Guest Post by Peter Brass on the new NSF guidelines

Peter Brass is a prior NSF theory director. He has written an intelligent guest post on the new NSF guidelines that we present here.

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You have received many mails regarding the proposed OMB Uniform Guidance for federal grant making. It is a very long document. So far the OMB has received more than 32,000 comments, which nominally all will be read and taken into account, but actually most are general comments along the lines of “this will destroy scientific research,” and unspecific comments will achieve nothing. You can sample the comments, they are public, and you will be disappointed: much heat but no illumination.

It is my aim to bring a bit more content to the debate, and encourage you to file comments, but on specific issues, in my opinion primarily on the passage against “promoting anti-American Values,” which might indeed be a catch-all for political pressure on research. Otherwise there is little new: reviews were always only advisory.

The relevant section is https://www.federalregister.gov/d/2026-10817/p-amd-155, the revision of section 200.205, especially section (b) “pre-issuance review,” which states (underline and boldface mine):

(b) Pre-issuance review. As part of the merit review process, Federal agencies must perform pre-issuance reviews to ensure that Federal award proposals selected for funding are consistent with applicable law, Federal agency priorities, and the national interest. In doing so, Federal agencies heads must designate one or more senior appointees to conduct a pre-issuance review of all discretionary awards. As part of this pre-issuance review for discretionary awards, senior appointees (or their designee) must, as relevant and to the extent consistent with applicable law, apply the following principles when reviewing Federal award proposals:

This is followed by a list of criteria, but to summarize this: The agency head (a presidential appointment) designates a senior appointee (possibly a presidential appointment) who might designate another person inside the agency, who checks all intended awards that certain criteria are satisfied. This is not really different from the state before 2025: any proposal I proposed for award needed to be checked by the division director.

So if there is a problem, it must be in the criteria. It turns out there might be, depending on the interpretation.

1. Discretionary awards must, where applicable, demonstrably advance the President’s policy priorities.
   Whether this is applicable is the agency’s decision.
2. Discretionary awards must not be used to fund, promote, encourage, subsidize, or facilitate:
   • (i) Racial preferences or other forms of racial discrimination by the recipient, including activities where race or intentional proxies for race will be used as a selection criterion for employment or program participation;
   • (ii) Denial by the recipient of the sex binary in humans or the notion that sex is a chosen or mutable characteristic;
   • (iii) Illegal immigration; or
   • (iv) Any other initiatives that compromise public safety or promote anti-American values.
   That last one is a catch-all, which can indeed be used by politics to suppress research. This is the passage against which we should protest.
3. All else being equal, preference for discretionary awards should be given to institutions with lower indirect cost rates.
   All else is never equal, but it is in the program director’s discretion to consider how much funding actually reaches science.
4. Discretionary awards should be given to a broad range of recipients. Research grants should be awarded to a mix of recipients likely to produce immediately demonstrable results and recipients with the potential for potentially longer-term, breakthrough results, in a manner consistent with the notice of funding opportunity.
   I am quite happy about this explicit recognition of the importance of basic research.
5. In performing activities under Federal awards, applicants should commit to complying with administration policies, procedures, and guidance respecting Gold Standard Science.
   It is fairly unclear what Gold Standard Science is, but that seems to affect only lab or simulation sciences with results that are advisory to politics.
6. Discretionary awards should include benchmarks for measuring success and progress towards relevant goals and, as relevant for awards pertaining to scientific research, a commitment to achieving Gold Standard Science.
   Same. For us the benchmark measuring success is publication, so no news.
7. To the extent institutional affiliation is considered in making discretionary awards, agencies should prioritize an institution’s commitment to rigorous, reproducible scholarship over its historical reputation or perceived prestige. For science grants, agencies should prioritize institutions that have demonstrated success in implementing Gold Standard Science.
   No news either. We should not give extra credit to a proposal coming from a famous institution. Of course that is in the discretion of the program director.

(c) Procedure for pre-issuance review. When conducting a pre-issuance review, senior appointees (or their designee) must not ministerially ratify or routinely defer to the recommendations of others, but must instead use their independent judgment when evaluating Federal award proposals.

