MIT graduate student Alex Kachkine once spent nine months meticulously restoring a damaged baroque Italian painting, which left him plenty of time to wonder if technology could speed things up. Last week, MIT News announced his solution: a technique that uses AI-generated polymer films to physically restore damaged paintings in hours rather than months. The research appears in Nature.

Kachkine's method works by printing a transparent "mask" containing thousands of precisely color-matched regions that conservators can apply directly to an original artwork. Unlike traditional restoration, which permanently alters the painting, these masks can reportedly be removed whenever needed. So it's a reversible process that does not permanently change a painting.

"Because there's a digital record of what mask was used, in 100 years, the next time someone is working with this, they'll have an extremely clear understanding of what was done to the painting," Kachkine told MIT News. "And that's never really been possible in conservation before."

Figure 1 from the paper. Credit: MIT

Nature reports that up to 70 percent of institutional art collections remain hidden from public view due to damage—a large amount of cultural heritage sitting unseen in storage. Traditional restoration methods, where conservators painstakingly fill damaged areas one at a time while mixing exact color matches for each region, can take weeks to decades for a single painting. It's skilled work that requires both artistic talent and deep technical knowledge, but there simply aren't enough conservators to tackle the backlog.

The mechanical engineering student conceived the idea during a 2021 cross-country drive to MIT, when gallery visits revealed how much art remains hidden due to damage and restoration backlogs. As someone who restores paintings as a hobby, he understood both the problem and the potential for a technological solution.

To demonstrate his method, Kachkine chose a challenging test case: a 15th-century oil painting requiring repairs in 5,612 separate regions. An AI model identified damage patterns and generated 57,314 different colors to match the original work. The entire restoration process reportedly took 3.5 hours—about 66 times faster than traditional hand-painting methods.

Alex Kachkine, who developed the AI-printed film technique. Credit: MIT

Notably, Kachkine avoided using generative AI models like Stable Diffusion or the "full-area application" of generative adversarial networks (GANs) for the digital restoration step. According to the Nature paper, these models cause "spatial distortion" that would prevent proper alignment between the restored image and the damaged original.

Instead, Kachkine utilized computer vision techniques found in prior art conservation research: "cross-applied colouration" for simple damages like thin cracks, and "local partial convolution" for reconstructing low-complexity patterns. For areas of high visual complexity, such as faces, Kachkine relied on traditional conservator methods, transposing features from other works by the same artist.

From pixels to polymers

Kachkine's process begins conventionally enough, with traditional cleaning to remove any previous restoration attempts. After scanning the cleaned painting, the aforementioned algorithms analyze the image and create a virtual restoration that "predicts" what the damaged areas should look like based on the surrounding paint and the artist's style. This part isn't particularly new—museums have been creating digital restorations for years. The innovative part is what happens next.

Custom software (shared by Kachkine online) maps every region needing repair and determines the exact colors required for each spot. His software then translates that information into a two-layer polymer mask printed on thin films—one layer provides color, while a white backing layer ensures the full color spectrum reproduces accurately on the painting's surface. The two layers must align precisely to reproduce colors accurately.

High-fidelity inkjet printers produce the mask layers, which Kachkine aligns by hand and adheres to the painting using conservation-grade varnish spray. Importantly, the polymer materials dissolve in standard conservation solutions, allowing future removal of the mask without damaging the original work. Museums can also store digital files documenting every change made during restoration, creating a paper trail for future conservators.

Kachkine says that the technology doesn't replace human judgment—conservators must still guide ethical decisions about how much intervention is appropriate and whether digital predictions accurately capture the artist's original intent. "It will take a lot of deliberation about the ethical challenges involved at every stage in this process to see how can this be applied in a way that's most consistent with conservation principles," he told MIT News.

For now, the method works best with paintings that include numerous small areas of damage rather than large missing sections. In a world where AI models increasingly seem to blur the line between human- and machine-created media, it's refreshing to see a clear application of computer vision tools used as an augmentation of human skill and not as a wholesale replacement for the judgment of skilled conservators.

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Legend has it that physicist Ernest Rutherford once dismissed all sciences other than physics as mere "stamp collecting." (Whether he actually said it is a matter of some debate.) But we now live in the information age, and scientists have found tremendous value in amassing giant databases of information for large-scale analysis, enabling them to explore different kinds of questions.

