WSPR or APRS radio transmitter, an inexpensive micro-controller such as an Arduino to manage the transmitter, and a few solar cells to power everything. A complete package, including the balloon, can be assembled for at most a few hundred dollars.

What’s WSPR, you say?  It’s an acronym for Weak Signal Propagation Reporter (WSPR). The protocol was designed by Nobel Laureate and avid ham radio operator, Joe Taylor, and is intended to test propagation paths on the medium frequency (MF) and high frequency (HF) amateur radio bands using very low (and I do mean very low) power transmissions. A typical WSPR message contains the licensed amateur radio operator’s call sign, the Maidenhead grid locator of their rough location, and the transmitter power in dBm.  The entire message fits in 50 bits. Transmitting such a 50-bit WSPR message takes 110 seconds at 1.46 baud.

Okay, let’s now reconstruct the phrase “pico-balloon carrying an extremely low power amateur ham radio transmitter.”  There are several design options of varying price and sophistication, but here are the elements of a globe-circling amateur balloon, all within the expertise of amateurs and even (with supervision) high school science enthusiasts

• One small balloon (~$50 from Yokohama Balloon or ~$150 for a slightly more expensive research version from Scientific Balloons)
• Helium canister for balloon charging (~$75)
• Lightweight (~10 gram) payload, custom designed or purchased (e.g., ~$90 ZachTek WSPR controller or ~$130 QRPLabs LightAPRS-W tracker) h containing
  • GPS tracker
  • Microcontroller (e.g., Atmel 8-bit AVR microcontroller)
  • WSPR and/or ARPS transmitter
  • Solar cells for electrical power
• Dangling 0.005-inch wire dipole antenna of resonant length for the desired frequency (~33 feet for the 20m radio band)
• Open source software
  • Microcontroller configuration (call sign, Maidenhead locator, …)
  • Flight path prediction (e.g., NOAA HYSPLIT model or com)
  • Pico-balloon tracking collaborations (e.g., https://amateur.sondehub.org/ or http://lu7aa.org/wsprset.asp)

I would have been thrilled beyond measure to have had the opportunity to participate in such an activity as a student. Such projects are precisely the kind of experiential science education that can excite students, filling them with the realization that science is not a desiccated recitation of facts, but rather is an endeavor filled with passion and excitement. We need many more such experiences if we are to attract the millions of additional STEM workers this country needs to remain globally competitive. (See For Science and Society, The Future Begins with Better Dreams.)

My Geek Bona Fides

During the COVID-19 pandemic, my days (and often my nights) were consumed with managing the evolving crisis and ensuring the academic enterprise of the University of Utah continued with as few disruptions as possible.  Given the stress and uncertainty, I regularly counseled others to spend some time away from work doing something fulfilling and emotionally satisfying, lest they be overwhelmed by uncertain and rapidly changing events. (See The Simple Things Matter, Most of All Now.)  Knowing I should heed my own advice, I sought solace and distraction in my own happy place – science and technology. 

My first stop was my digital astrophotography hobby, which though always rewarding, proved uncertain, given the vagaries of Utah weather.  More practically, the late nights were incompatible with my day job due to sleep deprivation. After that, I turned to writing (See My Balm in Gilead) and, finally, to my tried and true friend, computing technology.

First, I developed an end-to-end suite of software for low-power, wireless sensors, using an inexpensive ESP32 microcontroller, LoRaWAN low power wireless transceivers, environmental sensors, and open source software tools.  All of this served as a citizen science complement to the larger SAGE project of which I am a part. (See Cyberinfrastructure: Lots of Room at the Edge.)

With the sensor suite complete, I studied for and passed the three FCC-mandated examinations – technician, general, and amateur extra – to become a fully licensed amateur radio operator.  The study materials gave me an opportunity to refresh my memories of electricity and magnetism (E&M) from undergraduate physics and also to delve more deeply into antenna and radio transceiver design.  The geek in me wished the American Radio Relay League (ARRL) examination materials had been a bit more mathematical, as many of the concepts are best understood via their roots in Maxwell’s equations.

All of which brings me back to pico-balloons and the “because we can” excitement.

First, armed with an ICOM-7300 radio and a trusty PC, I fired up WSJT-X and transmitted WSPR messages (radio callsign KK7EUJ, maidenhead grid locator DP40cp) at one milliwatt on the 20M HF band (14.0956 MHz) using my house’s gutter as an end-fed-random-wire (EFRW) antenna. Reflecting the effectiveness of WSPR encoding in a high noise, low power environment, these WSPR messages have been received by amateur stations across the entire continental United States, as shown at the right.

Second, I have written extensive code for the ESP32 and its smaller, 8 and 16 bit cousins, the ESP8266 and Arduino, the latter based on the Atmel 8-bit AVR microcontroller. Just as the proliferation of powerful, open source software has enabled construction of complex toolkits, so too have open source libraries of hardware components and powerful, open source design tools (e.g., KiCAD) facilitated design of inexpensive, custom hardware boards. As a complement to the wireless sensor library, I have also specified the design of custom printed circuit boards (PCBs), with GPS, sensors, and ESP32, which have been inexpensively fabricated in varying quantities by one of many fabrication firms.

Put another way, I realized I had followed the same path as my pico-balloon compatriots, using open source software and easily designed open source hardware to build new and interesting projects, ones that in the past would have been only within the reach of nation-states.

Coda

Sino-American relations remain deeply strained, and not just because of the surveillance balloon. The global battle for 21^st century geopolitical hegemony is underway, waged via economic competition, trade sanctions, diplomacy and political rhetoric, and military posturing. There seems little doubt that we are entering a new political era, one fraught with uncertainty.

In the midst of all this, I was on Capitol Hill just days after the downing of the Chinese balloon, discussing the importance of greater U.S. research investments and developing the domestic STEM workforce. I have been working with the National Science Board (NSB) on the intersection of dual use technologies, basic scientific research, economic competitiveness, and national security. In addition to a variety of meetings on Capitol Hill, I have invited the White House National Security Advisor, Jake Sullivan, to join the NSB in discussions.

Meanwhile, no alien UFOs have been confirmed, unless the definition of alien includes a loose affiliation of geeks (young and old), all fascinated by the combination of electronics, software development, atmospheric sciences, mathematics and coding theory, ionospheric electromagnetic propagation, and small balloons, and all wishing to share their scientific passion with others.  Could they be?  They might be. The signs and omens are all there. Yes, they are.  I am proud to say those are my people.

I am a geek, albeit an aging (and balding) left-handed, myopic one, some of those adjectives necessarily being implied by the geek nom de guerre.  I will try not to trigger an international incident by launching any pico-balloons of my own, but I will not rest in encouraging the passion of discovery in young minds.

Nena WSPRed rightly about the Luftballons:

        Man, who would have thought
        That it will come to this
        Because of ninety-nine balloons

Public Higher Education: A New Social Compact for Innovation

N.B. This is a long post, discussing both higher education futures and the economic challenges of STEM graduate education. 

As Lewis Carroll wrote in The Walrus and the Carpenter,

"The time has come," the Walrus said,
"To talk of many things:
Of shoes, and ships, and sealing wax -
Of cabbages and kings,
And why the sea is boiling hot,
And whether pigs have wings."

I speak, of course, of the complex, often eroding, and – to any outsider, seemingly nonsensical – financial models of most U.S. public higher education institutions. Some of this necessarily reflects their manifold and expanding mission, in varying parts social service agency, hospitality service operator, real estate developer, performing arts center, museum complex, sports franchise, and healthcare system, alongside the enduring and core missions of education, research, and community service.

Even as public colleges and universities struggle financially, the rising cost of higher education has put a college degree beyond the financial reach of an increasing fraction of students, with many increasingly questioning the value of a degree, as the post-World War II notion of the American Dream fades with it.  Meanwhile, the U.S. leadership of the global academic research enterprise is slipping away.

What happened?  How did college become unaffordable for so many? How did we lose touch with so many of society’s pressing needs? Where did we lose our way? More importantly, can we speak honestly and forthrightly about the problems and their solutions?

The Brave and Uncertain New World

Today’s colleges and universities are so much more than curricula and classrooms, social networks, and athletic teams. Universities all too often find themselves not just educating, but also helping impoverished students find a place to sleep overnight or even find a meal to eat.  They may be providing essential mental health services for students in crisis or addressing intimate partner violence and sexual assault.  With the public social safety net increasingly limited, universities are now often the helper of last resort, with resource demands and service expectations unknown a generation ago.

All of this is occurring amidst lagging state and federal support, and the limited backfill afforded by philanthropy. Consequently, university finances have become a byzantine web of cross-subsidies and interdependencies, with a surfeit of needs and a deficit of resources.  In turn, this has created competing agendas and incentives, an expanding backlog of deferred building maintenance, and increasing and worrisome addictions to rising tuition and cheap labor.  It’s a Faustian bargain at best, and the future of our public colleges and universities now hangs in the balance, measured in dreams deferred and opportunities lost.

At their best, U.S. public higher education institutions have been a transformative societal force, uplifting millions into higher socioeconomic classes via inculcated knowledge, skills, and acumen, and creating the new knowledge that has illuminated our world, transformed our economy, advanced our national security, and improved our health.  The generational impact of the G.I. Bill in educationally and economically uplifting millions of returning veterans is undeniable, as have been the benefits of the National Defense and Education Act’s investments in higher education and science, technology, engineering, and mathematics (STEM).

Pointedly, the cracks in the façade of U.S. higher education excellence are increasingly obvious and concerning, even as global competition in the knowledge economy intensifies. Although U.S. higher education institutions remain the envy of the world, other global universities, notably in China, are increasingly rivaling or eclipsing the U.S. in research citations and impact, as those countries invest in higher education.

Make no mistake, an educated and empowered populace of a community, a state, a region, or a nation remains the only truly renewable resource, the key to public health, national security, and economic competitiveness. A clarion call for reckoning now echoes loudly throughout our society, as the past speaks urgently to the present about the exigencies of future. It whispers about what was, what is, and for better or for worse, what might be. (See Academia:  Who We Are and Why It Matters and Innovation Imperiled: Golden Eggs No More.)

How Did This Happen?

Some of U.S. higher education’s challenges are a consequence of our fractured and litigious society, the pitched cultural battle between the left and right over perceived ideological purity, and, concomitantly, declining societal support for education as a universal public good rather than a perceived individual and private benefit. It’s been further exacerbated by the discordant, self-reinforcing echo chambers and ever shrinking sound bites that now pass for public debate. (See From Broadcast to Narrowcast: Finding Signal in the Noise and Contemplative Reflection and Instantaneous Communication.)

Nor are we in higher education without culpability, having been complicit in protecting the ivory tower, slow in responding to societal needs, and smug in our defense of often hidebound traditions and byzantine bureaucracy. There are also substantive ways to reduce costs judiciously and prune non-essential services, while retaining the core educational mission, and these should be pursued assiduously. However, such changes are unlikely to substantively change the cost structure of higher education.  Even with automation and expanded online classes, education remains a labor-intensive endeavor.

In public higher education institutions, these pernicious realities have led to a decline in inflation-adjusted state support, compensated only in part by rising student tuition.  The latter, of course, has made college increasingly unaffordable and precipitated a student debt crisis.  Concurrently, colleges have found themselves in the crosshairs of the national political battle, critiqued by some for indoctrinating students with liberal values. It's a wicked problem, a catastrophe of potentially existential proportions.  (See On Catastrophes and Rebooting the Planet.)

