The Quiet Rhetorical Rebranding of American Science: From "Investment" to "Assistance"

For decades, the story of American scientific supremacy was built on a simple, powerful premise: basic research is a vital public investment. In the postwar era, the pioneering policymaker Vannevar Bush argued that funding curiosity-driven science was a foundational national necessity. In his view, which prevailed across U.S. administrations for the next 8 decades, it wasn’t a luxury or an expense; it was an investment that would inevitably return dividends in the form of cured diseases, economic growth, and national security. This strategy laid golden eggs. USA has been an engine of innovation and scientific research unparalleled in the history of mankind.

But over the last few years, a subtle, bureaucratic shift in language has begun to quietly erode that grand narrative.

If you look closely at the recent (2025-26) Notices of Award issued by the National Institutes of Health (NIH) or the National Science Foundation (NSF)—including the NSF's sweeping new Guidance on Financial Assistance—you will notice a hyper-fixation on a specific legal phrase: "Financial Assistance." (see citations 1-4)

While federal agencies defend this branding as a technical, administrative necessity to align with uniform government standards (2 CFR Part 200), words matter far beyond their narrow legal definitions. By replacing the rhetoric of "public investment" with the language of "financial assistance", the government has fundamentally altered the narrative of why it funds science.

The rhetorical consequences of this shift are profound, manifesting in two dangerous ways, dangerous for the nation and, because of American supremacy, dangerous for the world:

1. From Public Partner to Government Dependent

Rhetorically, an investment or an endowment implies a shared future and a mutual return on capital. It suggests that scientists and universities are elite partners creating a permanent national asset.

The word assistance, however, implies a deficit. Historically tied to corporate bailouts, disaster relief, or social safety nets, "assistance" carries the connotation of a one-way subsidy or charity. It reframes the scientist not as a creator of foundational public knowledge, but as a dependent beneficiary requiring a government handout to survive.

2. The Weaponization of the "Assistance" Label

This isn't just an abstract debate over semantics; it has become a central battleground in recent congressional budget hearings. When science is branded as discretionary "assistance," it becomes far easier for fiscal hawks to target it as bureaucratic waste.

We are already seeing this manifest on Capitol Hill. Recent proposals from the Office of Management and Budget (OMB) have attempted to codify scientific peer-review as merely "advisory," arguing that because these funds are "financial assistance", the executive branch should have the final political say over which projects get greenlit (see citations 5-6) Meanwhile, critical research infrastructure is being targeted for budget cuts, with lawmakers treating university laboratory reimbursements as an inefficient drain on taxpayers rather than an essential public good.

Fighting Back for the Public Good

Admirably, the scientific community is refusing to let this rhetoric go unchallenged (see citations 7-8). Major professional organizations, such as the American Chemical Society (ACS) and the American Physical Society (APS), have launched aggressive counter-campaigns. They are fighting the "assistance" narrative by flooding Congress with hard economic data, proving that a single dollar invested in fundamental research returns multifold dividends to the public treasury over time.

Advocates are reminding the public of history: how a niche, federally funded study on Gila monster venom in the 1990s—dismissed at the time by critics as a wasteful government handout—ultimately led to the creation of blockbuster GLP-1 diabetes and weight-loss drugs like Ozempic.

The lesson is clear: when the government funds a laboratory, it isn't giving charity to a scientist. It is buying the future of global health, technology, and economic security. If we allow bureaucratic legalese to redefine scientific exploration as a public burden rather than a public good, we risk sacrificing the very breakthroughs that secure our future. Let's call it what it is: an investment.

Relevant Citations:

1. The Original Statutory Distinction (1977/1978): The foundational requirement to clearly segregate government contracts from grants is codified under the Federal Grant and Cooperative Agreement Act (31 U.S.C. §§ 6301–6308).

2. The Modern Overhaul: 2 CFR Part 200 (Uniform Guidance): Detailed directives on cost limitations and structural revisions are governed by the Office of Management and Budget (OMB) Uniform Guidance.