The latest addition is the Marine Organizational Body Size (MOBS) database, an open-access resource that—as its name implies—has collected body size data for more than 85,000 marine animal species and counting, ranging from microscopic creatures like zooplankton to the largest whales. MOBS is already enabling new research on the ocean's biodiversity and global ecosystem, according to a paper published in the journal Global Ecology and Biogeography. The database is now available though GitHub and currently covers 40 percent of all described marine animal species, with a goal of achieving 75 percent coverage.

"We've really lacked that broader persecutive for a lot of ocean life," marine ecologist Craig McClain of the University of Louisiana at Lafayette told Ars. McClain is the lead creator of MOBS. "We know about evolution and ecology for mammals and birds especially, and to a lesser extent reptiles and amphibians. We just haven't had these big collated body size data sets for the marine groups, especially the invertebrates." The MOBS project is basically constructing a "library of [marine] life."

This in turn paves the way for research on macroecology and macroevolution for marine animals. As McClain likes to say, "Body size isn't just a number; it's a key to how life works," because that simple trait is related to "how a species moves, eats, survives, and evolves. What jellyfish do and their ecology is so much different than for a mammal that it makes you wonder how universal some of the [widely accepted] 'rules' really are."

The ocean runs on size

McClain officially launched MOBS as a passion project while on sabbatical in 2022 but he had been informally collecting data on body size for various marine groups for several years before that. So he had a small set of data already to kick off the project, incorporating it all into a single large database with a consistent set format and style.

Craig McClain holding a giant isopod (<em>Bathynomus giganteus</em>), one of the deep sea’s most iconic crustaceans Credit: Craig McClain

"One of the things that had prevented me from doing this before was the taxonomy issue," said McClain. "Say you wanted to get the body size for all [species] of octopuses. That was not something that was very well known unless some taxonomist happened to publish [that data]. And that data was likely not up-to-date because new species are [constantly] being described."

However, in the last five to ten years, the World Register of Marine Species (WoRMS) was established with the objective of cataloging all marine life, with taxonomy experts assigned to specific groups to determine valid new species, which are then added to the data set with a specific numerical code. McClain tied his own dataset to that same code, making it quite easy to update MOBS as new species are added to WoRMS. McClain and his team were also able to gather body size data from various museum collections.

The MOBS database focuses on body length (a linear measurement) as opposed to body mass. "Almost every taxonomic description of a new species has some sort of linear measurement," said McClain. "For most organisms, it's a length, maybe a width, and if you're really lucky you might get a height. It's very rare for anything to be weighed unless it's an objective of the study. So that data simply doesn't exist."

While all mammals generally have similar density, "If you compare the density of a sea slug, a nudibranch, versus a jellyfish, even though they have the same masses, their carbon contents are much different," he said. "And a one-meter worm that's a cylinder and a one-meter sea urchin that's a sphere are fundamentally different weights and different kinds of organisms." One solution for the latter is to convert to volume to account for shape differences. Length-to-weight ratios can also differ substantially for different marine animal groups. That's why McClain hopes to compile a separate database for length-to-weight conversions.

While body size might seem to be a relatively easy measurement to take, compared to something physiological like metabolic rate, when it comes to marine animals there can be unique challenges. McClain recalled working on a project to measure the length of whale sharks in the wild. "You can't just swim up and extend a tape measure down the length of a whale shark," he said.

The solution was admittedly convoluted, but it worked. "We would swim up to the shark, shoot a couple of laser dots on their side at known widths, and take pictures. We knew that the distance between their last gill and the dorsal fin scaled with total body length, so that's how we figured it out." By contrast, measuring the lengths of submillimeter organisms has typically involved taking multiple images under a microscope and conducting image analysis.

Scientists are already using MOBS to study things like whether the size-related patterns in descriptions of species in biodiversity databases might reflect longstanding biases, for example. But it also provides McClain and his team with a viable research focus at a time when federal funding for scientific research is facing an existential crisis.

"The other work I do, deep sea research, is very expensive and requires grant funding," said McClain. "The great thing about this project is that it only really requires me and a laptop and some other people and their laptops. It may be over the next few years, while there's no funding to do any other field or lab science, that this may be our main focus. At the end of the day, [MOBS] is going to require lots of people approaching this from a lot of different ways to get great coverage across all these different groups."

DOI: Global Ecology and Biogeography, 2025. 10.1111/geb.70062  (About DOIs).

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The following is an open letter from Global Tetrahedron CEO Bryce P. Tetraeder that was included with each copy of  The Onion that was sent to Congress.