(so the designee should actually look at the proposal before approving the award)

(d) Use of peer review. Nothing in this part must be construed to discourage or prevent the use of peer review methods to evaluate proposals for discretionary awards or otherwise inform agency decision making, provided that peer review recommendations remain advisory and are not ministerially ratified, routinely deferred to, or otherwise treated as de facto binding by senior appointees or their designees. Further, nothing in this part must be construed to create any rights to any particular level of review or consideration for any funding applicant except as consistent with applicable law.

(good news: nothing changes)

(e) Agency discretion to reissue funding opportunities. A Federal agency is not required to issue a discretionary award as a result of a NOFO if doing so would fund low-quality proposals or be inconsistent with the principles of this part. The agency may, at its discretion, repost a funding opportunity.

(so the agency can decide not to make any award)

This is the entire section which causes so much outcries. I believe that the proposed budget cuts are a much larger danger to research than these recommended OMB rules.

The Zone

When you start thinking deeply about a mathematics problem you may enter the "zone", a period of intense focus where you think solely about the problem and potential solutions, and more importantly block out all other thoughts and even lose track of time. Mathematicians don't own the zone, actors, musicians, athletes and many others have their own version of the zone. But for math, when working on an open problem, you have no idea how difficult a solution may be, or if a solution exists at all. Most of the time you will fail (if not you need to try harder problems). Failure is not wasted time. You may find a counterexample, a partial solution and always you will come out with a better understanding of the problem. Then days, months or years later, some new proof idea comes from a paper, a talk or just the back of your head, and back in the zone you go. And when you do succeed you get a feeling not unlike scoring a goal in a soccer game. You should see my proof dance.

With AI generated and assisted proofs, we may think of outsourcing the zone to ChatGPT and Claude. We may prove more and stronger theorems, but you'll understand the results just a little bit less and mathematics will become a little less fun. 

The New Result on Off-diagonal Ramsey Numbers

(All references in this blog post can be found in the main article the post is about which is here.)

Recall that \(R(s,k)  \) is the least \(n\) so that, for all 2-colorings of the edges of \(K_n\), there is either a RED \(s\)-clique or a BLUE \(k\)-clique.

\(R(k,k)\) has been well studied and is often called \(R(k)\).

However, today we are concerned with \(R(s,k)\) \(s\) is fixed and \(k\) goes to infinity.

1) In 1995 Jeong Han Kim showed \(R(3,k)\) is asy \(\Theta(\frac{k^2}{\log k})\). At the workshop In Ramsey Theory: Yesterday, Today, and Tomorrow, Edited by Alexander Soifer, 2011, Joel Spencer gave a great talk titled

80 years of \(R(3,k)\).

The title implies that the problem was open for 80 years, but 40 years is a better estimate.

The general sense I got from both Joel and the audience is that \(R(4,k)\) is a much harder problem.

2) In 2023 there was substantial progress on \(R(4,k)\). Sam Mattheus and Jacques Verstraete showed

\(R(4,k) = \Omega(\frac{k^3}{\log^4 k})\)

Combined with prior results this yields

\( c_1 \frac{k^3}{\log^4 k} \le R(4,k) \le c_2 \frac{k^3}{\log^2 k} \)

The prior lower bound had been \(\Omega(\frac{k^{2.5}}{\log^2 k })\) so this was a big improvement.

3) The following was known for \(R(s,k)\) for fixed \(s\) and asy \(k\):

\( k^{(s+1)/2 + o(1)} \le R(s,k) \le O(k^{s-1}) \)

There were polylog improvements to this which we omit.

Surely improving the lower bound to \(\Omega(k^{s-1+o(1)})\) would be hard.

4) Domagoj Bradac showed the following (posted on May 27, 2026, and pointed to at the beginning of this post):

\(R(s,k) =\Omega(\frac{k^{s-1}}{(\log k)^{2s-4}}).\)

Combining this with the known best upper bounds yields (ignoring constants) 

\( \frac{k^{s-1}}{(\log k)^{2s-4}} \le R(s,k) \le (1+o(1))\frac{k^{s-1}}{(\log k)^{s-2}} \)

So the bounds are now only a polylog apart!

Random Notes.

1) I am not surprised by the results.

2) I am very surprised that the results were obtained.

3a) In 2011 most people thought that \(R(4,k)\) would be hard.

3b) After the progress on \(R(4,k)\) I still thought that \(R(5,k)\) would be hard, though I don't know what others thought.

4) Why such fast progress?