3. The "Regulation for Federal Financial Assistance" (Docket OMB-2026-0034): The active draft outlining the transition of peer-review into an advisory body and expanding political termination powers is detailed via the Federal Register (May 29, 2026).

4. National Science Foundation's GFA Framework: The formal rebrand and transition text substituting legacy guides with the new manual is documented in the draft NSF Guidance on Financial Assistance (GFA) released on June 24, 2026.

5. House Science Committee Hearings (June 2026): Representative George Whitesides (D-CA) raised formal concerns regarding White House political interference in the NSF's internal approval pipelines, pointing to a slow-down in issued awards AIP.org FYI.

6. Congressional Pushback on Merit Review Integration: Representatives George Whitesides and Zoe Lofgren explicitly demanded a briefing on political appointees encroaching on federal research allocation protocols via Official House Correspondence.

7. American Chemical Society Public Comments: The ACS formally recorded detailed pushback regarding the threat to fixed-amount awards and core research investments via their Official Statement on Notice ID OMB-2026-0034.

8. American Physical Society Campaigning: The policy updates and coordinated grassroots advocacy responses intended to challenge the OMB's alignment strategies are monitored through the APS Policy Initiative Network.

Modularity, Reconfigurability and Institutional Robustness: A post-COVID19 model

COVID-19 crisis has revealed the vulnerabilities of our interdependent and globalized economies, where highly specialized and focused manufacturing industries are distributed across the world and supply chains that are managed through numerous tenuous links, where the resultant structure is only as strong as its weakest link. At this time of emergency, incentives to reconfigure businesses to produce essential items did not work as efficiently as we generally credit private enterprises: governments had to step in with war-time powers to compel businesses; broken supply chains had to be restored through intergovernmental airlifts. We are rudely awakened to limitations of private enterprises for rising to the challenges of a globalized emergency, and a renewed appreciation of efficient governance is in order.

How might such governance be organized?

There are two previous models where nationalism provided the impetus: wartime efforts in the west and in the east. The US and UK used war powers acts to first subdue and to ultimately kindle the awesome power of private industries manufacturing automobiles and railways to make tanks, battleships, fighter planes and bombs; the Soviet dictatorship did the same, perhaps even more effectively, to convert lumbering shipyards and steam engine manufacturing factories to make tanks and rifles. The problem was that at the end of the war both had to keep the war-time factories working for a while so as to feed the nations—so we and the Soviets had our Korean war and our Vietnam.

Is there a less destructive way out of war-time diversion of resources? Or must we always be burdened by our momentum? Is there a way that minimizes the retooling for reconfiguration of industries to enter and then to leave the war-economy, be it due to human conflict or a future pandemic?

A recent concept in evolutionary biology is ‘modularity’—a term borrowed from engineering and systems science. Like Lego blocks, are there basic building blocks of gene circuits that are reconfigured by evolution to produce the bewildering diversity in nature? Moreover, such modularity is thought to provide evolutionary robustness—the niche vacated by the extinction of a species is quickly replaced by organisms that evolve through reconfigured genetic modules. Modules reconfigured perform novel functions that their previous ensembles didn’t. There are lessons here to be learned.

What we need is an abstraction, a conceptualization of modularity of manufacturing industries, and of supply chain Lego pieces. A high-level government agency would need to examine each industry to identify the modularity and reconfiguration strategies for natural (pandemic, earthquake, global-warming) or man-made (foreign or civil war) catastrophes. They will be the intelligence gatherers, systems modelers, and will develop scenario-specific contingency plans based on data and model.

They will interface with FEMA, the NAS, the Congressional Budget Office, will be overseen directly by the Congress, and will work in direct consultation with a similar structural entity established through the UN. An agency for the analysis management and design for systemic robustness.