Not surprisingly, the result has been increased questioning of the cultural and economic value of higher education, even among those predisposed to believe its enduring importance.  As an example, a recent survey by the Association of American Universities showed that roughly half the respondents felt a college degree from one of the leading research universities in the United States was valuable but not worth the cost. Without doubt, a college degree remains an economically and culturally wise choice for many, when coupled with appropriate career counseling, but we have not done nearly enough to address rising education costs, respond to market demands, meet students where they are, and prepare them for a lifetime of learning and periodic retraining. (See Higher Education in the 21^st Century.)

Conventional wisdom says the first step in any addiction treatment and recovery program is personal honesty – admitting that one has a problem, then taking determined action. Alas, I fear we in higher education are still in denial. (See Pinball, War Games, and Universities.)

Our addiction takes many forms. On the staff side, it includes an increasing number of poorly paid adjunct lecturers, often euphemistically called career-line faculty, who teach a large fraction of our undergraduates. It includes the underpaid support staff who maintain our buildings and grounds, and others who advise and support students.  It also includes many of our faculty outside the professional schools, particularly in the liberal arts. All struggle to afford housing and find child care, and some are forced to avail themselves of federal assistance programs. Even more troubling are those contract teachers whose work is spread across multiple institutions; many lack health insurance.

On the student side, it includes well-documented undergraduate tuition and affordability challenges, as well as mental health support, and food and housing insecurities. Convolved with these is an ugly truth – fewer than half of all college students will graduate within four years, leaving them saddled with debt, but lacking the intellectual “union card” – a college degree – that creates differential market value.  

Even there, the near-term financial return on investment (ROI) differs markedly based on the type of degree earned and the institution from which it was received. Meanwhile, high paying, intellectually satisfying jobs in the skilled technical workforce go unfilled, ones for which training is much less expensive and time consuming than a college degree.

It also encompasses an issue little discussed outside academia, namely the sad financial state of graduate students and post-doctoral research associates (post-docs).  Finally, it is reflected in our focus on academic rankings and prestige, often at the expense of the communities and states who need our help.

Before delving into these dynamics, it’s worth remembering how much the world of higher education has changed in my professional lifetime.  Herewith is a personal story, which I will use to illustrate shifting academic finances and affordability.

A Personal Tale

Sometime after the K-T extinction, when mammals began filling the newly vacated ecological niches, I started college.  The year was 1975, Saigon had just fallen, Nixon had resigned the year before, and platform shoes and polyester leisure suits were all the rage. I will begrudgingly admit to owning the former, but I vehemently deny ever owning or wearing the latter – I may have been poor, but I did have some sartorial principles.

As a first-generation college student and poor scholarship kid from the Arkansas Ozarks, I was able to attend and graduate from college without debt, thanks to a combination of academic scholarships (National Merit Scholarship Program and university), Basic Educational Opportunity Grants (now Pell) for low income students, and the small amount of money I could save working at summer jobs; my parents were unable to help me financially. I lived like a pauper, with a social life limited to free lectures and cultural events, and my wallet was empty, save for my student ID card and a driver’s license.  The latter was of about the same practical value as a launch authorization for a Mars mission, as I lacked both a working heavy lift launch vehicle and an automobile.

In the process, I became intimately acquainted with student dormitory housing and the culinary delights of fixed menu cafeteria food – eat what was served or go hungry.  Finally armed with an aging and cantankerous Ford Galaxie 500 – the green monster – I lived in a trailer park mobile home for a bit, where I learned to appreciate a variety of nutritional price performers, including “four for a dollar” Kraft mac-and-cheese (Pro tip: It’s better with milk, even powered milk, but it’s edible even with water). Nevertheless, in my mind, I was rich beyond imagining. (See Transforming Lives via Public Higher Education.)

With an undergraduate degree in computer science now within reach, I told my father that I had higher ambitions. I planned to attend graduate school, rather than get a job, one that would have paid substantially more than my father earned as a sawmill laborer.  In response, he looked at me sadly and said, “Son, you know we can’t ever afford that.”  I just smiled and said, “Dad, here’s the amazing thing. It won’t cost anything.  In fact, they will pay me to go to graduate school!” Already poor, I did not fully realize how many more years of deferred financial reward I had unknowingly embraced. Free can prove extraordinarily expensive.

When I began graduate school in January 1979, annual inflation in the U.S. was hovering near 14 percent, proving the truth of the hoary guns versus butter economic adage and highlighting the hidden costs of the Vietnam War, which begat the failed Whip Inflation Now campaign. Because I completed college early, I began graduate school “off cycle” in January, with a fellowship that did not start until September.  That spring, I was a teaching assistant (TA), lecturing on the joys of FORTRAN programming to engineering students at 7:30AM as I watched the sun rise over the snow-covered Purdue campus.

As a TA, my monthly stipend was a princely $368/month, which covered food and a room in a graduate student residence hall, with perhaps ~$20/month of discretionary money. I felt rich when my graduate fellowship began that fall, for it was a munificent $400/month, about $1,400/month in today’s inflation adjusted dollars.  Alas, with no cost-of-living adjustment (COLA), the $400/month stipend was quickly eroded by inflation, moving me from poor to poorer. Then my old car died, leaving me an involuntary pedestrian, constrained to live in on-campus graduate student housing until I completed my Ph.D. in 1983.

Graduate school now behind me, I drove away to my new academic job in a cheap, subcompact car, financed on the strength of my future income, all my worldly possessions inside – some old clothes, a battered popcorn popper, a cheap guitar, some books and graduate school notes, and a bicycle. After my arrival at my new home, I began my career as an assistant professor, living in a one bedroom apartment.

Lest the de minimis nature of my material state remain in any doubt, my little apartment was soon burglarized and ransacked, but nothing taken, for I lacked anything worth stealing. I’d have been annoyed if my bicycle has been stolen, but outraged if my graduate school books and notes had been pillaged. The only other, potentially more devastating insult would have been if the burglar had left a $20 bill and a note, saying “You need this more than I do.”

Shifting Student Economics

What is the moral of my little story?  First, the 1970s and 1980s economics of higher education afforded me an opportunity to realize my dreams.  It was not easy, but like thousands of other students, I completed both undergraduate and graduate degrees and emerged with no debt. For that, I will be forever grateful.

Alas, it is increasingly clear that my path is no longer easily replicable. Undergraduate tuition at a state university in the 1970s was just a few hundred dollars per year, an amount covered by any reasonable combination of an academic scholarship, BEOG/Pell grant, summer job savings from a minimum wage job, and for families wealthier than mine, some parental contributions.

Today, undergraduate tuition at most public institutions exceeds $10,000, with comparable student room and board costs. Put another way, a four-year college degree at a public university can cost substantially more than $100,000, while the maximum Pell grant is under $7,000, and just recently increased by $500.  Further confounding the economics, four years was once the nominal time to complete a college degree. Today, we most often report six year graduation rates, as students work full or part-time jobs, delaying graduation as they struggle to cover college costs.

As for graduate school, I never paid graduate student tuition – I had no idea what it even was. It was once common to waive tuition for students employed as TAs or research assistants (RAs), and it was included in fellowship awards.  Such graduate tuition waivers are now less common, particularly in STEM disciplines, as universities seek to recover tuition from federal research grants and contracts.  In turn, this has exacerbated pressures on the federal research enterprise – more on that later.

Meanwhile, as data from the National Science Board and the National Center for Science and Engineering Statistics (NCSES) show, the percentage of graduate STEM degrees awarded to international students continues to rise.  The fact that the U.S. is a magnet for global talent is a tremendous competitive advantage. 

Not only do many of those international students pay tuition, many remain in the U.S. after school and contribute greatly to the vitality and economic competitiveness of the country. A recent National Bureau of Economic Research study highlighted the disproportionate positive contributions of immigrants on the entrepreneurial ecosystem.  But this begs an important question – where did the domestic STEM graduate students go?

Graduate School: Yes, It Is Different

If one is pursuing a terminal professional degree (e.g., an MS or MBA), graduate school may seem like an in-depth continuation of the undergraduate experience, filled with classes and projects, albeit focused on one’s chosen discipline. For those aspiring to a research Ph.D., the experience is strikingly different. (See Research: When There Are No Answers in the Back of the Book.)

Many Ph.D. students serve as teaching assistants (TA), research assistants (RA), or graduate fellows (i.e., they are supported by the university, either with institutional funds (TAs) or research grant or contract funds (RAs)). In STEM fields, RA support is common, though rare in the humanities. One is not quite staff, and certainly not faculty, but neither is one fully a student. 

TAs assist faculty, hold office hours, grade homework sets, lead discussion sessions, and, in some cases, as I did, even have full responsibility for a class. RAs and fellows are research apprentices, working in faculty-led research laboratories, often, but not always on projects related to their research, hoping to collect the requisite data and experimental validation needed to craft a Ph.D. dissertation. Post-docs labor in a different netherworld, hoping to build a resume worthy of an academic faculty position.

In a perfect world, all of these young scholars would be mentored, nurtured, supported, and treated as future members of the profession, whether as future faculty, industry or laboratory technical staff, or government experts.  In many cases, they are, but all too often they are treated – implicitly or explicitly – as cheap labor whose primary function is to further the career ambitions of the faculty advisor or to satisfy the institutional needs for inexpensive instructors. Amidst this class system, TAs and RAs battle the existential angst of graduate school, uncertain when – or if – they will graduate, dependent on the continuing good will of their supervisor and advisor for intellectual and emotional support, letters of recommendation, and certifications of academic progress.

Graduate school will always be intellectually challenging – pushing the boundaries of the known is never easy.  As Jimmy Dugan said in A League of Their Own, “It’s supposed to be hard.  If it wasn’t hard, everyone would do it. The hard is what makes it great.”

However, intellectually difficult, graduate education can and should be a nurturing and supportive environment.  Indeed, there are very good ideas for how to improve this process, reemphasizing the role of mentorship and respect, reducing the power inequities between advisor and advisee, and better educating students to translate their knowledge into practical impact. (See, for example, the recent NAESM study, Graduate STEM Education for the 21^st Century.) Sadly, there can be strong faculty resistance and institutional disincentives to implement these changes.

More Than Genteel Poverty

The stress of graduate school is made more difficult by the vow of poverty taken by graduate students.  Yes, one is paid to be a TA, RA, or fellow, but the compensation is low, and increasingly inadequate to meet the basic cost of living needs in many American communities. For graduate students with dependents, those from traditionally under-represented groups, and those with little intergenerational wealth, the financial challenges can seem bleak, especially with annual inflation in the U.S. now above six percent.  (See Missing Talent, Missing Opportunities, and Losing Ground.)

In response, graduate students are increasingly turning to strikes and unionization to increase their bargaining power. The recent strike by University of California graduate students and post-docs – the largest in U.S. history – brought salary and working environment issues into stark relief. The newly negotiated contract raises the minimum salary for nine months to just over $34.5K, with a slightly higher rate of $36.5K at UC-Berkeley, UCSF, and UCLA, in recognition of the high cost of living there. Contrast that with MIT’s Living Wage Calculator, which estimates a “living wage” for one adult without children at $50,463 in Alameda County, the home of UC-Berkeley.

Why has it been so challenging to raise graduate student salaries?  In part, it is related to the financial exigencies facing universities, but there is a complicating factor related to federally-funded research. Federal research grant sizes have also not kept pace with inflation, and raising RA stipends would necessarily reduce the number of funded RAs.  Moreover, it would create strong equity pressures to raise TA salaries, which are funded directly by universities, to comparable levels.

Again, let me illustrate the challenges with a personal example. My very first National Science Foundation (NSF) grant in 1984 was for ~$55K (~$155K in current dollars), enough to cover two months of summer salary for me for three years, fund travel and supplies, and cover the salary of one graduate student for three years, the standard NSF grant period.  Mind you, my nine-month academic salary was $30K, the then common starting salary for assistant professors in computer science. Moreover, the university waived tuition for funded research students, meaning tuition was not charged to the grant, and the cost of health benefits was much lower.