If you are reading this, you are likely either a member of Congress or one of the many underlings tasked with prodding lawmakers from a senile haze when they must cast a vote. You may be wondering why you have lucked out and received a free issue of our storied publication without so much as inserting a rider into a bill classifying The Onion as a tax-free religious organization.

Simply put, the inaction of Congress has already made me happier than any legal loophole could.

As a titan of business, I find this nation’s descent into corruption and tyranny not simply a balm for my soul, but also a huge benefit to my bottom line. We are on the precipice of a new economic order, one in which affluent men like myself will be able to select their own tax rate from a drop-down menu. It’s a reality I barely dreamed possible just a few months ago.

But sending each member of Congress a copy of our vaunted reporting is more than just a token gesture of thanks for bringing about a future in which scions like myself are given unlimited influence over government and veto power over bike lanes. As we stand in the smoldering ruins of our democratic government, we at Global Tetrahedron LLC would be doing a disservice to our shareholders, their descendants, and their descendants’ thoroughbred horses if we didn’t take this opportunity to snatch up as much power and money as possible while the getting is good.

On that note, I invite you to peruse this issue and let it dictate your every action as you lead us forth into ruin. There’s no longer any need to pretend to read reports from fact-obsessed experts or listen to the drivel spewed by your half-wit constituents. The Onion is now your everything.

It is your sole guide, your lodestar, your universe. Burn all other newspapers. Drive their so-called journalists out into the cold. From here on out, America’s Finest News Source holds a monopoly on deciding what is best for our nation’s business interests, and therefore our nation.

As you’ll read in the piece I made my editorial board write while hovering over their shoulders and breathing my will into their ears, our country is slipping smoothly into the warm bath of authoritarianism and oligarchy. I wish I could take credit for this, and I will. But much of the praise must go to Congress and its cowardice. I ask you to stay the course and allow The Onion’s strong, steady arm to point the way. Your capitulation will be justly rewarded with glowing press coverage and the opportunity to borrow our paperboys to do with as you wish.

To the esteemed members of Congress, I say: Enjoy the paper. I look forward to seeing many of you at my annual orgy in one of the $500,000-per-head sex pits.

Infinite Influence Forever,

Bryce P. Tetraeder, Global Tetrahedron CEO

The post Why I’m Sending Issues of ‘The Onion’ To Every Member Of Congress appeared first on The Onion.

The drugs in development include a pill that a new trial suggests is about as effective as Ozempic.

Population Projections: The World’s Top Countries by 2100

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By the end of this century, global demographics could look dramatically different than they do today. While some countries will be growing, others will be in the midst of long-term declines.

Using the latest data from the UN’s 2024 World Population Prospects, this infographic visualizes how the world’s most populous countries are expected to change by 2100.

Animated Chart: Population Projections to 2100

This graphic is also animated! Check out the video below to see how these demographic shifts will play out over time.

Data & Discussion

The data we used to create this graphic is listed in the table below. According to these projections, India will be the most populous country in 2100, followed by China, Pakistan, and Nigeria.

