The most pow­er­ful MacBook Pro ever is here. With the blaz­ing-fast M1 Pro or M1 Max chip — the first Apple sil­i­con de­signed for pros — you get ground­break­ing per­for­mance and amaz­ing bat­tery life. Add to that a stun­ning Liquid Retina XDR dis­play, the best cam­era and au­dio ever in a Mac note­book, and all the ports you need. The first note­book of its kind, this MacBook Pro is a beast.

Supercharged for pros.

The most pow­er­ful MacBook Pro ever is here. With the blaz­ing-fast M1 Pro or M1 Max chip — the first Apple sil­i­con de­signed for pros — you get ground­break­ing per­for­mance and amaz­ing bat­tery life. Add to that a stun­ning Liquid Retina XDR dis­play, the best cam­era and au­dio ever in a Mac note­book, and all the ports you need. The first note­book of its kind, this MacBook Pro is a beast.

Available start­ing 10.26

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Watch the film

View in AR

The most pow­er­ful MacBook Pro ever is here. With the blaz­ing-fast M1 Pro or M1 Max chip — the first Apple sil­i­con de­signed for pros — you get ground­break­ing per­for­mance and amaz­ing bat­tery life. Add to that a stun­ning Liquid Retina XDR dis­play, the best cam­era and au­dio ever in a Mac note­book, and all the ports you need. The first note­book of its kind, this MacBook Pro is a beast.

Supercharged for pros.

The most pow­er­ful MacBook Pro ever is here. With the blaz­ing-fast M1 Pro or M1 Max chip — the first Apple sil­i­con de­signed for pros — you get ground­break­ing per­for­mance and amaz­ing bat­tery life. Add to that a stun­ning Liquid Retina XDR dis­play, the best cam­era and au­dio ever in a Mac note­book, and all the ports you need. The first note­book of its kind, this MacBook Pro is a beast.

Available start­ing 10.26

Watch the event

Watch the film

View in AR

Up to 32GB of uni­fied mem­ory

Up to 64GB of uni­fied mem­ory

M1 Pro and M1 Max scale the amaz­ing M1 ar­chi­tec­ture to new heights — and for the first time, they bring a sys­tem on a chip (SoC) ar­chi­tec­ture to a pro note­book. Both have more CPU cores, more GPU cores, and more uni­fied mem­ory than M1. Along with a pow­er­ful Neural Engine for su­per­charged ma­chine learn­ing and up­graded me­dia en­gines with ProRes sup­port, M1 Pro and M1 Max al­low pros to do things they never could be­fore.

M1 Pro takes the ex­cep­tional per­for­mance of the M1 architecture to a whole new level for pro users. Even the most am­bi­tious pro­jects are eas­ily han­dled with up to 10 CPU cores, up to 16 GPU cores, a 16‑core Neural Engine, and ded­i­cated en­code and de­code me­dia en­gines that sup­port H.264, HEVC, and ProRes codecs.

M1 Max is the most pow­er­ful chip ever cre­ated for a pro note­book, with 10 CPU cores, up to 32 GPU cores, and a 16-core Neural Engine. It de­liv­ers two times faster graph­ics pro­cess­ing and dou­ble the mem­ory band­width of M1 Pro. And it has a ded­i­cated me­dia en­gine for de­code and two for en­code — with up to two times faster video en­cod­ing — and two ProRes ac­cel­er­a­tors for even higher mul­ti­stream per­for­mance.

The new MacBook Pro is avail­able in 14- and 16-inch mod­els. Each can be con­fig­ured with the M1 Pro or M1 Max chip and of­fers un­prece­dented lev­els of pro per­for­mance. So you can ma­nip­u­late mil­lions of poly­gons in Cinema 4D, edit up to seven streams of 8K ProRes video in Final Cut Pro, or grade color in HDR on 8K 4x4 ProRes video — all miles away from the edit bay.

Ferocious per­for­mance with game‑chang­ing bat­tery life — that ef­fi­ciency is the magic of Apple sil­i­con. A sin­gle charge lets you com­pile up to four times as much code in Xcode or edit im­ages for up to twice as long in Lightroom Classic. And un­like other note­books, MacBook Pro de­liv­ers the same amaz­ing per­for­mance whether it’s plugged in or not.

The coolest part. Advanced ther­mal sys­tems move 50 percent more air, even at lower fan speeds. And thanks to the ef­fi­ciency of Apple sil­i­con, the fans never turn on for many tasks you do every day.

Fast. And vast. Get jaw-drop­ping read speeds from the up to 8TB SSD — up to 7.4GB/s or two times the pre­vi­ous gen­er­a­tion. So you can open 8K videos in­stantly or store hun­dreds of thou­sands of RAW pho­tos at once.

The new MacBook Pro is the first to put a sys­tem on a chip (SoC) into a pro note­book. Other pro sys­tems use power-hun­gry CPUs, dis­crete GPUs, and mul­ti­ple chips, each work­ing sep­a­rately. M1 Pro and M1 Max com­bine the CPU, GPU, I/O, and Neural Engine in a sin­gle SoC with uni­fied mem­ory. As a re­sult, M1 Pro and M1 Max not only crush in­ten­sive work­flows that were once im­pos­si­ble on a note­book — they also pro­vide in­cred­i­ble bat­tery life.