Estimated Loss to the US Economy due to COVID-19, If the Disease were to Take its Course

Here I attempt a back of the envelope estimation of the total GDP loss to the USA alone if COVID-19 is allowed to run its course without any containment measures in place.

This assumes a conservative figure (30%) of the oft-repeated range(25 – 75%) of the total incidence rate of COVID-19 if no quarantine or stay-at-home types of segregation measures are taken.

I have used the 2010 demography numbers of the US population by age and sex and used the life-expectancy table of 2010 for the US population from the US census.

Here are some broad stroke assumptions I made for this rough estimation:

1.     2010 US census age and sex distributions

2.     2010 US life expectancy distributions

3.     Infection rates follow a normal distribution (this is the epidemiological standard)

4.     Males and females are equally infected (though not equally affected)

5.     30% of the population are ultimately infected within 1 year (the range provided by epidemiologists are from a low of 25% to a high of 70%)

6.     All age groups are equally infected (though not equally affected)

7.     I have used a sliding scale of treatment weight for various age groups, assuming 100% treatment rates for all age groups, except the above 65 years, in which I assumed 80% treatment rates (an ad hoc assumption but something that is seen in Italy where the resources are saturated).

8.     Used a sliding scale of death rate (0.01 – 0.035) per age group. The upper range is a low estimate. Current mean rate according to WHO is 0.045 (which is widely considered to be an over-estimate, but current >75 years age group mortality rate is ~14.8%, so I have erred in favor of an under-estimation)

Using these parameters, I have calculated the DALY lost due to COVID-19 in one year. DALY, Disability-Adjusted Life Years is an economic measure used to estimate the loss to the economy due to a particular health issue.

DALY = Number of cases x disease duration x Disability weight + YLL

Here, the number of cases was estimated according to the assumptions stated above; disease duration was assumed to be 2 weeks for below 65 years and 3 months for >65 years; disability weight was 0 for all age groups except for 65+ yeas which on average was assumed to be 20%.

YLL is more complex, and is automatically calculated by the R-package “DALY” within R, and is defined below (ref: https://www.ncbi.nlm.nih.gov/pubmed/23927817)

Below is a snapshot of the input data:

And below is the resulting distribution of DALY estimated with the above parameters:

The mean value of DALY loss turns out to be: 41,056,030 years.  This is the estimated mean loss of DALY to the economy due to COVID-19.

To translate that to the loss to the economy, we will need to multiply the per capita annual GDP of the US by this number:

The total estimated loss to GDP in one year due to COVID-19 if no specific additional measures to minimize the natural course of COVID-19 were in place

= 41,056,030 years x (per capita GDP of 2019) per year

= 41,056,030 x $65,116

= $2.67 x 10¹²

=$2.67 Trillion

Assuming again the US GDP of 2019, this means ~13% expected drop in GDP in 2020 relative to the previous year's GDP.

In Memoriam: Professor R. L. Brahmachary

On February 13, Professor R. L. Brahmachary passed away at the age of 85. I had known Professor Brahmachary since 1973. He was an impressive man who bounded with energy, his eyes flashed with excitement as he spoke. I often sought him out in his lab at the Indian Statistical Institute. He mentored me, and tried to bring a bit of discipline to my thoughts, edited my letters I was in the habit of writing to foreign scientists on their work while I was an undergraduate student in Calcutta.

Ratan Lal Brahmachary was born in Dhaka, Undivided India, in 1933. He was noticed by Professor S. N. Bose (of Bose-Einstein statistics/Boson fame) when he was an undergraduate student of Physics in Calcutta, a refugee from the newly divided East Pakistan with his widowed mother. On Professor Bose's recommendation, young Brahmachary went to do PhD in physics in Germany (Institut für Theoretische Physik, Universität Hamburg) shortly after WWII--neither Bose nor Bhrahmachary knew that German physics was in near ruins. Nonetheless, Brahmachary completed his thesis on relativistic field theory in record time in Germany (he published this in 1956 and 1957: "A generalization of Reissner-NordstrÖm solution I" & later "... II). At that time he received a letter from his mother that she was not well. Brahmachary left for India without completing the stipulated residence of 18 months on campus for qualifying for a PhD degree. So he never received his PhD, but had a solid amount of work under his belt worthy of good publications and a glowing recommendation from his German advisor (I don't remember who he was) and also professor Bose.