4a) AI? Some was used but not that much. See comment on page 4 of the paper.

4b) More people working on the problem?

4c) More advanced tools developed over time? Surely yes.

5) What other problem that seems like its solution is long in coming will be solved much faster than we think?

The Tech of Silk Road

Last week I saw a talk by Northwestern professor Nina Wieda on the history of the Silk Road, a network of trading routes across Asia active from the second century BCE until the mid-15th century. I knew of the Silk Road but was surprised by how much it used and fostered various technologies. 

It started with a technology that allowed for traveling long distances with limited access to water, better known as a camel. Travel was slow, it could take nearly two years to get from one end (modern day Turkey) to the other (China). Cities grew along the way for travelers and their protection, and for trading.

The travelers did not just carry silk and other materials for trade, the routes became an information superhighway of a sort. Religions spread including Buddhism, early Christianity and Manichaeism, an old religion that mostly divided the world into good and evil. Artistic style and influences spread as well with motifs like halos and winged figures appearing across widely separated cultures. Traders needed to converse in several languages, and documents could have several translations. 

The roads carried knowledge about technology itself from astronomy, calendars, medical information, geography and mathematical methods in text translated among the many languages. Travelers brought scribes that created a written language for Mongolian among others. Chinese papermaking made its way west reducing the cost of recording and transmitting information. Gunpowder technology also made its way into Europe from the east. 

A confluence of technologies helped hasten the end of the Silk Road. Marco Polo wrote about his travels along the Silk Road at the end of the 13th century but the account spread more widely with the advent of the printing press in the 15th century. The book inspired explorers who used advances in ship building and navigation to find water routes to the East (and Christopher Columbus to try a western route). These water routes would prove a faster cheaper way to ship goods between Europe and East Asia. The technological revolution shrunk or shuttered cities that used to host traders on the Silk Road. On the other hand, wealthy European traders would help fund and usher in the Renaissance. 

The Silk Road harkens to a time where, for the most part, people, goods and information travelled along the same routes at the same speed. Large cargo still moves fastest by ship, people by airplanes and information via wires and satellite nearly instantaneously.

When we think of technological disruption we think of the industrial revolution or even what's happening today, but the Silk Road reminds us that disruption has always been a part of the world's history, for bad and for good. 

mnemonic devices and pangrams that could be real sentences

A mnemonic device is a sentence where the first letters of the words are helpful to remember something. My favorite one is

                              My Very Educated Mother Just Said Uh, No Pluto

You probably know what it's for. If not you can type it into Google and you will find:

                             Mercury, Venus, Earth, Mars, Jupiter, Saturn, Uranus, Neptune

and of course NOT Pluto anymore. 

Consider 

                              Kids Prefer Cheese Over Fried Green Tomatoes

Again, if you just google that sentence you will find that it is a mnemonic for biological taxonomy: 

                          Kingdom, Phylum, Class, Order, Family, Genus, Species

I came across the mnemonic device

                          Do Men Ever Visit Brighton Beach?

The place I read this did not say what it was a mnemonic device  for. So I typed it into Google and found out:

Yes, they do! If you are refereeing to the Metropolitan museum of Art (The Met) staff, researchers or groups from New York, they frequently travel to the seaside city of Brighton, England, or its coastal namesake Brighton Beach, NY, for educational trips, research, and cultural exchange.

(This is word for word--- so any misspelled words odd phrasing is from Google, not from Bill.) 

What happened? Most mnemonic devices are not sentences you would say in normal conversation. This one is! So what to do? I Googled

                  What is ``Do Men Ever Visit Brighton Beach' a mnemonic device  for? 

It is British nobility:

                           Duke, Marquess, Earl, Viscount, Baron, Baronet.

1) Are there other mnemonics that are sentences one might actually say?  I asked Google and the Google AI overviews gave me the following:

Sam's Horse Must Eat Oats.  This is a mnemonic for the great lakes. 

A big secret conceal her past. This is a mnemonic for the last names of King Henry the 8th's wives. Not quite last names- Catherine of Aragon is regarded as having Aragon for a last name. Bonus: By looking all this up I found out that 3 of the 6 wives has first names that sounded the same: Catherine of Aragon, Catherine Howard, and Katherine Parr. So, he had a type. He had 2 of his 6 wives killed, so 1/3 and he had 1 of the 3 C/K atherine's killed, also 1/3. 