Data in millions

Year	India	China	Pakistan	Nigeria	DRC	U.S.	Ethiopia	Indonesia
1950	342.6	539.2	35.5	36.9	12.2	153.1	17.5	68.1
1951	350.0	548.9	36.2	37.6	12.4	155.3	17.8	69.5
1952	357.8	558.6	37.0	38.4	12.7	157.6	18.2	71.1
1953	366.0	571.6	37.8	39.1	12.9	160.0	18.5	72.7
1954	374.2	583.5	38.7	39.8	13.2	162.4	18.9	74.4
1955	383.1	596.8	39.6	40.6	13.5	164.9	19.2	76.3
1956	392.3	610.3	40.6	41.3	13.8	167.5	19.6	78.3
1957	401.7	622.8	41.6	42.1	14.1	170.1	20.0	80.3
1958	410.8	637.9	42.7	42.9	14.4	172.8	20.3	82.5
1959	420.8	650.5	43.9	43.7	14.7	175.6	20.6	84.7
1960	430.8	654.9	45.1	44.6	15.1	178.7	21.1	87.1
1961	441.2	654.7	46.3	45.5	15.5	181.8	21.6	89.5
1962	451.9	656.9	47.5	46.5	15.9	185.1	22.2	92.1
1963	462.6	673.3	48.8	47.5	16.3	188.3	22.8	94.7
1964	473.7	695.8	50.1	48.5	16.7	191.3	23.4	97.4
1965	484.8	713.8	51.5	49.5	17.2	194.3	24.0	100.3
1966	495.5	733.0	52.9	50.6	17.7	197.0	24.6	102.5
1967	506.0	751.8	54.4	51.7	18.2	199.5	25.3	105.1
1968	517.2	770.3	56.0	52.8	18.7	201.9	26.0	107.9
1969	528.2	791.7	57.6	54.0	19.3	204.1	26.7	111.0
1970	539.5	812.6	59.3	55.2	19.9	206.5	27.4	114.1
1971	552.2	834.1	61.0	56.5	20.4	209.1	28.2	117.2
1972	564.0	854.1	62.7	57.9	21.0	211.7	29.0	120.4
1973	577.0	873.3	64.5	59.3	21.5	214.0	29.8	123.7
1974	590.0	891.9	66.4	60.8	22.1	216.0	30.6	126.9
1975	604.1	908.7	68.4	62.5	22.7	218.1	31.3	130.2
1976	618.5	923.5	70.5	64.3	23.3	220.2	32.1	133.5
1977	633.2	937.3	72.7	66.2	23.9	222.2	33.0	136.8
1978	648.4	949.8	75.1	68.3	24.5	224.3	33.4	140.2
1979	663.1	962.5	77.5	70.4	25.5	226.5	34.1	143.7
1980	679.2	976.1	80.5	72.6	26.3	228.7	34.4	147.2
1981	695.5	990.2	84.1	74.9	27.1	231.0	34.5	150.7
1982	711.9	1005.2	87.5	77.2	27.9	233.4	36.1	154.3
1983	728.7	1022.0	90.7	79.5	28.8	235.8	37.3	157.8
1984	746.0	1036.2	93.8	81.3	29.7	238.2	38.2	161.5
1985	763.7	1051.5	96.6	83.7	30.8	240.5	39.3	165.0
1986	781.6	1068.1	99.9	86.1	31.8	242.9	40.4	168.5
1987	799.8	1086.4	103.3	88.4	32.8	245.1	41.7	171.8
1988	818.1	1106.0	106.9	90.8	33.8	247.3	43.3	175.2
1989	836.6	1124.4	110.5	93.3	34.9	249.5	45.0	178.5
1990	855.5	1143.5	114.2	95.8	36.0	251.8	46.7	181.9
1991	874.5	1163.6	118.2	98.4	37.3	254.9	48.5	185.1
1992	893.4	1177.9	122.0	101.0	38.5	258.0	50.9	188.4
1993	912.5	1191.2	125.1	103.7	39.9	261.2	52.8	191.7
1994	931.7	1203.4	128.9	106.5	41.4	264.1	54.6	194.9
1995	950.6	1214.6	132.6	109.4	43.9	266.9	56.6	198.2
1996	970.0	1225.7	136.6	112.3	45.0	269.5	58.5	201.5
1997	989.4	1236.2	140.5	115.2	45.6	272.2	60.4	204.9
1998	1008.9	1246.1	144.5	118.3	46.5	274.8	62.4	208.2
1999	1028.4	1255.6	148.5	121.4	48.1	277.5	64.4	211.5
2000	1048.0	1264.7	152.6	124.7	49.7	280.1	66.4	214.6
2001	1067.8	1274.4	157.1	128.1	51.3	282.9	68.4	217.6
2002	1088.0	1283.0	161.4	131.6	52.9	285.7	70.5	220.6
2003	1107.2	1290.7	165.0	135.3	54.6	288.5	72.7	223.6
2004	1126.4	1298.3	169.2	139.1	56.1	291.3	74.9	226.5
2005	1145.6	1305.9	173.4	143.0	57.9	294.2	77.2	229.3
2006	1163.7	1314.2	177.5	147.0	59.7	297.2	79.5	232.4
2007	1182.0	1321.9	181.9	151.1	61.5	300.3	81.9	235.5
2008	1199.3	1329.7	187.1	155.4	63.4	303.4	84.3	238.6