Brahmachary joined Indian Statistical Institute upon coming to the notice of its founding director, Professor Mohalanobis, the famed physicist/statistician who was once the Cambridge roommate of S. Ramanujan, the mathematics savant. He continued working on field equations (published, "A class of exact solutions of the combined gravitational and electro-magnetic field equations of general relativity" in 1958).

Very soon, Professor J. B. S. Haldane, the last of the English polymaths, joined ISI, and he influenced young Brahmachary's conversion to biology. Upon Haldane's recommendation, Brahmachary went to Paris, to work in Jean Brachet's laboratory, where he did pioneering work on demonstrating that there is a fraction of rather stable RNA in frog's eggs that appeared to be maternally inherited. This was entirely on the basis of fractionating pulse-labeled RNA into oocytes and eggs, at a time when one thought there were only two kinds of RNA--the mRNA (which was just discovered by Sydney Brenner, Jacque Monod, and Francis Crick, and the not-mRNA, which would soon be labeled as rRNA and tRNA by people in Jim Watson's lab. I am talking of 1958-1961.

Returning from Brachet's lab, Brahmachary went on a dizzying bit of activity, in which he worked with sciona, a protochordate, with Acetabularia mediterannia, and also common Indian snail, and showed evidence for the maternal inheritance of stable mRNA into the embryo. This work was collected together into one publication in a somewhat obscure journal, Progress in Biophysics and Molecular Biology in 1968, a copy of which I accidentally picked up in the British Council Library on Theatre Road in Calcutta, when I was a first year undergraduate. That was when I sought him out.

The city was in chaos at the time, with Naxalite, CPI-M, Youth Congress and CRP clashing in dingy lanes and young students disappeared every day. Colleges were closed on many days, classes cancelled, which gave me enough time to take the bus to ISI and talk to Professor Brahmachary and snoop around in his lab. When I moved to JNU in 1975, we kept in touch.

In 1976 he came to New Delhi on a review panel, but took this occasion to come and spend a weekend in my dorm room--we visited the Delhi Zoo and the ravines next to JNU looking for migrating birds. He had taken the slow train from Calcutta to Delhi--the Toofan Mail--because he wanted to estimate the breeding success of the Saras Cranes, the great migratory cranes that winter in northern India, by counting the number of nesting adults and chicks along the railway route. An ingenious idea.

He had by then visited East Africa (Congo, Rwanda/Urundi, and I think Tanzania) some four or five times to study mountain gorillas. Apparently a kind of gorilla he was observing ate only the leaves of the Mufumba tree (I still recall the name, which he pronounced with his characteristic gusto), which, he had determined, had extremely high content of ascorbic acid.

Soon after, he switched his research full time to the study of pheromones in tiger, and became an active player in tiger conservation in eastern India. He and the famous animal behaviorist George Schaller communicated frequently. In 1990, he published a report in Nature on tiger pheromones (Nature 344:26), which had as the main ingredient a fatty acid derivative that was also found in Basmati rice! Here is a clip on his intense dynamism and optimism, as he describes what science is like, at the time of his retirement in 1993. The interviewer is my daughter, who was a fifth-grade student at the time.

“Whence I am” The joy of multiple identities

An answer of sorts to Stephen Murphy-Shigematsu

The taxi picks me up at the blue hours. Blowing swirls of gray vapor in the air I shuffle in. “Airport?” He asks.

With a slight air of discomfort that exists between any two grown men who’d likely never see each other again, he eyes me with a respectful silence. The morning is crisp and fresh, but who knows how long has he been up in this shift.