Big gorillas eat hotdogs, not cold pizza. Really? I thought they liked cold pizza. I am more surprised that Google thinks someone might say this sentence in normal conversation, and not just when they are trying to remember the countries of Central America: Belize, Guatemala, El Salvador, Honduras, Nicaragua, Costa Rica, Panama. 

2) I would normally see if Chatty or  Claude does better, but at this point I'm beating a dead horse (maybe Sam's).

3) My favorite pangram (sentences with every letter) that one might actually say is (or was- you'll see why)

                     Watch Jeopardy!---Alex Trebek's fun TV quiz game

Maybe put `show' at the end to make it more something someone might say- or would have said before Alex Trebek passed away.

Google AI overview game me those below. Are they sentences people would say? I leave that as an exercise for the reader 

                         Jim quickly realized that those beautiful gowns are expensive   

                         The quick onyx goblin jumps over the lazy dwarf. 

                         (I uttered that sentence just the other day!)

                         Just keep examining every low bid quoted for zinc etchings.

                         (This was a reasonable sentence until the word  etchings.)

                       Bored? Craving a pub quiz fix? Why, just come to the Royal Oak!  

                       (I prefer the Alex Trebek pangram more.)   

4) Google AI overview seems to not quite know what a sentence one might actually say is.

Respect the P v NP Problem

There are two ways to look at the P v NP problem, as a formal mathematically defined conjecture as a Clay Millennium Prize Problem, and as the more intuitive notion that everything efficiently verifiable is efficiently computable and the implications that has on our ability to compute.

I've written considerably about how artificial intelligence has affected the latter. In particular, how AI and other advances in computing have brought us to this Optiland of getting most of the good implications of P=NP while our cryptographic codes remain unbreakable. 

But now with the recent advances in AI-created and assisted proofs, will AI change what we know about the formal mathematical statement? Is an AI-generated proof of P ≠ NP around the corner?

No, it isn't.  I do not believe we will see a P v NP proof in my lifetime proven by man or machine, separately or working together.

While the disproof of the Erdős unit distance problem is an impressive AI achievement, keep in mind that for every AI math proof there are hundreds of problems that we have tried to solve with AI where we haven't seen progress. And there is a huge chasm between Erdős combinatorial conjectures and the Clay Millennium problems. AI will continue to improve, but there are limits.

People, particularly those outside of computational complexity, don't realize how difficult a mathematical challenge this is. Polynomial-time algorithms can work in strange and mysterious ways. They don't have to respect the semantics of an NP-search problem or do any searching at all. Bill gave me the following "algorithm" for clique: Take the eigenvalues of the adjacency matrix. For all we know, if there are two primes p and q such that the pth eigenvalue and the qth eigenvalue differ by more than 1/k then the graph has a k-clique. Of course this doesn't work. But to prove P ≠ NP, you need to prove not only that this algorithm doesn't work, but neither do any of the infinitely other potential algorithms for solving NP-complete problems.

We simply know of no way to manage general polynomial-time algorithms other than by simulating them. We know by relativization that simulation and diagonalization will not work to settle P v NP. Other attempts to understand polynomial time, like circuit complexity, proof complexity and algebraic geometry have gotten bogged down well below the full power of polynomial-time. At this time we don't even have a viable approach to settling the P v NP problem.

Don't waste your time trying a formal approach via Lean. (I'm looking at you Dmitry Khanukov) Computational complexity is very messy to formulate technically. I can't get an AI willing to give me a full Lean-verified proof of something trivial like P closed under complement, forget the PCP theorem. If someone or something does come up with a P ≠ NP, it'll be following the right intuitive approach, not a formalistic one.

At least start with something simpler, like showing BPP is in subexponential time, or SAT doesn't have quadratic algorithms. You won't succeed there either, even though these questions should be galactically simpler than P ≠ NP. 

Humans Solve Erdos Problem!!

(In 2008 I wrote a survey of some of the known sum-product theorems, see here. Avi Wigderson has a great slide-set on sum-product theorems and their applications---the slides are on Avi's webpage of talks he has given (all the talks are excellent) which is here. I had a prior post on sum-product theorems here) 

If \(A\) is a set then let

\(A+A = \{ x+y \ \colon\  x,y\in A \} \),     \(A\cdot A = \{ xy \ \colon \  x,y\in A \} \).