2009	1216.5	1338.0	191.9	159.8	65.4	306.5	86.8	241.7
2010	1234.5	1347.1	196.9	164.3	67.5	309.5	89.2	244.8
2011	1252.5	1356.0	201.6	169.0	69.7	312.6	91.8	247.9
2012	1270.0	1364.5	205.9	173.8	72.0	315.6	94.5	251.1
2013	1287.4	1374.6	209.4	178.6	74.5	318.6	97.1	254.3
2014	1304.3	1383.4	212.7	183.5	77.1	321.6	99.8	257.4
2015	1320.2	1392.5	215.8	188.3	79.7	324.6	102.5	260.4
2016	1335.8	1399.8	218.8	193.0	82.4	327.6	105.3	263.2
2017	1352.1	1408.3	221.5	197.9	85.6	330.7	108.2	266.0
2018	1367.2	1416.4	225.0	202.6	88.6	333.7	111.2	268.7
2019	1382.1	1421.6	228.8	207.2	91.5	336.4	114.2	271.2
2020	1396.0	1425.4	232.8	211.7	94.4	339.1	117.3	273.7
2021	1409.3	1426.8	237.2	216.3	97.6	339.7	120.5	275.9
2022	1419.1	1426.1	241.7	220.8	100.7	340.6	123.7	277.6
2023	1431.7	1424.3	245.7	225.5	104.1	342.5	127.0	280.0
2024	1444.4	1420.9	249.3	230.3	107.5	344.5	130.4	282.4
2025	1457.4	1417.7	253.2	235.1	111.0	346.4	133.8	284.6
2026	1470.3	1414.5	257.2	240.0	114.6	348.2	137.2	286.8
2027	1483.0	1411.4	261.4	244.9	118.3	349.9	140.6	289.0
2028	1495.4	1408.0	265.7	249.9	122.0	351.6	144.1	291.0
2029	1507.5	1404.3	270.1	254.8	125.8	353.2	147.6	293.0
2030	1519.4	1400.3	274.6	259.9	129.6	354.9	151.1	294.9
2031	1530.9	1396.0	279.2	264.9	133.5	356.4	154.6	296.8
2032	1542.2	1391.5	283.9	269.9	137.4	358.0	158.1	298.6
2033	1553.1	1386.6	288.6	275.0	141.4	359.5	161.7	300.4
2034	1563.6	1381.5	293.4	280.1	145.4	361.0	165.2	302.1
2035	1573.8	1376.2	298.2	285.2	149.5	362.6	168.8	303.8
2036	1583.6	1370.6	303.1	290.2	153.6	364.0	172.3	305.4
2037	1593.0	1364.8	307.9	295.3	157.8	365.4	175.9	306.9
2038	1602.0	1358.8	312.8	300.3	161.9	366.9	179.4	308.4
2039	1610.6	1352.6	317.7	305.3	166.2	368.2	183.0	309.8
2040	1618.7	1346.2	322.5	310.2	170.4	369.6	186.6	311.2
2041	1626.5	1339.5	327.3	315.2	174.8	370.9	190.3	312.4
2042	1633.8	1332.5	332.2	320.0	179.1	372.1	193.9	313.6
2043	1640.7	1325.3	336.9	324.9	183.6	373.3	197.5	314.8
2044	1647.1	1317.9	341.7	329.6	188.0	374.5	201.2	315.8
2045	1653.2	1310.2	346.5	334.3	192.6	375.6	204.8	316.8
2046	1658.9	1302.1	351.2	339.0	197.2	376.6	208.5	317.7
2047	1664.2	1293.6	355.9	343.6	201.8	377.6	212.2	318.5
2048	1669.1	1284.7	360.5	348.1	206.4	378.6	215.9	319.2
2049	1673.6	1275.3	365.1	352.6	211.1	379.5	219.5	319.9
2050	1677.7	1265.5	369.6	357.0	215.9	380.4	223.2	320.5
2051	1681.5	1255.1	374.1	361.4	220.6	381.3	226.8	321.0
2052	1685.0	1244.3	378.6	365.7	225.4	382.1	230.5	321.4
2053	1688.1	1232.9	383.0	369.9	230.2	383.0	234.2	321.7
2054	1690.9	1221.0	387.3	374.1	235.1	383.8	237.8	322.0
2055	1693.3	1208.7	391.6	378.2	239.9	384.5	241.3	322.3
2056	1695.4	1196.0	395.9	382.3	244.8	385.3	244.9	322.4
2057	1697.2	1182.9	400.1	386.3	249.6	386.0	248.5	322.5
2058	1698.7	1169.5	404.3	390.2	254.5	386.8	252.0	322.6
2059	1699.8	1155.9	408.4	394.1	259.4	387.7	255.4	322.6
2060	1700.7	1142.1	412.4	397.9	264.2	388.5	259.0	322.6
2061	1701.2	1128.2	416.4	401.7	269.1	389.3	262.5	322.5
2062	1701.4	1114.2	420.4	405.3	274.0	390.2	265.9	322.4
2063	1701.2	1100.2	424.3	409.0	278.8	391.1	269.3	322.2
2064	1700.7	1086.3	428.1	412.5	283.6	392.0	272.7	322.0