Connected at the chip. Other pro note­books need to copy data back and forth over a slower in­ter­face. Not the new MacBook Pro. Its CPU and GPU share a sin­gle pool of uni­fied mem­ory. That means every part of the chip con­nects to data and mem­ory with­out need­ing to copy it, so every­thing you do is faster and more ef­fi­cient.

M1 Pro and M1 Max CPUs each lever­age up to eight high-per­for­mance cores and two high-ef­fi­ciency cores to de­liver faster pro­cess­ing at a tenth of the power. Their GPUs have ac­cess to lower-la­tency data with vastly im­proved power ef­fi­ciency for un­matched per­for­mance per watt.

Apple has of­fi­cially an­nounced its most pow­er­ful chips ever: the M1 Pro and M1 Max, souped-up ver­sions of the M1 chip that it de­buted last fall and the heart of its new MacBook Pro mod­els.

The orig­i­nal M1 chip was an­nounced a lit­tle less than a year ago as Apple’s first in-house, Arm-based chip for lap­tops. At launch, it was fea­tured in Apple’s re­vamped MacBook Air, 13-inch MacBook Pro, and the en­try-level Mac Mini, in ad­di­tion to the 2021 iMac and iPad Pro re­freshes.

But as good as the M1 chip was, it was only a so­lu­tion for re­plac­ing Intel on Apple’s en­try-to-mid level hard­ware, with its high-end MacBook Pro, iMac, and Mac Mini mod­els (meant for de­vel­op­ers, pro­gram­mers, graphic de­sign­ers, and other more de­mand­ing work­loads) all no­tably still stick­ing with Intel’s chips for the last year.

The M1 Pro and M1 Max are Apple’s long-awaited an­swer to that is­sue: while both are built on the same 5nm process, Apple is promis­ing big jumps in per­for­mance here.

For the M1 Pro, Apple promises 70 per­cent bet­ter CPU per­for­mance and twice the graph­ics per­for­mance com­pared to the M1. While the ba­sic ar­chi­tec­ture is still the same on the M1 Pro, Apple is up­ping the hard­ware here in a big way, with a 10-core CPU that of­fers eight per­for­mance cores and two ef­fi­ciency cores, along with a 16-core GPU with 2,048 ex­e­cu­tion units.

The new chip also sup­ports more RAM, with con­fig­u­ra­tion op­tions up to 32GB (although, like the M1, mem­ory is still in­te­grated di­rectly into the chip it­self, in­stead of user-upgrad­able) with 200GB/s mem­ory band­width. In to­tal, the M1 Pro has 33.7 bil­lion tran­sis­tors, roughly twice that num­ber of tran­sis­tors that the M1 has.

But Apple is­n’t stop­ping there: it also an­nounced the even more pow­er­ful M1 Max, which has the same 10-core CPU con­fig­u­ra­tion, with eight per­for­mance cores and two ef­fi­ciency cores. But the M1 Max dou­bles the mem­ory band­width (to 400GB/s), RAM (up to 64GB of mem­ory) and GPU (with 32 cores, 4,096 ex­e­cu­tion units and four times the GPU per­for­mance of the orig­i­nal M1.) The M1 Max fea­tures 57 bil­lion tran­sis­tors, mak­ing it the largest chip that Apple has made yet. The new chip also means that you can con­nect up to four ex­ter­nal dis­plays to a sin­gle de­vice.

For com­par­i­son, the orig­i­nal M1 of­fered a clas­sic Arm mix of eight CPU cores: four per­for­mance cores for more de­mand­ing tasks and four high-ef­fi­ciency cores for ex­tend­ing bat­tery life. The M1 also of­fered ei­ther a seven-core or eight-core GPU, de­pend­ing on the com­puter model, and only al­lowed ei­ther 8GB or 16GB of RAM.

Apple also promised that the new M1 Pro and M1 Max chips of­fer up to 1.7 times bet­ter CPU per­for­mance per watt com­pared to both the reg­u­lar M1 and un­spec­i­fied four-core and eight-core PC lap­top chips — al­though the com­pany did­n’t give hard num­bers, or iden­tify what other chips it was com­par­ing things to, mak­ing it dif­fi­cult to as­cer­tain just how the new chips will hold up in the real world.

Developing… we’re adding more to this post, but you can fol­low along with our MacBook Pro event live blog to get the news even faster.

Cloudflare launched on September 27, 2010, and every year since, it has made it a point to cel­e­brate “Birthday Week” with a raft of launches. By far, the show-stop­per this year was the an­nounce­ment of R2 Storage, an S3-compatible Object Storage ser­vice that di­rectly takes aim at AWS’ “Hotel California” busi­ness model. This has been ex­tremely well re­ceived, go­ing by the re­sponse on HN and Twitter. In its past 5 birth­days, Cloudflare has gone from world-class CDN to of­fer­ing:

…and de­clar­ing that they will be “the fourth ma­jor pub­lic cloud”. When your mar­ket cap is $36 bil­lion and your next biggest com­peti­tor is worth $1.6 tril­lion (~45x larger, al­beit not pure-play), this is a bold state­ment. Many star­tups are try­ing by of­fer­ing spe­cial­ized Cloud Distros, but all build­ing with AWS as the pre­sump­tive win­ner of the “first layer cloud” rather than try­ing to com­pete.