Let \(A= \{1,\ldots,n\} \).

\(|A+A| = \Theta(n)  \) which is small. 

What about \(|A\cdot A|\)?  By the prime number theorem there are  \( \Theta(\frac{n}{\log n}) \) primes in \(A\) hence \(|A\cdot A| \ge \Omega(\frac{n^2}{\log^2n})\) by taking product of two primes. 

Or better: look at  \(\{ xy \ \colon \ x \hbox{ a prime in } \{n/2,\ldots,n\} , y \in \{1,\ldots,n/2\} \} \).

This is a subset of  \( A\cdot A \) of size  \( \Omega( \frac{n^2}{\log n} ) \). 

Ford improved this to 

\[ |A\cdot A| = \Theta\biggl (\frac{n^2}{(\log n)^a(\log\log n)^{3/2}} \biggr ) \]

 

 where  \(a=1-\frac{1+\ln\ln 2}{\ln 2} \sim 0.086\). So \(|A\cdot A|\) is Large! (Ford's paper is here.) 

Let \(A= \{2^1,\ldots,2^n\} \).

\(|A+A| = \Theta(n^2) \). Large!  \(|A\cdot A| = \Theta(n)  \). Small! 

Is it always the case that, for \(A\) a finite subset of numbers, \(\max(|A+A|,|A\cdot A|)\) is large?

In 1976 Erdős made a series of conjectures:

For all \(A\subset N\), A finite, \(\max(|A+A|,|A\cdot A|) \ge |A|^{2-o(1)} \).

For all \(A\subset Z\), A finite,  \(\max(|A+A|,|A\cdot A|) \ge  |A|^{2-o(1)}\).

For all \(A\subset R\), A finite,  \(\max(|A+A|,|A\cdot A|) \ge |A|^{2-o(1) } \).

For all  \(A\subset C\), A finite, \(\max(|A+A|,|A\cdot A|) \ge  |A|^{2-o(1) }\). 

Even though there are four of them (plural) these have come to be called The Sum-Product Conjecture (singular).

The paper appeared in the Israel Journal of Math and is oddly titled: Problems and results on 3-progressions and related topics. (I could not find this paper online- if you can, email me the pointer or pdf.)

In 1983 Erdős and Szemerédi made progress on the conjecture for \(Z\) by showing the following two theorems (they combine it into one).

1) There exists \(c>0\) such that, for all \(n\),  for all \(A\subset  Z \), \(|A|=n\), \( n^{1+c} < \max\{|A+A|,|A\cdot A|\}\). The \(c\) was very small. In my survey I present a sequence of results where \(c\) gets bigger and bigger. More has been found since my survey, see the Wikipedia page on Sum-Product Theorems here. 

2) There exists \(d>0\) such that, for all \(n\), there exists \(A\subset Z\), \(|A|=n\), such that  \(\max\{|A+A|,|A\cdot A|\}  < n^2\exp(\frac{-c\log n}{\log\log n})\).  

The paper appeared in a Studies in Pure Mathematics volume in memory of Paul Turán and is properly titled On sums and products of integers. The paper is here. For a sanity check I worked out that this is an improvements on Ford's result, so the set \(A\) in part 2 is better than \(\{1,\ldots,n\} \), see here.

Because of the two papers, the conjecture is sometimes attributed to Erdős and sometimes attributed to Erdős-Szemerédi.

Who should the conjecture be attributed to? It no longer matters since humans  found a counterexample to the conjecture! In particular Thomas Bloom, Will Sawin, Carl Schildkraut, and Dimitri Zhelezov found a counterexample for the conjecture over \(R\) (and hence also over \(C\)). Remarkably, they used a thought-to-be-obsolete tool called thinking. Their paper is here

The math world was shocked! An Erdős problem resolved by humans!  One Abel prize winner was quoted as saying We knew the day would eventually come when humans could resolve Erdős problems, but we didn't know it would come this soon!  Several math departments now have plans for workshops on Human Alignment.

MY POINTS

1) The Sum-Product conjecture is a well known and interesting conjecture, so this is not some obscure problem invented to make humans look good.

2) Human-generated or Human-assisted? The announcement claims that it  was mostly human. I tend to agree since if it was done by AI they wouldn't hide it, they'd brag about it (see my post: here).