If one examines the history of NSF funding from the Merit Review Digest,, the median award size in 2020 was ~$150K, and it has remained largely stagnant since 2011. Any simple analysis shows that this number is now inadequate to support activities similar to those I pursued in 1984. The stipend, benefits, and tuition for a single graduate student over three years easily exceed this amount, leaving no funds for summer faculty salary, supplies, or research travel. This means faculty must scramble to write more proposals – to multiple funding agencies – to create a portfolio of awards with differing objectives, timescales, and funding levels to maintain a research laboratory.

To be sure, money – current or deferred – has never been – nor should it be – the primary reason to attend graduate school or become an academic. However, a stark and growing financial disparity between private sector remuneration and graduate student compensation can be an insurmountable social and economic barrier to many. 

Houston, We Have a Problem

We’ve now talked of many things.  The sea, though not boiling hot, is experiencing global warming due to climate change.  As for pigs, absent a lower gravity environment and deeply unethical genetic engineering that substantially exceeds our current capabilities, they really can’t fly.  But, U.S. higher education and its research enterprise face very real problems.  Let’s summarize the challenging calculus.

Public Higher Education Dynamics

• Society is increasingly questioning the value proposition of public higher education, both as a valuable return on family investment in undergraduate education and as a force for good – culturally and practically – in our cities, our states, and our country.
• The upcoming (2025) demographic cliff – a decline in the number of U.S. high school graduates – will create even greater competition for students and their tuition dollars. Some smaller institutions, particularly private ones, will be forced to close their doors, and many public institutions will be forced to retrench and eliminate programs.
• The student debt crisis is real, with the burden falling disproportionately on lower income, first generation, and traditionally under-represented students.
• We have made a college degree the default social objective for all high school graduates, even as high paying jobs in the technical trades go unfilled.
• Public colleges and universities continue to raise undergraduate tuition to offset declining state support. In most states, these form an “x curve,” with tuition now a larger contributor to college and university budgets than state appropriations.
• Financial pressures have triggered a shift toward lower paid, contingent faculty, who are hired only to teach, rather than conduct a mixture of teaching, research, and service.
• Aspirations are good, but mission creep has led many of our community colleges to aspire to become four-year institutions, our four-year institutions aspire to become research universities, and our research universities to aspire to flagship leader status. An appropriately differentiated — and interconnected — educational system serves different educational niches.

I believe it is time for us to collaboratively and thoughtfully rethink the social compact surrounding public higher education.  This includes defining the 21st century knowledge economy version of the public land-grant university, which was originally charged to

… teach such branches of learning as are related to agriculture and the mechanics arts, … in order to promote the liberal and practical education of the industrial classes in the several pursuits and professions in life.

It means admitting that we in higher education do not have all the answers, or even know all the right questions. It means a true conversation – of give and take and mutual respect – where all parties put aside preconceived notions about the best path forward. Concomitantly, it means using our intellectual expertise to deliver perceived and real value to our communities, regions, and states, addressing the social, economic, technical, and cultural issues that now bedevil our society.

It also means embracing differentiated higher education roles, recognizing that each type of institution is a critical part of our higher education ecosystem. Finally, it means better career counseling about expected outcomes from differing courses of study, including celebrating the skilled technical workforce (aka, the skilled technical trades) and importance of preparing graduate students for non-academic careers. Only by doing so will we change the rhetoric about higher education’s utility and the importance of investing in our children’s and our country’s futures.

Now, let’s turn to the other issue – the challenges facing our STEM domains. (N.B. Equally important, partially overlapping but also partially orthogonal, issues face the liberal arts and humanities.  Both are important, as transdisciplinary education is critical to addressing our societal challenges.)  (See Renaissance Teams: Reifying the School at Athens.)

STEM Research Dynamics

• Completing a STEM Ph.D. can take five to seven years, sometimes more, at low pay and long hours, with continued support dependent on the research supervisor’s ability to secure competitive research funding.
• In many STEM disciplines, four or more years of post-doctoral study are then expected, also at low pay and with long hours, if one intends to seek an academic position. During this process, all too often we fail to provide adequate mentoring and career development, as most post-docs take industry positions, rather than academic ones.
• Academic faculty salaries are often not competitive with those in the private sector, and one may never recoup the five to ten years of “lost income” during one’s graduate and post-graduate education.
• Except in growing STEM disciplines (e.g., computing), the competition for tenure track faculty positions is increasingly intense. Moreover, with few universities growing, the availability of such positions depends on faculty retirements and domain fit. This is exacerbated by current faculty deferring retirement and many tenure track positions being replaced with lower paid adjunct instructional faculty.
• U.S. leadership in STEM, once unquestioned, is increasingly in doubt as China builds world-class research universities and develops critical technologies such as AI, quantum computing, and biotechnology.
• Because the growth in federal research funding has not kept pace with workforce growth and inflationary pressures, the probability that a research proposal will be funded is now lower than in the past. Many NSF programs now have proposal success rates in the teens, and median award sizes have been stagnant for a decade.  Similarly, the mean age to receive a National Institutes of Health (NIH) R01 grant (the academic gold standard in biomedicine) is now well over 40 years.
• Universities conduct research to build prestige, attract high-quality faculty, and explore the future. However, in a narrow sense, research loses money, as the direct and indirect costs (overhead) from federal grants and contracts rarely cover the true costs of research facilities, libraries and serials, research computing, pre-award and post-award services, research compliance, and a host of other services. The deficit is paid by institutional funds.

The career opportunities enabled by graduate degrees vary widely across disciplines. In some, a Ph.D. is de rigueur; in others, it is optional, though the value is intellectually and economically demonstrable; and in some cases, it may not be worth the opportunity cost of delaying entry to the workforce.  In all cases, the personal financial costs of graduate education continue to grow.

In my own discipline, computer science, the opportunities and the resources available in industry often make it more attractive for domestic students to either skip graduate school entirely, or leave with an M.S. rather than a Ph.D. In part, these opportunities explain the relative dearth of domestic Ph.D. students in computing.  Conversely, graduate school provides an intellectual, cultural, and visa entry point (via optional practical training and H-1B visas) for international students.

Finally, we must never forget that humans pursue science and engineering research for the joy it brings, just as do artists and musicians. Discovery illuminates the great unknown and satisfies the human curiosity older than history. (See Science: It’s About the Wide-Eyed Wonder and The (Scientific) Good News.) In so doing, it provides the insights and ideas that advance our society, improve human health, and secure our economic future. 

It’s not always about the money, but neither should the lack of money close the door on talent. Nevertheless, the invisible hand of economics is real, tempered by market realities, career ambitions, and credential expectations. We must recognize and address the economic challenges that bedevil our domestic graduate students and our faculty, which limit both their numbers and their diversity.  It’s ethically and practically important.

Coda

There can be no doubt that our current higher education and research enterprise has both great strengths, but also deep flaws. Higher education is too expensive for too many, and too many of our public colleges and universities are struggling financially. Simultaneously, our research ecosystem is stressed and falling behind our global competitors.

As a country, I believe we must put aside our differences – at least long enough to recognize our shared fears, hopes, and dreams – and rethink our approaches and renew the social compact. Only then can we build the social consensus needed to invest in the future appropriately. In so doing, we can create an environment that not only redresses current university funding deficits and academic dynamics, but also responds to societal needs and expectations.  In the process, we can and must build a culture shaped by what Lincoln called the “better angels of our nature.” (See For Science and Society, the Future Begins with Better Dreams.)

Computing Futures: Technical, Economic, and Geopolitical Challenges Ahead

In February 2022, I wrote an essay entitled American Competitiveness: IT and HPC Futures – Follow the Money and the Talent, where I outlined a series of challenges facing the state of the global information technology ecosystem. These spanned the shifting locus of innovation and economic power, from those companies we have traditionally called computing vendors (Intel/AMD, HPE, and IBM) to the hyperscalers and social media giants (Amazon, Facebook, Google, and Microsoft, along with their Asian siblings), the influence of smartphone vendors (Apple and Samsung), and the artificial intelligence juggernaut driving them all.

I also discussed the end of Dennard scaling and the slowing/end of Moore’s Law, both of which have ended the “free lunch” cycle of faster and cheaper microprocessors every two years. Then, within this framework, I outlined the technical, economic, and cultural challenges bedeviling leading edge high-performance computing, where ever larger machines deliver increasingly small fractions of the peak hardware performance. (It is often the case that complex, multidisciplinary applications achieve single digit percentages of the peak hardware performance.) I ended by discussing the sad state of U.S. STEM education and the implications of these trends for U.S. global competitiveness and national security.

As I had hoped, the essay generated a great deal of federal policy and vendor introspection, as well as considerable Internet chatter. 

Based on many discussions and helpful feedback from several readers – including Doug Burger, Jim Larus, Tony Hey, and John Shalf, I asked my friends and colleagues, Dennis Gannon and Jack Dongarra (this year’s Turing Award winner), to help me formalize my blog observations in a March 2022 arXiv paper, entitled Reinventing High Performance Computing: Challenges and Opportunities. There, we expanded the high-performance computing history lesson and expounded on the rise of cloud computing and deep learning, something Dennis and I were privileged to see first-hand at Microsoft.  Later in March, I participated in an insideHPC podcast, where I discussed several of our observations.

We followed this with a standing room only discussion in November at SC22, with additional panel members Dorian Arnold (Emory), Jack Dongarra, Torsten Hoefler (ETH Zurich), and Kathy Yelick (UC-Berkeley). (For those of you who attended SC22 or are ACM/IEEE members, you can view the panel discussion here.)  Then in January 2023, John Shalf organized a panel discussion with Kathy, Neil Thompson, and me at the Exascale Computing Project (ECP) annual meeting, where we discussed the topic as well.

Finally, the Communications of the ACM has published an abridged version of our arXiv paper in the February 2023 issue.  The latest version of the article is entitled HPC Forecast: Cloudy and Uncertain, and you can listen to Dennis, Jack, and me discuss the maxims and their implications at this link.

A Few Maxims

In both our arXiv paper and our Communications of the ACM paper,  we outlined some maxims that we believe should guide any consideration of advanced computing futures.

• Maxim One: Semiconductor constraints dictate new approaches. The “free lunch” of lower cost, higher performance transistors via Dennard scaling and faster processors via Moore’s Law is at an end. (Most do not realize that the inflection point for transistor costs occurred near the 20 nanometer feature size.  Since then, chip feature sizes have continued to decline, with more transistors per unit area, but the per transistor costs began to rise due to yield challenges and rising fabrication facility costs.) This minimax problem of maximizing chip yields, minimizing costs, and maximizing performance has driven the adoption of the chiplet model, mixing and matching multiple chips via interconnects such as UCIe, which opens new opportunities for scientific computing optimization and domain-specific accelerators.

• Maxim Two: End-to-end hardware/software co-design is essential. Given current semiconductor constraints, substantially increased system performance will require more intentional end-to-end co-design, from device physics to applications. (See Advanced Computing: Integrative Thinking for the Future, for a discussion of end-to-end design.) There is also an important corollary about changes to procurement models at scale.
• Maxim Three: Prototyping at scale is needed to test new ideas. Across multiple axes – semiconductors, chiplets, AI hardware, cloud innovations – the computing system is now in great flux.
• Maxim Four: The space of leading edge HPC applications is far broader now than in the past. Domains dominated by complex optimization problems and time-dependent partial differential equations will always matter, but other areas of advanced computing are of high and growing importance, enabled by advances in deep learning. The AI revolution is also driving the hardware ecosystem in new ways.
• Maxim Five. Cloud economics and deep learning have changed the supply chain ecosystem. The largest HPC systems are now dwarfed by the scale of commercial cloud infrastructure deployments.
• Maxim Six. The societal implications of technical issues really matter, and a diverse and inclusive domestic and global workforce is critical. This speaks to our challenges around workforce development and attracting and retaining talent individuals.