“Where are you from?” I ask, trying to break the ice. He beams broadly and says, “Make a guess!” “Seattle, obviously,” I said with a smile. “Oh no my friend, you are avoiding the real question! Where am I really from?”

Where is anyone really from? As a fourteen year old, though born in a small city in India whose parents were both considered refugees from the freshly partitioned East Pakistan, though both had spent their formative youths in India, I did not fit in well with the local boys. I was always an outsider in a city where the insiders had lived for at least four or five generations. I looked at the national boundaries on a painted tin globe bought at the railway station, and thought of the invisible curvy planes that people imagine going up from the soil into the thin skin of air surrounding our planet, which then diffuse into confusion far below the troposphere. These imaginary planes had always seemed to me as arbitrary, as meaningless, as religion or nationalistic pride.

When I look into the mirror I don’t seen an Indian or an American; only brown eyes, slightly asymmetric, a wrinkled skin in need of a shave. I doubt if my daughter sees an Indian, an Australian, or an American either

Guessing where one is from is a game—trivial because of its superficiality and important at the same time because the answer might provide a clue to the person one might be. Sentient beings that we are, we instinctively go beyond the species identification by sight or smell as we encounter another individual. We try to make a mental image of the mind of the other—we try to guess, perhaps second guess, the persona, the biases, common interests, what might offend, perhaps even some idea of the stranger’s experiences. This is pure human curiosity—the one characteristic that has ensured our evolutionary survival despite the nakedness of our skin and our relative frailty in relation to those of other apes. Whether we ask, permitted by our arbitrary norms of politeness, or not—we cannot prevent ourselves thinking about it. We are curious apes.

Each one of us is a vector of identities: birth place, where we lived as a child, where we live now, ethnicity albeit its plastic boundaries, color of skin, how we speak, what we eat, how we dress, our belief systems, political persuasion, what books we like, whom we like, and so on. Each of us ranks the elements of the vector in a unique hierarchical order. Therefore, no two persons can probably match their respective identities, and yet we instinctively attempt to find the distance between our two vectors—because the result might crucially influence the outcome of our interaction. Possible friendship; a successful collaboration; a business deal; falling in love perhaps; avoid a cheat or a stab in the back; even finding something to talk about so as to rub out the boredom of an early morning taxi stint.

The question “where are you from” is one that is at once blatantly superficial as well as purely human; important for our survival and for functioning as humans.

To deny the existence of the question in our mind is to not look at the mirror to see ourselves as we really are.

In the mean time, having spent nearly half of my life in America, having grown up in West Bengal, having spent significant time in Northern India and in Australia, I still imagine myself from a little village in Bangladesh, by the side of a pond, where the evening light falls slowly, a fog might rise above the dark water, a bell might ring in the temple of the family deity, muffled puffs on the conch shell might float across the water and hordes of spiraling mosquitoes might rise from the amorphophalous bush near my grandparents' gravestones and swarm over my head.

I had been to that spot only twice in my life, separated by a span of forty years.

If I wrote that yesterday was an interesting day with the Grand Finalists at the Intel Science Fair, it would be an understatement.

A veritable collection of near geniuses in their mid-to-late teens.

I also got to test a little question that has been bothering me for a while...the impact of society on science.

Here's how I tested it. I took the division of mathematics as the subject population because mathematicians on average reach their heights starting around 21 or 22 years, and these kids are within five or six years of reaching this age. So they are a good choice for assessing their future potentials. I classified some 64 Grand Finalists in mathematics into two groups: Pure Math and Applied Math.

In no other discipline the difference between pure and applied is as clear-cut. A pure math is nearly always recognizable from applied math as a different beast from a mile away--a distinction not usually possible in any other discipline of science.

There were 30 pure math, 29 applied math and 5 that I couldn't classify unambiguously, so I had to eliminate these 5.

Among the 30 pure math kids, 20 were from foreign countries (Russia, Bulgaria, Iraq, India, China, Sweden, Germany etc) and 10 from the USA.