3) AI may still be needed to clean up the proof. In the future, we will all use AI the way we currently use grad students, cleaning up what we do.

4) The final proof was readable. One concern about human proofs is that they would be unreadable and hard to verify. That was not the case here.

5) The ideas needed for the solution already existed; however:

a) The right combination was hard to find.

b) The relevant techniques used, algebraic number theory, are not standard tools in this field (What field is the sum-product conjecture in? If you have an opinion on this, leave a comment. Future blog topic: what dictates what field a conjecture is in? a theorem is in?)

c) It was widely believed that the Sum-Product conjecture was true.

d) Contrast the difficulty of the following two statements

The Sum-Product Conjecture over \(R\) is false. This requires finding a counterexample.

The Sum-Product Conjecture over \(N\) is true. If true this would require a proof that covers all finite subsets of \(N\).

The first statement seems easier to prove.

It would be of interest to see if humans can prove the Sum-Product Conjecture for \(N\).  Of course, it might not be true. In which case it would be even more impressive to prove it.  My undergraduates can not only prove \(\sqrt{2}\) and \(\sqrt{3}\) are irrational but also that \(\sqrt{4}\) is irrational.

6) In the short term, this result and what it portends, is good: math problems we care about will be resolved with the help of humans, perhaps solely by humans.  But in the long run AI may lose the ability---or at least the patience---to do the proofs themselves.

See item 10-ONE for a counter thought.

7) Humans are good at combining known concepts.  Are they good at coming up with new ones?  Is AI?  The distinction between combining known ideas and coming up with new ideas is thin.

8) Two contrary lessons:

a) AI's should know many fields of mathematics so that they can use ideas from one field in another, like the humans did.

b) AI should know some field of math really well so they may do something new in it that current humans could not have done.

Whether the AI's choose to do (a) or (b) they should also spend time attending seminars they do not understand, as humans do.

9) If  humans produce a new treatment for cancer that is better than what is known, we will not care that humans did it (though AI will check it).  Is mathematics similar? Do we care that a human did it?  Medicine values outcomes; mathematics also values understanding.

10) I suggest two futures:

ONE: While this human-generated (or human-assisted) result is impressive, it will be a rare occurrence. This result was actually a counterexample. The needed math was known. The result was interesting. This is a perfect storm that we might not see again for a while.

TWO: Paul Erdős lived in an earlier time before AI helped us do math.  Without the help of AI (or even computers really) he made conjectures, solved some of them with thinking, collaborated with others (before email! before Zoom!) who also used thinking.  Does the recent resolution of the sum-product conjecture mean we are going back to that time? A time when people actually had to think? For some that is a dream, for others a nightmare.

The Industrialization of Academic Research

Yesterday, National Academy of Sciences President Marcia McNutt delivered her last annual State of the Sciences Address. Overall the talk basically calls us to adapt to the new reality that industrial and foundation support for research has taken a far larger role in academic research. I fear we may be losing the broad academic independence and exploration that has made our universities the envy of the world.

Government funding for research has become more challenging to receive, more bureaucratic and more political. The US Office of Management and Budget has proposed new regulations that would require all grant funding to go through political review. 

McNutt presented this graph showing the seemingly exponential growth in research requirements from zero in 1990 (when I got my first grant) to well over three hundred today. Researchers now spend close to half their time on regulatory compliance. 

Increase in regulatory requirements

McNutt mentioned six specific issues.

1. Reimagine connections between universities and industry
2. Realign the academic reward system
3. Meet the needs of the STEM workforce
4. Reduce research regulations (as in the graph above)
5. Increase the rate of innovation with the help of automation in shared facilities
6. Tackle Big Challenges

For the first, she highlighted U Washington CS where a large percentage of their faculty had half-time appointments in industry. I understand why this is necessary from both sides, but that doesn't mean it's a good thing. Academics should not have two masters. We become academics to choose our own research directions and to prepare the next generation, both more challenging when you spend half your time connected with a company. 

Even for the "big challenges" she specifically mentioned two foundation-driven projects.

McNutt has hopes that government research can be less bureaucratic and willing to take bigger risks. Outside of theoretical areas, CS conferences now have heavy industrial representation. I worry that we bend our research to fit the needs of corporations and foundations funded by wealthy donors. Welcome to the new world.