Chips Shortages and Globalization

While we were sounding the alarm about the technical and economic challenges in advanced computing, the public debate on what became the U.S. Chips and Science Act continued apace. Ultimately, the act appropriated money to help “on shore” a larger fraction of U.S. semiconductor manufacturing and authorized, but did not fully fund, major increases in funding for the National Science Foundation (NSF) and the Department of Energy’s (DOE) Office of Science. (See Working the Hill for some perspectives on where my research interests and public policy roles – as chair of the National Science Board, which oversees NSF, and chair DOE’s Advanced Scientific Computing Advisory Committee (ASCAC) – intersect.)

How did we find ourselves in this situation, you might ask?  Simply put, via a series of locally rational and arguably, even short-term optimal decisions, companies focused on quarterly profits and shareholder value. They outsourced chip fabrication and device manufacturing, benefiting from lower offshore costs, and focused on design and integration of the chips into high margin products.  Meanwhile, Intel, the dominant U.S. semiconductor fabricator, struggled with the transition to extreme ultraviolet lithography (EUV) and ceded global semiconductor leadership to Taiwan Semiconductor Manufacturing Company (TSMC). Nevertheless, all seemed well until two things upset the global supply chain – COVID-19 with disruptions in global supply chains and the rising economic tensions and national security concerns involving China. Now, economic uncertainties, declining tech sector profits, and corporate layoffs have further exacerbated the challenges.

The pressure to maximize short-term gains obscured the longer term need to control the means of production, exposing the risks of what my friend Tren Griffin calls the power of wholesale transfer pricing (i.e., the bargaining power of company A that supplies a unique product XYZ to Company B which may enable company A to take the profits of company B by increasing the wholesale price of XYZ.) Put another way, it is very attractive to be a fabless semiconductor company, but only when your semiconductor fabrication partner is both inexpensive and highly reliable.

As Warren Buffet famously said about the hidden risks some companies take, “Only when the tide goes out do you discover who’s been swimming naked.” The U.S. has been swimming naked for quite a while, dependent on international sources for key semiconductor components. If you have tried to buy anything containing embedded electronics – and almost everything these days does, from automobiles to greeting cards – then you know there has been a global chip shortage, manifest in rising product costs and even scarcity.  Nor has high-performance computing been immune from these shortages. To complete construction of the Frontier supercomputer at Oak Ridge National Laboratory, the U.S. Department of Energy had to involve national security priorities to secure access to scarce chips.

How big is the U.S. dependence on Asian semiconductor foundries? As the chart shows, Taiwan has about 65 percent of the global market, with TSMC holding the largest fraction of this, followed by UMC, PSMC, and VIS.  Korea (Samsung) has about 18 percent, China (SMIC and HHGrace) has about 5 percent, and the United States is in the “other” category – the final 12 percent – along with Global Foundries.

Why don’t we just build more fabrication plants, you say? We are, stimulated by Chips and Science Act subsidies, but building new facilities takes time and serious money.  To put this in perspective, a state-of-the-art fabrication line for high-end microprocessors and graphics accelerators (GPUs) now costs at least $10B and is rising. Moreover, much of the chip shortage is not at the bleeding edge, but for older chip technologies (e.g., for car electronics) that are fabricated on fully depreciated semiconductor fabrication lines (e.g., 45-28 nanometer feature sizes).  Vendors rarely build new lines for older technologies, they simply extract cash from existing fabrication lines, and until the recent economic downturn, there was unprecedented demand for devices normally fabricated on these lines.  (The New York Times has a reasonable summary of the global semiconductor dynamics.)

National Security

Semiconductors have become more than just an economic engine; they are now critical to any country’s national security and global aspirations.  Indeed, semiconductor manufacturing and supply chain control have become proxies in the ongoing battle for global hegemony.  In a bit – but only a bit – of hyperbole, at the World Economic Forum event in Davos, Intel’s Pat Gelsinger recently called chips the new oil of geopolitics. The U.S. Department of Commerce recently tightened export controls around China, both to purchase and to manufacture certain high-end semiconductor chips used in military applications. (See the October 2022 Department of Commerce declaration.)

Meanwhile, the new U.S. National Security Strategy focuses on protecting semiconductor supply chains and continued advances in semiconductors, advanced computing, and next-generation communications, among others. The U.S. National Security Advisor, Jake Sullivan, has highlighted four pillars of U.S. strategy:

• Investing in the science and technology ecosystem,
• Nurturing top STEM talent,
• Protecting technology advantages, and
• Deepening and integrating alliances and partnerships.

All these goals emphasize three families of technologies, including (a) biotechnologies and biomanufacturing, (b) clean energy, and (c) computing-related technologies, including microelectronics, quantum information systems, and artificial intelligence.

Nor are these issues of concern only to the U.S.  Korean leaders have raised similar concerns. Whatever one might wish to be true, it is increasingly clear the world is moving rapidly into a post-globalization era, with the increasingly likelihood of a 21^st century non-kinetic Cold War, albeit one with very different dynamics than the one of the 1950s to 1980s.  The global economy – and especially that of the U.S. and China – are deeply intertwined in ways unthinkable during the days of the standoff between the Warsaw Pact and NATO. Meanwhile, the Russian invasion of Ukraine has highlighted the uncertain future of Taiwan and western dependence on TSMC.

Coda

How might the U.S. respond in a positive way?  The short answer is we must out-compete – invest more and continue to expand an ecosystem that attracts and retains the best and brightest talent the planet can produce, both domestic (The Missing Millions) and international. I have sought to summarize some of these objectives in another series of essays.  As I wrote in Not Alone: Leaders in Focus, let’s dream big and bright about a larger, more diverse and inclusive science and engineering ecosystem that continues to be a magnet for global talent. (See For Science and Society, the Future Begins with Better Dreams.)

Working the Hill

Creating bills, negotiating amendments, and shepherding them to passage as U.S. laws is a messy process, with a cacophony of voices, each with differing perspectives, all seeking to be heard and appreciated.  To observers – and often to the participants – it can be slow, confusing, frustrating, and convoluted, but that is how participatory democracy works.  It is a collective fumbling toward majority views on what are almost always complex issues with many discordant voices and few, if any, unambiguously obvious solutions.

As one program examiner from the Office of Management and Budget (OMB) once noted to me, when I was chairing a review of the Networking and Information Technology Research and Development (NITRD) program as a member of the U.S. President’s Council of Advisors on Science and Technology (PCAST),

Dan, I think you know this, but I feel obligated to say it.  Rarely does anyone come to Washington and say, “I’m dumb and I have too much money.  Can you help me fix my problem?”

There are always more ideas than there is money to support them. Consequently, successful legislation is by necessity a compromise, a selective and sometimes partisan amalgam of ideas, priorities, and projects.  Rarely does any individual or group get everything they want, even when a bill becomes law on a party line vote.

Sadly, the notion that compromises are often key to progress is increasingly anathema to many, as social media echo chambers spread misinformation and harden emotional battle lines, posing increased risk to the political process. (See From Broadcast to Narrowcast: Finding Signal in the Noise.) Fact-based reasoning matters, especially as it relates to matters of science and engineering. (See Nature Cannot Be Fooled.)

 It seems increasingly old-fashioned, but I still believe thoughtful advocates can and will disagree on one issue while also finding common ground on another. Put another way, one can have respectful disagreements while not demonizing the other participant; demonization forecloses the possibility of future collaborations. Most of the time, most people are trying to do what they think is right; it is important that we cling tightly to belief in innate goodness, as a shared commitment to a better future.

Nor does the legislative process always yield the best outcome, as what constitutes “best” lies in the eye of the beholder.  This too is part of the great and ongoing experiment of collective debate and shared governance. Unanimity is rare, for as E. B. White once wrote, “Democracy is the recurrent suspicion that more than half of the people are right more than half the time.” Therein lies one of the bedrocks of democracy -- the consent of the governed; accepting the current will of the majority, even as one continues to disagree and respectfully advocate for reconsideration and change.

There are, of course, always exceptions to the notion of compromise.  There are occasions when any leader, whether elected or appointed, recognizes that a decision of moral and ethical consequence is at hand.  Are the public statements mere rhetorical devices and political slogans, or are they derived from deeply held, carefully researched, and wisely considered beliefs and principles.

One is sometimes called to do the right thing, even when the personal and political costs are high. At these times, leaders are tested; some rise to the occasion, and some do not. We all instinctively know true leadership when we see it.

In these cases, not only does the weight of history rest heavy on one’s shoulders, but that most fierce critic and constituent – the one who stares back at you in the mirror each morning – will judge you each day for the rest of your life.  That is why it is always important, but especially important in these circumstances, to think deeply and deliberatively, seek facts and context, and welcome perspectives from both those who agree and those who disagree.

CHIPS and Science

I was reminded of all of this as I watched the U.S. Congress struggle this summer to reconcile differing versions of bills that would enhance U.S. competitiveness by funding – among many other things – new semiconductor manufacturing facilities and semiconductor workforce development.  What began as the Endless Frontier Act, a political nod to Vannevar Bush’s famous memorandum, then became the U.S. Innovation and Competition Act (USICA), then the COMPETES Act, the Bipartisan Innovation Act, CHIPS-Plus and, ultimately, the CHIPS and Science Act.

The CHIPS and Science Act appropriated $52 billion in subsidies for semiconductor manufacturing and associated research and development.  The overwhelming majority of that money is to help the U.S. semiconductor industry “reshore” semiconductor fabrication at new and upgraded facilities.  I will editorialize just a bit here and note that the U.S. lost its semiconductor manufacturing dominance in part due to a series of business decisions, each of which made near-term sense, but were not consistent with long-term national interests.

As passed, the CHIPS and Science Act also authorizes major budget increases for the National Science Foundation (NSF), targeting expanded investments in basic research, growing and diversifying the Science, Technology, Engineering, and Mathematics (STEM) workforce, and creating the Technology, Innovation, and Partnerships (TIP) Directorate for technology transfer. (See Missing Talent, Missing Opportunities, and Losing Ground.) As the current chair of the Presidentially-appointed National Science Board (NSB), which oversees the NSF, I have been heavily involved in discussions surrounding what ultimately became the CHIPS and Science Act and the still evolving implementation of its provisions.

Despite the inevitable compromises, the CHIPS and Science Act is a huge win for the science and engineering enterprise.  As science fiction writer, Neal Stephenson, once noted, “If we want to create a better future, we need to start with better dreams.”  The CHIPS and Science Act is a big dream, and we owe a debt of gratitude to all who helped make it a reality.

Making the Sausage

In my varied academic roles over the years, and as leader of the Technology Policy Group (TSG) while at Microsoft, I have worked the Hill and testified to both the U.S. House and the Senate on a variety of issues, mostly related to high-performance computing and how it advances scientific discovery and economic competitiveness, but also on topics as varied as weather forecasting, clouds (the compute kind), digital privacy, wireless spectrum policy, and transnational data flows.  (See, NITRD: Come, Let Us Reason Together and It is Fall: There Must Be Clouds, for example.) These hearings have a certain rhythm, with prepared written statements, a brief oral presentation by each witness, then questions from the committee or subcommittee members.  

Hearings are part of the sausage making that is the political and legislative process, and as important as they are, hearings are just the public theatre that complement the many private discussions with industry, academia, think tanks, policy and lobbying groups, Congressional staffers, and members of Congress. These one-on-one and small group meetings, policy briefings, and commissions, along with protests (though rarely in scientific circles) are how constituents, and we in science and engineering are definitely constituents, make their voices heard.