Among the 29 applied math kids, only six were from foreign countries and the rest 23 all from the USA.

By Fisher's two-tailed exact test, the difference in the distribution between the two groups (Foreigners over-represented among Pure Math and USA being over-represented among Applied Math) is statistically highly significant (P =  0.0006).

So we in the US influence genius kids to become applied mathematicians and elsewhere they are influenced to become pure mathematicians.

I am not making any value judgement here, but the effect might be lamentable in some respects: at this rate US might run out of novel directions in mathematics for application to practice were it to be that the rest of the nations conspire to secretly hide the output of their pure mathematicians! In this connection, it is worth reading a brilliant editorial by Uncle Syd written 16 years ago: http://www.sciencemag.org/content/282/5393/1411.full

Why do journals no longer publish hypothesis without validation?

In the past journals regularly published hypotheses.

The first Watson and Crick paper was little more than a hypothesis (The first sentence of that paper was: "We wish to suggest a structure for the salt of deoxyribose nucleic acid (D.N.A.)."). So was the Corey-Pauling alpha-helix paper. 

The "one-gene, one-enzyme" paper by Beadle and Tatum was a hypothesis. The first DNA coding paper by Gamow was a hypothesis. 

Although not formally published, Crick's famed tRNA paper, which introduced the "wobble-hypothesis" for the third anticodon position, which allowed deciphering of the genetic code, was a hypothesis, was widely circulated among the practitioners. 

The central dogma paper by Crick was a hypothesis. The so-called French-flag model of morphogen gradient in developmental biology was a hypothesis by Lewis Wolpert. Crick wrote a paper in Nature in early 1970s on the probable physical size limit of morphogens, which was entirely a hypothesis (no morphogen was yet identified). The proposal that eukaryotic chromosome ends must have a special structure (specifically, a hair-pin, which some 15 years later was discovered as telomeres) was a hypothesis advanced by Jim Watson in the late 1960s in Nature. In 1964, Robin Holliday proposed the now famous Holliday junction model of DNA recombination, which could be directly tested some 25 years after the publication of the hypothesis.  The second realistic model  of DNA recombination, the so-called Meselson-Radding model, published in PNAS was entirely hypothetical.

In other areas of science publishing hypothesis was the norm.  

The famous Bohr's paper on atomic theory was strictly speaking a hypothesis (consistent with past data), the general theory of relativity was a hypothesis (proved a few years later by observing the bending of light past the sun during a complete solar eclipse). Schroedinger's equation paper was a hypothesis (it can't be derived). Plank's famous paper that introduced quantization of energy was a hypothesis. 

 I could go on and on. 

In recent times, journal editors and reviewers have generally and unintentionally conspired together to not publish hypothesis without validation, because of impact factor considerations.  A hypothesis without validation is hard to evaluate, and so it is risky for a journal to publish because it might be proved wrong. If proved wrong, the article would not be cited further, and this should lower the journal's impact factor rating.

For example, in early 1970s,there was a paper published in Nature entitled "A quantum mechanical muscle model" (by CWF McClare), which proposed that actin and myosin molecules generate force through a quantum mechanical "resonance" process, which turned out to be untestable (not incorrect, mind you), and was hardly cited (the untimely death by suicide of the author due to mental depression might have also contributed to the paper being not much cited, however). 

This does not necessarily need to be the case.  For example, the Meselson-Radding model of DNA recombination turned out to be incorrect in general (though there are some specific cases wherein it is likely true), and yet was widely cited because this (ultimately incorrect) model prompted a flurry of experimental and theoretical investigations.  

As Carl Popper, the preeminent philosopher of modern science, has shown (See, "Conjectures and Refutations" by Popper), hypotheses that are proven wrong are more useful hypotheses for the progress of science than are hypotheses that are difficult to test. 

So when the mud settles, we might look back to this age and conclude that the current journal trends might indeed have impeded the progress of science!