2065	1699.9	1072.5	431.8	416.0	288.4	392.9	276.1	321.8
2066	1698.7	1058.8	435.5	419.4	293.2	393.8	279.3	321.6
2067	1697.2	1045.2	439.1	422.6	297.9	394.8	282.6	321.3
2068	1695.4	1031.7	442.7	425.9	302.7	395.8	285.9	321.0
2069	1693.2	1018.5	446.2	429.0	307.4	396.7	289.1	320.6
2070	1690.6	1005.3	449.6	432.0	312.1	397.7	292.2	320.2
2071	1687.8	992.3	452.9	434.9	316.7	398.7	295.3	319.9
2072	1684.6	979.3	456.1	437.8	321.3	399.7	298.4	319.4
2073	1681.0	966.5	459.2	440.5	325.9	400.6	301.5	319.0
2074	1677.2	953.6	462.3	443.2	330.4	401.5	304.5	318.5
2075	1673.1	940.9	465.2	445.7	334.9	402.4	307.6	318.0
2076	1668.6	928.1	468.1	448.2	339.4	403.4	310.6	317.4
2077	1664.0	915.3	470.9	450.5	343.8	404.3	313.5	316.8
2078	1659.0	902.5	473.6	452.8	348.2	405.1	316.4	316.2
2079	1653.8	889.6	476.1	454.9	352.5	405.9	319.2	315.6
2080	1648.4	876.7	478.5	457.0	356.8	406.7	321.9	314.9
2081	1642.7	863.8	480.9	458.9	361.0	407.5	324.6	314.2
2082	1636.8	850.9	483.2	460.7	365.2	408.3	327.2	313.5
2083	1630.8	838.0	485.5	462.4	369.4	409.0	329.9	312.7
2084	1624.5	825.2	487.6	464.0	373.5	409.8	332.4	311.9
2085	1618.1	812.4	489.6	465.5	377.5	410.5	334.9	311.1
2086	1611.5	799.8	491.5	466.9	381.5	411.2	337.4	310.2
2087	1604.8	787.3	493.4	468.2	385.4	411.9	339.8	309.4
2088	1598.0	775.0	495.1	469.4	389.2	412.6	342.1	308.5
2089	1591.0	762.8	496.8	470.4	393.0	413.4	344.4	307.5
2090	1583.9	750.8	498.4	471.4	396.7	414.1	346.5	306.6
2091	1576.7	739.0	499.9	472.3	400.3	414.8	348.8	305.6
2092	1569.4	727.4	501.4	473.1	403.8	415.6	350.9	304.6
2093	1562.1	715.9	502.9	473.9	407.3	416.3	353.0	303.6
2094	1554.6	704.5	504.2	474.5	410.7	417.0	355.0	302.6
2095	1547.1	693.3	505.5	475.0	414.0	417.8	357.0	301.5
2096	1539.6	682.2	506.7	475.5	417.2	418.5	359.0	300.5
2097	1532.0	671.2	507.8	475.9	420.3	419.1	361.0	299.4
2098	1524.4	660.2	508.8	476.2	423.4	419.8	362.9	298.3
2099	1516.8	649.4	509.7	476.5	426.4	420.4	364.7	297.2
2100	1509.1	638.7	510.6	476.7	429.3	421.0	366.4	296.1

India Population Projections

India recently overtook China to become the world’s most populous country, and it’s projected to continue growing until 2062, peaking at 1.7 billion people.

Birth rates in the country have actually been declining since the 1960s, but are currently at a relatively high 2 births per woman.

China Population Projections

China held the title of the world’s most populous country for many decades, but is now shrinking due to its rapidly aging population and falling fertility rates.

Based on these projections, China’s population will be 639 million in 2100, which is 788 million lower from its 2021 peak.

U.S. Population Projections

The U.S. is currently the world’s third biggest country, but is expected to fall to sixth place by 2100. Unlike many other developed countries, however, the U.S. should keep growing throughout this century.

Most of this growth will be due to immigration, rather than new births, as fertility rates in the U.S. are already below the replacement level.

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