Most of these meetings are with Congressional staff members, who take notes and collect messages for their principals. Bear in mind there are staff who work directly for members of Congress and those who work for Congressional committees, each of which has responsibility for some aspect of government. Such Congressional committee staffers are often long-time experts and serve as the Congress’ institutional memory.

For example, the House Committee on Science, Space, and Technology has exclusive jurisdiction over the National Science Foundation (NSF), the National Aeronautics and Space Administration (NASA), the National Institute of Standards and Technology (NIST), and the White House Office of Science and Technology Policy (OSTP).   However, although the House Committee on Science, Space, and Technology has jurisdiction over NSF, the agency’s appropriations are ultimately determined by the House and Senate Committees on Commerce, Justice, Science, and Related Agencies.

The fact that NSF is but one among several agencies addressed by these committees speaks to both its relatively small size (~$8.8 billion) and the competing priorities the committees must juggle (e.g., NOAA and weather forecasting, NASA, community policing, civil rights, and economic development).  The 302(b) mark given to each subcommittee, in turn derived from the 302(a) mark given to the House and Senate Appropriations Committees, determines the total amount of money – the spending cap – available for allocation.  Hence, there are always difficult choices to be made.

In between all this, lies the U.S. President’s proposed budget, which outlines the administration’s priorities. It is developed by the Office of Management and Budget, working with the executive branch agencies.  These priorities may – or may not – be those of Congress, particularly if different parties control the White House and the Congress.  That’s why an old Washington aphorism applies, “The President proposes and Congress disposes.”  Congress gets the final word on both budget authorizations and appropriations.

NSF and the CHIPS and Science Act

Distinguishing budget authorizations (“you can and/or you shall”), as defined from appropriations (“take this money and do this”) is important.  You cannot spend an authorization; it and a few dollars will buy you a cup of coffee.

There is a long history of increased authorizations for NSF, including its proposed budget doubling in 2007. Alas, the poetry of authorization yearnings has rarely translated into the appropriations prose of major budget increases. Research discoveries satisfy our unrequited curiosity, what astronomer Edwin Hubble called “the urge older than history,” but all too often, transient exigencies – wars, disaster remediation, pandemic response – and the realities of discretionary spending limits have precluded major budget increases for NSF.

One experienced Washington hand once explained Federal budget politics by noting that the U.S. government is a social service agency (Medicare, Medicare, Social Security, and other services) with a police force (national defense) and some hobbies.  Science is one of the hobbies – a really important one, but, like hobbies, it is discretionary.

All of which is why it is important to make the case for appropriations in person and with “leave behind” evidence and talking points. This fall, I did just that, spending two days on Capitol Hill, meeting with Representatives, Senators, and staff members about the importance of increased investment in basic research, expanding and diversifying the STEM workforce – the Missing Millions of the NSB’s Vision 2030 – and funding the TIP Directorate’s Regional Innovation Engines (RIEs), which will broaden the geography of innovation by creating technology transfer engines across the United States.

Specifically, we asked Congress to

• Fund the National Science Foundation’s (NSF) FY23 budget at $11.89 billion, as authorized by the CHIPS and Science Act, and concomitantly
• Support the RIEs, lest the opportunity to broaden the geography of innovation be lost
• Embrace the rising global competitiveness challenges, as noted in the CHIPS and Science Act, and support growth of the STEM workforce.

It was a start.  There is more work to be done – more visits, more conversations, and more evidence.

Coda

It was great to be back, seeing the hustle and bustle, the young staffers, the excited interns, the wise and experienced veterans, the earnest conversations, the clock ticking down on votes, even the concerned protesters exercising their rights.  It’s democracy in action.  I’ve missed seeing it.

Before 9/11 and certainly before January 6^th, one could enter the U.S. Capitol and the U.S. House and Senate buildings with little fanfare, as everyone who visited was expected to exhibit a certain level of decorum and respect.  It is a much more complicated process today, with more security and in many cases, an escort required on entry.  However, once inside the buildings – Cannon, Longworth, and Rayburn (House) and Russell, Dirksen, and Hart (Senate) and even the U.S. Capitol – one is largely left to navigate on their own.  It is not at all uncommon to pass unaccompanied Representatives and Senators in the hallways. After all, this was and is the heart of American democracy.

At the end of two days of Hill visits, my compatriots and I retired to Bullfeathers, a bar in the Capitol Hill neighborhood, to assess and recap our Hill discussions.  The irony that I had once been trapped there on 9/11 for several hours was not lost on me. (See I Could Smell the Smoke.)

For Science and Society, The Future Begins with Better Dreams

Working with National Science Board (NSB) staff, I recently authored a post for Rafael Bras' new discussion group, Not Alone: Leaders in Focus. As the tagline says, it is a place for "unfiltered perspectives on global issues by research and academic leaders."  You can read my recent post, For Science and Society, the Future Begins with Better Dreams."

In addition, I co-authored an a science funding advocacy op-ed in The Hill with Dario Gil (SVP for Research at IBM).

Memos, Janitors, Teams, and Innovation

Memoranda (memos) are the detritus of organizational processes, frequently written only so the writer can point to the message as prima facia evidence of purported communication, concern, action, or principles: mistakes were made, committees have been appointed, or actions will be taken. Some are internal, revealing the jaded reality behind polished exteriors.  Some are leaked, some are belatedly discovered in the archives of history, and still others are published and promulgated widely.

Not surprisingly, whether in academia, industry, or government, cynicism often runs high among any memo’s target audiences, for such memos can be turgid and meandering, written and edited by committees. Consequently, most are little read, and even fewer are remembered, much in the spirit of the old Communist adage, “They pretend to pay us, and we pretend to work.”  The exceptions prove the rule, exposing existential concerns and broader truths, part of an authentic dialog.

Memo Filtering

Long ago, I was privileged to serve as head of the computer science department at the University of Illinois, one of the country’s great academic teams, famous for its work in high-performance computing.  In those pre-Internet days, a veritable blizzard of paper memoranda came to me from the offices of the college dean and the university administration.  After scanning them, I discarded the vast majority, forwarding only a few of the most relevant to the paper mailboxes of my colleagues, who still good-naturedly complained about unnecessary deforestation.  Reminding them that I filtered the chaff and that they were only seeing the “good stuff,” I bided my time.

Eventually yet another memo from the campus reached my desk regarding campus pouring rights and the new campus contract for either Coca-Cola or Pepsi products. Though practically important to the campus for its financial contributions to scholarship, it was largely irrelevant to most.  Seizing my opportunity for friendly parody, I wrote an extended, tongue-in-cheek cover memo encouraging my colleagues to engage in shared governance and participatory democracy by attending the upcoming campus town hall and expressing their views on one of the great and important issues of our day – the relative merits of Coca-Cola and Pepsi.  I’d made my point, which elicited some laughter and friendly discussion at our next faculty meeting.

Including the Janitor

One of the most famous memos in all of computing, perhaps rivaled only by Bill Gates’ 1995 Internet Tidal Wave memo, was written by IBM President Thomas Watson Jr. on August 28, 1963.  The entire memo, directed to the company’s senior leadership prior to an upcoming retreat at Jenny Lake, New York, is reproduced below:

August 28, 1963

Last week CDC had a press conference during which they officially announced their 6600 system. I understand that in the laboratory developing this system there are only 34 people, “including the janitor.” Of these, 14 are engineers and 4 are programmers, and only one has a Ph. D., a relatively junior programmer. To the outsider, the laboratory appeared to be cost conscious, hard working and highly motivated.

Contrasting this modest effort with our own vast development activities, I fail to understand why we have lost our industry leadership position by letting someone else offer the world’s most powerful computer. At Jenny Lake, I think top priority should be given to a discussion as to what we are doing wrong and how we should go about changing it immediately.

T. J. Watson, Jr.

Condensed to its essence, Watson asks how a pipsqueak little company could develop a world-leading technological marvel that eclipsed the efforts of a Fortune 500 corporation. After all, IBM’s slogan was and is simply THINK.  I pondered similar things myself while at Microsoft, as we in the senior leadership looked at the Apple iPad and iPhone with similar dismay during our executive retreats. 

At the time of Watson’s memo, IBM was the world’s largest computing company, deep in the development of the IBM System/360, a multibillion dollar, “bet the company” plan to develop an entire product line of instruction set compatible computers.  The successful result reshaped the computing industry and ensured IBM’s dominance for the next twenty years.  In the years prior, IBM had developed the IBM 7030 Stretch, the fastest computer in the world until the CDC 6600 appeared, a leadership role IBM would not reclaim until almost forty years later.

On being told of Watson’s memo, CDC’s Seymour Cray, the architect of the CDC 6600 and widely known in computing for eschewing bureaucracy, reportedly quipped, “It seems like Mr. Watson has answered his own question.”  Cray, universally lauded as the father of supercomputing, later designed the eponymously named Cray-1, once again the world’s fastest computer. However, this is not a story about supercomputers, but about culture, shared responsibility, and respect.

As Watson noted, IBM had vast development activities and a large cadre of visionary and talented people, luminaries like Fran Allen, John Backus, Fred Brooks, John Cocke, and Gene Amdahl, several of whom, along with Seymour Cray, I have known over the years, and others I was blessed to count as colleagues. Where IBM struggled, by its own admission, was not talent; it was bureaucracy and culture.  Some of its best minds left the company to pursue their own ideas, and others were relieved of responsibility when they challenged the orthodoxy. 

As an example, John Cocke’s own, legendary work on the IBM 801, which long predated the reduced instruction set computer (RISC) revolution, took years to see the light of day. In a story that has been told and retold, and embellished with retelling until it has become the stuff of legend, John returned from a meeting with management, frustrated and disgruntled by their lack of vision.  He gathered his team and reputedly said, “<EXPLETIVE> them!  We are going to build the best <EXPLETIVE> computer the world has ever seen.”

With IBM’s tolerance and unencumbered by market niches and product lines, they did just that, though the reality was more nuanced and complicated than this story might suggest. It was a powerful combination -- John’s vision and the quality of his team, each of whom knew this was the intellectual opportunity of a lifetime. Because the project lacked the blessing and imprimatur of any product line,  they named it the 801, after the offices where they worked. (The New York Times has a great story about John Cocke and his maverick ways.) 

Team Cultures

Mr. Watson’s deprecating allusion to the janitor is telling.  As Tolstoy remarked in Anna Karenina, “Happy families are all alike; every unhappy family is unhappy in its own way.”  Dysfunctional teams can and do fail in a variety of ways. Conversely, great teams, regardless of the mission and objective, share several common attributes. 

First, the team members believe passionately in a greater goal and mission, as something worthy of shared commitment and mutual sacrifice. (“We are going to build the fastest computer the world has ever seen.”) Individually and collectively, they are united in their desire to do something that will be remembered, something others may believe improbable or impossible; they want to make a difference. Whether that vision originates from the team’s leader or from one of its members matters not.

Second, a successful team is greater than its parts.  Each team member recognizes and understands they each have a critical role, and if they fail individually, the team will fail collectively.  Hence, each depends on the others to not only meet their commitments but to exceed them. They will disappoint or fail their partners.  Everything and everyone matter, and everyone knows it, including the janitor.

It is also why being “lean and mean” is often so critical to success.  With no distractions, no tangential activities, leadership shielding them from bureaucratic intrusions, and just enough resources to complete the task – but no more – everyone is highly motivated and keenly focused on the goal and the competition. The fierce urgency of now motivates one and all. Venture capitalists know this well; overfunding a startup is as deadly as critically underfunding.

Third, leaders know the buck stops with them.  They bear the ultimate responsibility for the team’s overall performance, and they will accept blame if necessary to protect the team.  Steve Jobs often talked about the difference between the janitor’s responsibility and that of a leader. As he put it, if the janitor cannot empty the trash because the locks have been changed and the janitor lacks a key, that is understandable. Conversely, a leader must anticipate such problems and clear the path for the team. Locked doors cannot be a barrier.

The Spider-Man rule applies to leaders: With great power comes great responsibility. Sometimes, that responsibility means providing the vision and strategy that animates the team, other times it means holding the bureaucracy at bay.  Or, it can mean getting new keys made over the weekend, so the janitor can empty the trash without interruption. In other cases, it can mean buying and personally delivering pizza to fuel the inevitable late nights, or sweeping the floors if the janitor is ill.  Good leaders do what needs to be done, instinctively and unhesitatingly, without drama.

Finally, and most importantly, in a great team, everyone is respected for their differential skills and responsibilities.  The janitor matters, just as does the Ph.D., the engineer, and the software developer, albeit each with different roles and expectations.  As M. L. King, Jr. once said, “Whatever your life’s work is, do it well. A man should do his job so well that the living, the dead, and the unborn could do it no better.”  There is virtue and honor in being a great janitor, just as there is in being a computer architect.

Never forget, the way leaders treat each individual sets the organization’s tone. In the cultures of great teams, the members feel empowered to generate ideas, offer suggestions, question authority, and take the initiative. If the leader respects and values the janitor, treating him or her as an equal and valued partner, the other team members will know, intuitively and immediately, that they too are valued, and they will respond accordingly. This is the working definition of servant leadership.  

Feed the Mind, Thank the Janitor

Inertia is endemic to large organizations, where bureaucratic processes can hamper or even strangle innovation.  In response, wise leaders often create alternative cultures, research institutes, and skunkworks that let radical innovation flourish unimpeded by memoranda, processes, and bureaucracy.  It is precisely this dichotomy – those who work within established paradigms and those who are compelled to create new ones – that Thomas Kuhn explored in his book, The Structure of Scientific Revolutions. Both are needed, and effective cultures support and nourish both.

Unfettered exploration by passionate individuals and committed teams is the fundamental premise of academic freedom and exploratory research, whether in universities or in industry.  As Vannevar Bush perspicuously noted in his famous memo to President Roosevelt at the end of World War II, “Scientific progress on a broad front results from the free play of free intellects, working on subjects of their own choice, in the manner dictated by their curiosity for exploration of the unknown.” Such individuals and teams rarely read memos; trust me on this.

And the next time you see a janitor, or anyone else engaged in a seemingly thankless but important task, stop, smile, and say thank you; they too make a difference. The word will spread, far faster and far more effectively than via any memo.

Missing Talent, Missing Opportunities, and Losing Ground

Growing businesses and economies depend critically on a pool of educated people, leading edge ideas and technologies, robust investment, and growing market opportunities.  Whether advanced manufacturing, information technology, energy, medicine and healthcare, or a host of domains, in an increasingly competitive global economy, continued innovation, fueled by a judicious combination of private and public sector actions, is key to success. Implicit in this model is an educational system that attracts and trains the talented individuals needed by businesses and a research and development environment that continually catalyzes new products and services. 

Every two years, the National Science Board (NSB) assesses the state of the U.S. research and development enterprise, issuing a Congressionally mandated report on science, technology, engineering, and mathematics (STEM) education and U.S. investment in research and development. How is the U.S. faring in education and innovation relative to the rest of the world?  Simply put, not nearly as well as in the past.

The NSB is a group of twenty-four scientific experts, appointed by the U.S. President for six-year terms.  The NSB has two statutory responsibilities. First, it provides strategic, policy, and budgetary oversight for the National Science Foundation.  In its second role, most relevant here, it provides advice to the White House and Congress on science and technology issues, STEM education, and workforce concerns. I am honored to serve as a member of the NSB.

This year’s NSB report, The State of U.S. Science and Engineering 2022, shows that other countries have learned from the historical successes of the U.S., and they are dramatically increasing their investment in STEM education and research and development.  Even as U.S. intellectual output has grown, China has surpassed the U.S. on several key metrics, including the number of science and engineering research publications and the number of issued patents, and it is at near parity on the number of science and engineering doctoral degrees awarded. Overall, the U.S. still leads, but not by nearly as large a margin as in the past.

Let’s begin by examining STEM workers in the U.S., which now comprise 23% of the total U.S. workforce. These STEM workers include a wide spectrum of occupations, ranging from scientists, engineers, and healthcare workers through technicians and workers in production and construction.  About 16 million of these STEM workers hold a bachelor’s degree or higher, with the remaining 20 million comprising the skilled technical workforce (i.e., with associate degrees, certificates, and other technical training).  Reflecting their criticality to the U.S. economy, throughout the COVID-19 pandemic, these high paying STEM jobs were resilient, with STEM workers employed at a higher rate than their non-STEM counterparts – at all education levels.

Sadly, U.S. STEM education leaves many students behind in our K-12 schools. U.S. 15-year-olds rank between Hungary and Turkey in national mathematics proficiency, behind the large majority of industrialized economies. However, the challenges begin even earlier. On average, U.S. 8^th grade students of all ethnicities who are eligible for free or reduced school lunches fail to achieve mathematics proficiency, but the disparity is most pronounced for Black, Hispanic, Native Hawaiian or Pacific Islander, and American Indian or Alaska Native students.  If a student is fortunate to be in a higher socioeconomic status (SES), the mathematics news is not much better.

Moreover, K-12 STEM teachers with fewer than 3 years of experience are more prevalent at schools with high-minority or high-poverty populations, perpetuating generational inequities. Nor does this inequitable state of affairs improve in college and post-graduate studies, where women and individuals of color are substantially underrepresented in STEM fields. 

Why is it culturally acceptable in the U.S. to say, “I’m bad at math and science” when few would willingly confess to semi-literacy by saying “I don’t read very well.”  Unless we reward and remunerate STEM teachers, celebrate student STEM accomplishments, and value scientific and mathematics literacy, the U.S. is likely to continue falling further behind its global competitors.

In a previous life, I was a Microsoft corporate officer, and met I often with government leaders from around the world. In my travels, I consistently emphasized the importance of a country’s people as its only truly renewable resource. I also reminded leaders that talent comes from everywhere – from all ethnic groups and socioeconomic strata.  Wise leaders nurture and cultivate talent everywhere, without exception.  The U.S. needs to heed that lesson.

If the U.S. STEM workforce were truly representative of the U.S. population, millions of additional skilled workers would be inventing the future and supercharging the U.S. economy. These “missing millions” are a huge lost opportunity for the U.S., all at a time when an increasing fraction of jobs require critical thinking, technical knowledge, and mathematical reasoning.

To meet a portion of this shortfall, the U.S., has historically imported talent, particularly at advanced levels. In my own discipline, computer science, over 60 percent of Ph.D.’s. working in the U.S. are foreign born. Many of them came here because U.S. research universities remain the envy of the world, attracting some of the world’s best and brightest.

However, the numbers of immigrant graduate students have declined recently due to COVID-19, shifting U.S. immigration policies, and rising opportunities in their home countries. U.S. academic preeminence is also increasingly at risk, as state support for public higher education declines, tuition rises, and competition for limited research funding intensifies, making it harder for domestic students to attend college, continue graduate studies, and pursue faculty careers.

Continued U.S. scientific and engineering leadership, with its manifest economic benefits, is not a birthright.  Every business person instinctively knows the truth of the aphorism from Intel’s Andrew Grove:

Success breeds complacency. Complacency breeds failure. Only the paranoid survive.

Leadership is built on sustained and strategic investment in education and critical workforce skills, as well as a collaborative industry and government research and development investment engine that fuels new businesses. The U.S. is now at a critical inflection point.  It is time to up our game by investing in our people and infrastructure to compete more effectively in the 21^st century.

American Competitiveness: IT and HPC Futures – Follow the Money and the Talent

N.B.  A more formal version of these arguments is posted on arXiv (https://arxiv.org/abs/2203.02544), co-authored with Dennis Gannon and Jack Dongarra.

This is a long post, containing perspectives that may be controversial to some.  That's okay; it is why we have lively intellectual debates.

Charles Dickens’ A Tale of Two Cities opens with this line:

IT was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness, it was the epoch of belief, it was the epoch of incredulity, it was the season of light, it was the season of darkness, it was the spring of hope, it was the winter of despair.

It’s an apt metaphor for the state of information technology and the state of U.S. competitiveness in computing and science, technology, engineering, and mathematics (STEM).  In some ways, the hegemony of U.S. IT has never been greater, with Apple and the cloud companies (Google, Microsoft, Amazon, and Facebook) dominating the Nasdaq with trillion dollar (and more) market capitalizations and reshaping the very definitions of computing with global services, deep learning, and advanced AI.

Conversely, the semiconductor shortage has highlighted the economic and national security risks from over-dependence on off-shore fabrication, and the relative influence of traditional computing companies on the IT ecosystem has waned. Meanwhile, the future of U.S. leadership in bleeding edge scientific computing (aka supercomputing) is at a critical crossroads.

 Finally, while the global competition for STEM talent in general and IT talent in particular has never been more intense, the U.S. is substantially underperforming relative to other countries in STEM education.  Let’s take a look at how all of this has happened.  Remember, follow the money and the talent.

Remembering the Past

Let’s start by playing a parlor game. Name the first computer company that comes to mind.  If you are an old timer, you might be inclined to mention IBM, Amdahl, and the BUNCH (Burroughs, UNIVAC, NCR, CDC, and Honeywell).  Although IBM once bestrode the computing world like a colossus, today, even its relevance has faded. Indeed, few now remember that IBM was once locked in a bitter struggle with the U.S. Department of Justice for monopolistic practices under anti-trust laws.  As for the BUNCH, they are either gone from the computer business or niche players at best. Later, others – Compaq, Digital Equipment Corporation (DEC), and SGI – came and went.

As an aside, I learned to program on an IBM System 360/50, wrote lots of code on a DEC PDP-11/45 for my first paid software development job, ported code from a GE 600 timesharing system, and used a CDC 6500 and 6600 during graduate school. I programmed in FORTRAN, COBOL, PASCAL, LISP, and PI/I (in addition to many more recent languages), and I’ve toggled absolute binary into the front panel. I’m also old enough to remember when one could do Mead and Conway VLSI design with colored pencils.  I also used a slide rule, while walking barefoot in the snow to school, uphill (both ways), but I digress. (See You Might Be a Computing Old Timer If …)

There’s a sad, parallel tale (pun intended) in the land of high-performance computing, where the cemetery is filled with departed denizens, some large, some small, but all now permanent members of the Dead Computer Society. Whether by misjudging markets or technologies, each in in its own way proved the truth of the old adage that the best way to make a small fortune in supercomputing is by starting with a large fortune, then shipping one or two generations of product. Sic transit gloria mundi SGI, Cray, and a host of others. (See HPC: Making a Small Fortune.) 

More to the point, the list of high-end supercomputing players keeps getting shorter.  Cray is gone, now part of HPE; and both IBM and Intel are struggling to right themselves, each for different reasons.  In the United States, this leaves HPE as the lone high-end HPC integrator in the United States.  Time will tell if Nvidia, with its recent acquisitions, steps into this space, though its proposed acquisition of ARM seems unlikely.

Meanwhile, the information technology ecosystem and the locus of innovation and global leadership have shifted dramatically. There are deep and important implications for the U.S.

Contemplating the Present

If you are Gen Z or Gen Alpha, the world begins and ends with Apple, Google, and Samsung smartphones and the cloud services behind them.  All of which brings us to the FAANG companies (Meta née Facebook, Amazon, Apple, Netflix, and Alphabet née Google) plus Microsoft and their Asian BAT (Baidu, Alibaba, and Tencent) counterparts.  These companies are economically exothermic in ways perhaps unprecedented since the days of the Standard Oil monopoly.  Doubt me?  Look at their market capitalizations  and free cash flow relative to traditional computer companies. (As I write this, Microsoft just made a $69B all-cash offer for Activision and reported over $50B in quarterly revenue.)

I first watched this power dynamic begin to shift while at Microsoft.  For years, the Wintel duopoly locked Microsoft and Intel in a collaborative/competitive partnership as rough peers.  I sat in more than one “frank and spirited” exchange between leaders of the two companies, where each pushed the other to accommodate the other’s market needs. As server chips, clouds, and infrastructure grew in importance relative to desktops and laptops, the power dynamic began to shift.  Today, Intel is the far weaker player, in many cases forced to respond to cloud vendor demands.  In hindsight, it is all obvious.

Technological and Economic Shifts

Today, Apple and the other cloud service companies dominate the computing hardware and software ecosystem, both in scale and in technical approaches. Initially, they purchased standard hardware for deployment in traditional colocation centers (colos). Then they began designing purpose-built data centers, optimized for power usage effectiveness (PUE), deployed at sites selected via multifactor optimization – inexpensive energy availability, tax breaks and political subsidies, political and geological stability, network access, and customer demand. 

As scale, complexity, and operational experience grew, new optimization and leverage opportunities emerged, including software defined networking, protocol offloads, and custom network architectures (greatly reducing dependence on traditional network hardware vendors), quantitative analysis of processor, memory, and disk failure modes, with consequent redesign for reliability and lower cost (dictating specifications to vendors via consortia like Open Compute), custom processor SKUs, custom accelerators (FPGAs and ASICs), and finally, complete processor design (e.g., Apple silicon, Google TPUs and AWS Gravitons).  In between, they deployed their own global fiber networks.

This virtuous cycle of insatiable consumer demand for rich services, business outsourcing to the cloud, expanding data center capacity, and infrastructure cost optimization has had several effects.  First, it has dramatically lessened – and in many cases totally eliminated -- FAANG/Microsoft/BAT dependence on traditional computing vendors and ameliorated the risks of wholesale transfer pricing. Put another way, Amazon AWS, when necessary, can now negotiate pricing terms with Intel and AMD in the same way Walmart does with Procter and Gamble (i.e., from a position of strength as a dominant customer).

Second, the absolute scale of infrastructure investment, denominated in billions of dollars per year for each cloud service vendor, means these companies are shaping computing technology research and development priorities in ways traditional computing vendors rarely can, driving design and fabrication priorities in every element of the ecosystem.

Bigger is not simply bigger; bigger is different, fueling investment in new technologies at a phenomenal rate. (See The Zeros Matter: Bigger Is Different.) Relatedly, I cannot overemphasize the massive scale of these cloud data center deployments, dwarfing anything seen in academia or national laboratories, with the already large gap widening each year.  The CAGR is simply astounding.

Third, custom infrastructure is about more than economic leverage; it’s about market differentiation and competitive advantage.  Anything not differentiating is standardized and commoditized, driving unnecessary cost out of the system, and concomitantly reducing profits for commodity purveyors.  Conversely, anything differentiating is the focus of intense internal optimization and aggressive deployment.

Equally importantly, client plus cloud services vendors predominately make their money from the services offered atop their infrastructure, much less from the infrastructure itself. Unlike Intel, AMD, and Nvidia, which sell silicon, Apple is not selling A15 SoCs; it is selling iPhones, just as Amazon and Google are selling services atop TPUs and Gravitons.

Fourth, all of this is occurring against a backdrop of big data and machine learning. Large-scale AI itself is fueling a computing revolution, with an insatiable hardware demand to train ever larger machine learning models.  As an example, modern speech recognition models now have hundreds of billions of parameters, and multiple groups have announced models with over one trillion parameters. Similarly, work on generative adversarial networks (GANs) has led to impressive advances in game playing and other tasks. Nor is deep learning limited to “AI workloads;” there are impressive results on solving differential equations as well.  (See Dennis Gannon’s excellent summary of Karniadakis’ work here.) Similarly, look at developments in generative adversarial networks (GANs),

Finally, all of these trends are convolved with the end of Dennard scaling, dark silicon, functional specialization, chiplets, semiconductor fabrication challenges, rising fabrication facility costs, and an uncertain political future for Taiwan and TSMC.  Moreover, the actual cost of transistors (dollars/transistor) is increasing as we move closer to one nanometer scaling.  In fact, the transistor cost minimum occurred at roughly the time (~28 nanometers) we switched from planar CMOS to FinFETs. It will likely only get worse as gate-all-around FETs predominate, given increased fabrication complexity.

In the midst of all this, Intel stumbled, losing its lead in process technology just as EUV finally and belatedly became mature, and TSMC staked a lead. Time will tell if Pat Gelsinger can turn things around.

The Future

Okay, let’s put all the technology and economics puzzle pieces together:

• Advanced computing requires non-recurring engineering (NRE) investment to develop new technologies and systems.
• The FAANG/Microsoft/BAT companies are cash rich (i.e., exothermic), and they are designing, building, and deploying their own hardware and software infrastructure at unprecedented scale.
• The traditional computing vendors are now small – in some cases, tiny – economic players in the computing ecosystem, and many are dependent on federal investment (i.e., endothermic) for NRE to advance the bleeding edge.
• AI is fueling a revolution in how we think about problems and their numerical solution.
• Dennard scaling is over and performance advances increasingly depend on functional specialization via custom ASICs and chiplet-integrated packages.
• Transistor costs are likely to continue to increase as we push to smaller feature sizes.
• Nimble hardware startups are exploring new ideas, driven by the AI frenzy.
• Talent is following the money and the opportunities, which are increasingly in industry.

With this backdrop, what is the future? I believe some of it is relatively obvious and secure, given the current dominance of U.S. cloud service providers. However, broader U.S. leadership in semiconductors, information technology, and bleeding edge HPC is less certain. I believe any viable solutions will require rethinking our approaches and assumptions, including how, where, and when we spend money and what will both grow and retain talent.  Let’s start with the broad issues around the U.S. information technology ecosystem.

Information Technology Futures

The big changes outlined above make it obvious that a computing revolution is underway, one that is reshaping global leadership in critical areas.  All revolutions create opportunity, but only for those prepared to accept the risks and act accordingly. In Far Side cartoon parlance, we must be tall enough to attack the city.

First, we need to be honest about the social, political, economic, and national security risks of losing U.S. leadership and control of the silicon fabrication ecosystem.  Fabless semiconductor firms are important, but we also need onshore, state of the art fabrication facilities, and we need to invest and act accordingly. It is why the U.S. CHIPS Act and its successors are so important, and why the recent announcements by Intel, TSMC, and GlobalFoundries that they are building new chip fabrication faculties in the U.S. (each for different reasons), is so important.

Second, the seismic business and technology shifts have also led to equally dramatic talent shifts. People gravitate to opportunities, and that giant sucking sound is people leaving academia, national laboratories, and traditional computing companies to pursue those opportunities.  Seymour Cray once said, “I wanted to make a contribution,” when asked why he had used so many high risk technologies in the Cray-1. The desire to make a difference is no less powerful today.

The academic brain drain among artificial intelligence researchers is well documented.  After all, in the commercial world one can develop and test ideas at a scale simply not possible in academia, using truly big data and uniquely scaled hardware and software infrastructure.  The same is true for chip designers and system software developers.

Making matters worse, the U.S. talent pipeline is uncertain.  As the recent 2022 Science and Engineering Indicators shows, the U.S. is losing ground compared to the rest of the world, trailing China in both patents and publications, as summarized in this Science story about the recent Science and Engineering Indicators report. 

Our K-12 STEM education performance lags most of Asia and Europe – as shown in the chart to the right, we rank between Hungary and Turkey in mathematics performance by 15-year-olds.  We also have deep and systemic inequalities in STEM education, and we have sadly created an environment that is less attractive to international graduate students and scholars than it once was.  The latter is especially worrisome given that over 60 percent of computer science and mathematics Ph.Ds. working in the United States were born overseas.

Practically, this means we need to recognize the need for determined actions:

• Raise the levels of federal investment in ongoing information technology R&D. This means billions, not millions of dollars.
• Invest in the talent pipelines needed to make academic and laboratory R&D attractive.
• Partner deeply with the U.S. private sector in new and creative ways.

Now, what about advanced scientific computing futures?

Leading Edge HPC Futures

It now seems self-evident that supercomputing, at least at the highest levels, is endothermic, requiring regular infusions of non-revenue capital to fund the NRE costs to develop and deploy new technologies and successive generations of integrated systems. In turn, that capital can come from either other, more profitable divisions of the business or from external sources (e.g., government investment). 

While at Microsoft, I once pitched a technical computing idea to CEO Steve Ballmer.  He listened patiently to my spiel, then simply said, “I’m not interested.”  Being nothing if not persistent, I asked why.  To which he replied, “It’s not that I think you are wrong.  I actually think you are right – we could drive a few hundred million dollars in revenue, but the opportunity cost is too high, and the return on investment (ROI) is too low.  Come back when you have an idea that starts with B (billions), not M (millions).”

I was chastened, but it was a valuable lesson that I fear too many in high-end scientific and engineering computing have not learned.  A business leader must always look at the opportunity cost (time and cost of money) for any NRE investments.  The core business question is always how to make the most money with the money you have, absent some other marketing or cultural reason to spend money on loss leaders, bragging rights, or political positioning.   The key phrase here is “the most money;” simply being profitable is not enough. 

Therein lies the historical “small fortune” problem for HPC.  The NRE costs for bleeding edge supercomputing are now large relative to the revenues and market capitalization of those entities we call “computer companies,” and they are increasingly out of reach for U.S. government agencies, at least under current funding envelopes. The days when a few million dollars could buy a Cray-1/X-MP/Y-MP/2 or a commodity cluster and land in the top ten of the Top500 list are long gone. Today’s game requires hundreds of millions of dollars to deploy a machine at the high end and at least similar, if not larger investments in NRE.

What does this brave new world mean for those of us in the big iron, bleeding edge HPC crowd?  First, as I mentioned over a decade ago, we are really the fast iron crowd, as our biggest systems are now dwarfed by the overall scale of commercial cloud infrastructure deployments. Second, we may still be invited to dinner, but economically, we are increasingly relegated to eating SpaghettiOs and drinking Kool-Aid at the kids table, while the adults are dining on lobster and drinking limited release, estate-bottled wine. 

Put another way, the golden rule applies – buying a $500M supercomputer every five years is not much financial leverage against a multibillion dollar spend each year. Equally importantly, most of the traditional supercomputing vendors are sitting at the kids table with us.  In turn, federal investment (e.g., DOE’s exascale PathForward program) is quite small compared to the scale of commercial cloud investments and their leverage with the same vendors.

First, it means acknowledging that we in bleeding edge HPC have limited leverage; we cannot influence vendors in the same ways we did in the past. The delays and cost overruns for Argonne National Laboratory’s Aurora system and the (resolving) deployment pains for Oak Ridge National Laboratory’s Frontier system speak to these challenges. Meanwhile, China has already deployed two exascale systems, both built using Chinese components.

We also need to recognize that bleeding edge HPC is about more than solving time-dependent partial differential equations on complex meshes.  They will always matter, but other areas of advanced computing are also of high and growing importance.  After all, the Science 2021 Breakthrough of the Year was for AI-enabled protein structure prediction, with transformative implications for biology and biomedicine.  This is a cultural shift that will require us to lessen our hold on some cherished beliefs and acknowledge the world has changed.

Second, it’s time we got serious – really serious – about end-to-end co-design, from device physics to applications, following the lead of the cloud ecosystem operators, and where possible and appropriate, partnering with them. This means more than encouraging tweaks of existing products or product plans.  Rather, it means looking holistically at the problem space, then envisioning, designing, testing, and fabricating appropriate solutions. (See Advanced Computing: Integrative Thinking for the Future.)  As a contact sport, it means substantially expanded investment in basic research and development, something that unfortunately suffered as the U.S. Exascale Computing Project (ECP) diverted limited funds to timeline-driven deployments

Third, we will need to embrace the chiplet ecosystem and work collaboratively with the commercial players to establish interoperability standards (e.g., the Open Domain-Specific Architecture (OSDA) project within the Open Compute Project). Chiplets are more than a way to mix and match capabilities from multiple sources, they are an economic and engineering reaction to the interplay of chip defect rates, the cadence of feature size reductions, and semiconductor manufacturing costs (CAPEX and OPEX). Concomitantly, we must both work with the new chiplet design and integration companies while also designing chiplets, both driven by the specific needs of quantified scientific workloads.

There ARE interesting things happening in the hardware space, but many of them are in the startup world.  One need look no further than companies like Graphcore, Groq, and Cerebras for inspiration, and at the ferment in edge devices and AI. I will not name other companies, as several are still in stealth mode. Part of this requires some flexibility and imagination.  (Full disclosure, I started NCSA on the GPU accelerator push by building a cluster of Sony Playstations and formed the first partnership with NSF on cloud computing.)

Fourth, we will need to build real hardware and software prototypes at scale, not just incremental ones, but ones that truly test new ideas using custom silicon and associated software. In turn, that means accepting the risk of failure at substantial scale (e.g., that $50M prototype didn’t work, but we learned some important things, and we are going to build another one).  (See Luck Is a Fickle Friend.)

To do these things, we will need to invest in recruiting and sustaining research teams – chip designers, system software developers, and packaging engineers – in an integrated end-to-end way AND create opportunities than make it intellectually attractive to work on science and engineering problems. Simply put, we will need to create integrated teams and build integrated prototypes at scale, both at national laboratories and in academia.

Finally, and I realize this may be heretical to some, we need to ask when and where we would be best served by embracing commercial cloud services for our HPC needs.  The performance gaps between cloud services and HPC have lessened substantially over the past decade.  See, for example, a recent comparative analysis by the UC-Berkeley group. HPC as a service is now both real and effective.

I realize some in academia and national laboratory community will immediately say, “But, we can do it cheaper, and our systems are bigger!”  Perhaps., though proving such claims means being dispassionately honest about technological innovation, NRE costs, opportunity costs, staff career goals, and the true total cost of ownership (TCO). In turn, this requires a mix of economic and cultural realism in making build versus use decisions and asking what cloud service vendors would be willing to do for academic and government consortia at large scale (e.g., a billion dollar annual partnership). 

In summary, it is increasingly unlikely that future high-end HPC systems will be procured and assembled solely by commercial integrators from commodity components.  Rather, they are likely to be (a) collaborative partnerships with cloud providers, (b) increasingly bespoke, designed and built collaboratively to support key scientific and engineering workload needs, or (c) a combination of these two.

Put another way, bleeding edge, exascale class computing systems are no different than the Vera Rubin Observatory, the LIGO gravity wave detector, or the Advanced Photon Source.  Each contains commercially designed and constructed technology, but they also contain large numbers of custom elements for which there is no sustainable business model.  Instead, we build these instruments because we want them to explore open scientific questions, and we recognize that their design and construction requires government investment.

Coda

As our U.S. political divisions illustrate, investing in the future will not be easy, but it is critical if the U.S. is to remain globally competitive. Let’s be clear.  The price of innovation keeps rising, the talent is following the money, and many of the traditional players – companies and countries – are struggling to keep up.  Andy Grove was only partially right.  It is true that only the paranoid survive, but they’d best have deep pockets and enticing intellectual opportunities as well.  That needs to be us.

N.B. My thanks to Jack Dongarra, Doug Burger, Dennis Gannon, Tren Griffin, Tony Hey, and Jim Larus for thoughtful feedback on drafts of this post.  Any errors of fact and all opinions are my own. In full disclosure, I am a former Microsoft corporate vice president and a current stockholder.

On Catastrophes and Rebooting the Planet

As all those who can self-isolate to help reduce the spread of COVID-19 and all but essential services are closed in many locations, local, regional, national, and international commerce has declined precipitously, with calamitous effects for many workers, businesses – large and small – and even governments and their instrumentalities.  There is fear, pain, and suffering in many places and in many forms, and this reality supersedes any claims to the contrary. (See Nature Cannot Be Fooled.)

This is a wicked problem, more complex in many ways than the Great Recession of 2008 because it convolves two complex issues – public health and economic vitality. The same actions that encourage commerce endanger human lives. Conversely, the actions that protect lives have profound, negative economic consequences.  Our socioeconomic equilibrium has been disrupted, with an uncertain path to its restoration.  It is, to use a common phrase, a global catastrophe -- an abrupt and unexpected event causing great and relatively sudden damage and suffering.

There’s a branch of mathematics that studies such abrupt and unexpected changes; it’s aptly named catastrophe theory.  As Wikipedia explains, “catastrophe theory is a branch of bifurcation theory in the study of dynamical systems,” but a simpler description is that catastrophe theory characterizes small changes in the parameters of a nonlinear system that cause equilibria to appear or disappear and a system to jump from one macrostate to another. In that sense, it is a formalization of the colloquial meaning of a disruptive event.

My Geek Childhood

When I was a young teenager, the catastrophe theory work of René Thom had a brief run in the popular consciousness, as the country grappled with economic shocks engendered by the energy crisis of the 1970s. It was even featured in the print edition of Newsweek, due in part to its popularization by Christopher Zeeman. Catastrophe theory was also featured in that other must read periodical of my childhood, Scientific American, alas after I was already in college. (See Zeeman’s delightful April 1976 Scientific American article, entitled simply, Catastrophe Theory.)

That I both read Newsweek and Scientific American avidly and ravaged multiple libraries for books on the underlying mathematics of catastrophe theory perhaps says more about my young teenaged years in rural Arkansas than about catastrophe theory’s grip on the popular imagination.  (See Libraries: Arms Too Short to Feed the Mind.) Let us now bow our heads in a brief acknowledgement of the self-evident reality that I was and am a geek or nerd. (I accept either appellation – but I take umbrage at dweeb or dork.) Needless to say, I had plenty of time for reading on Saturday nights.  Having explicated the limitations of my teenaged social life, let us plunge deeper into catastrophe theory.

Catastrophe Theory 101

This simple diagram on the right illustrates one of the most commonly used models, a cusp catastrophe. Here, two control variables (a, b) perturb a simple function y = u⁴/4 by the addition of a quadratic

yielding a potential function of the form

(Note that the constants are purely for mathematically simplicity.  Any desired scaling factor can be applied to the control or internal variables.)

When the control variables (a, b) have fixed values, the system settles into an equilibrium state where the internal variable u minimizes the potential function.  However, as the control variables change, a local minimum can disappear, and the internal variable can jump suddenly to a different equilibrium, the cusp in the diagram.

A little calculus – setting the first derivative to zero – shows that the equilibrium surface is

The line that marks the edges of the bend in the surface from the upper sheet to the lower sheet is called the fold curve. Finally, the bifurcation set (cusp) is the critical image of the projection (a, b, u) -> (a, b) from the equilibrium surface onto the control space.

Intuitively, small changes in the (a, b) control plane define situations where a stable solution (for values of u) will suddenly jump to an alternate stable solution. As the diagram also shows, there are hysteresis loops where crossing the cusp curve in one direction may cause a catastrophe, but immediately reversing direction does not restore the previous state.

The astute reader will have noted that the cusp catastrophe describes a deterministic system (i.e., the first derivative is time invariant), and most domains include random, time-dependent elements.  The simple cusp catastrophe can be readily extended to stochastic environments (e.g., by adding a Wiener process, W(t)), resulting in a stochastic differential equation.  This means the equilibrium plane and transition cusp vary over time, based on the underlying stochastic process.

For those of you playing the game at home, a Weiner process is a real valued continuous time stochastic process that is the scaling limit of a random walk.  This is yet another example of the interconnectedness of things, spanning topology, dynamic systems, random walks, probability theory, and diffusion processes.

Rebooting the Planet

Now that I have totally buried the lede of the current public health and economic crisis under childhood geekdom and the mathematics of catastrophe theory, here is the relevance and intuition. Think of our control variables (a, b) as social distancing and government economic policy, respectively, and our outcome as socioeconomic health and stability.  Small changes in social distancing or government policy can trigger abrupt transitions in socioeconomic stability due to large declines in public health or economic function (e.g., a pandemic and recession). This is the important point: no simple reversal of those changes can immediately return society to its prior, healthy and prosperous state. All of which brings us to hysteresis and a restart of global society.

Shutting down a planet, a country, a city, or even a university is neither a small nor a simple thing.  Critical infrastructure must be maintained and essential services must remain operational.  More importantly, each of those has collateral dependences.  Infrastructure and services are fractal and self-similar at multiple levels. They are also globally interconnected, with dependences unimagined even a generation ago. (See The World Is Small and Globally Connected, Globally Worried.)

How does one reboot a planet?  First, an immediate return to normal is simply not possible; as the cusp catastrophe model shows, there is hysteresis in any individual and collective action set.  Jobs and income have disappeared; businesses and industries have been damaged and destroyed; tax revenue has been lost; and families and communities have been devastated by illness and death.

Second, an overly rapid and aggressive restart – an “all is back to normal” ethos – risks a rebound pandemic, given inadequate herd immunity or a time to develop a future vaccine.  Conversely, a timid, overly cautious resumption of business and social activity risks unnecessary economic loss and prolonged pain and suffering for hundreds of millions of individuals.

Resumption is not a binary process. Today, we are self-isolating and social distancing, alternating between sitting alone in sweatpants while anxiously watching talking heads on cable television and venturing out with trepidation for food and supplies, warily eyeing the person across the street, fearful they will not keep their distance. We cannot immediately shift to large group activities, attending mass sporting events and glad-handing colleagues, friends, and family with abandon. Rather, resumption must be phased, with careful monitoring of public health and economic activity and recognition that there may be multiple missteps and necessary backtracking. It is a thoughtful, careful process that must be driven by reason and data, not emotion and intuition. (See The Simple Things Matter, Most of All Now and Just the Facts, Ma’am: Reasoning is Not Dead, Jim.)

Nor are universities exempt, with their tens of thousands of students, faculty, and staff drawn from around the world, living, working, and eating in close proximity.  As a practical matter, universities are small cities, with logistics and transportation systems, museums and performing arts centers, hospitals and healthcare systems, housing, dining, and recreation facilities, all interconnected with research, education, and community engagement.

These are complex dynamic systems with many co-moving stochastic components, self-similar at many, many scales.  Individuals adapt continuously, but public policy is necessarily discrete. As my colleague, Michael Good noted, it is wise to view the post-pandemic reboot as consisting of red, orange, yellow, and green phases, each representing incremental resumption of social and economic activities, albeit with potential reversion to earlier phases based on observed outcomes.

Remember the key lesson of the stochastic cusp catastrophe. Disruptions cannot be immediately inverted; complex dynamic systems involve hysteresis. We will persevere and the planet will reboot, but it is more complex than a simple Ctrl+Alt+Del. The future will not look like the past for quite a while, but it can be both different and better if we work together in a collaborative spirit.  (See Renaissance Teams: Reifying the School at Athens.)