My re­al­iza­tion: The big 3 clouds are play­ing Chess, but Cloudflare is play­ing Go.

While the tech in­dus­try is used to come-from-be­low dis­rup­tion, and the soft­ware in­dus­try is in­creas­ingly grasp­ing class-for-the-masses atomic con­cepts, I be­lieve Cloudflare is writ­ing a new play­book that is the lit­tle-guy coun­ter­part of the em­brace, ex­tend, ex­tin­guish model used by Microsoft.

Because it in­volves API com­pat­i­bil­ity, this play­book is par­tic­u­larly rel­e­vant to de­vel­oper tools, and is pro­tected by the Supreme Court rul­ing in Google v Oracle. If I were to sum­ma­rize it in three words, look­ing over Cloudflare’s his­tory and an­nual re­port, I might call it:

Establish: Establish a foothold in some­thing in­cum­bents don’t care enough about

Envelop: Reverse-proxy some­thing that in­cum­bents don’t serve cus­tomers well on

Expand: cross-sell other pre­mium prod­ucts and ser­vices un­til they are more cus­tomers of you than they are cus­tomers of the in­cum­bent.

Given Cloudflare’s fun­da­men­tally less-cen­tral­ized ap­proach to grow­ing its cloud, it is no sur­prise that it an­nounced its first Ethereum prod­uct this Birthday Week; al­though it re­mains to be seen if a Web2-native com­pany can re­ally drop enough of its as­sump­tions to han­dle Web3 threats or op­por­tu­ni­ties. If we are truly in the “early Internet” days of Web3, only the para­noid might sur­vive here. Fortunately, Prince seems to be a vo­cal fan of Andy Grove as well.

[ The fol­low­ing ar­ti­cle, along with com­ments from Smartsound and Shockwave-Sound, was writ­ten by Tony Fleming and pub­lished with his per­mis­sion. ]

Larry, you have helped me out in the past with prob­lems but this is not one of those sit­u­a­tions. It is some­thing of which I think you need to be aware, as­sum­ing you are not al­ready.

I am re­spon­si­ble for nearly all the 144 videos up­loaded on the You Tube chan­nel for Flemingyachts.com. We have just un­der 65,000 sub­scribers and sev­eral of the videos have well over 1,000,000 views. The videos range from a few min­utes to just over one hour.

I get many com­ments every day and many of them say they are the best they have watched on You Tube for the pho­tog­ra­phy, story telling, nar­ra­tion and mu­sic. I men­tion all of this not be­cause I have a swollen head but to prove con­text and to show that we are not a fly-by-night out­fit.

I have al­ways pur­chased all the mu­sic in all my videos from le­git­i­mate sources. eg: Smartsound, Shockwave-sound, Magnatune, and Pond5. My videos are now be­ing at­tacked by a bliz­zard of copy­right claims from peo­ple who are claim­ing copy­right on pretty well every piece of mu­sic on pretty well every video posted on You Tube. Things re­ally came to a head in the last cou­ple days over one par­tic­u­lar video which runs for 1 hour and 2 min­utes. This video was made and posted in 2009.

There was a claim made against a piece of mu­sic which ran 81 sec­onds. The mu­sic was orig­i­nally pur­chased un­der the ti­tle of Irish Reel from SmartSound. The iden­ti­cal track for which copy­right is be­ing claimed has been re-named Kilfenora Reels. I filled in the five page dis­pute which was re­jected by the claimant and You Tube told me that if I did­n’t re­move the mu­sic/​video within one week they would is­sue a copy­right strike against our Youtube ac­count. I had been re­ceiv­ing com­plaints in the com­ments about the amount of ads in this video so I watched it and found that You Tube had vir­tu­ally de­stroyed it with 12 ad­ver­tis­ing video seg­ments in­ter­rupt­ing the play­ing of my video in ad­di­tion to six lower third dis­play ads and, of course some ad­ver­tis­ing rub­bish be­fore the video would start play­ing! So You Tube and the ad­ver­tis­ers are mak­ing rev­enue off my video from these ads.

Larry adds: Note that Anthony has to ap­peal the take-down no­tice NOT to an un­bi­ased third-party, but to the per­son mak­ing the orig­i­nal claim. This is nei­ther fair, nor likely to pre­vent ad­di­tional abuse. A fraud­u­lent claimant has every rea­son to be un­help­ful.

I de­cided to re­move the 81 sec­onds of dis­puted mu­sic by im­port­ing the MP4 ver­sion into Final Cut and do­ing some mi­nor edit­ing. I then ex­ported the re­vised ver­sion. I deleted the ex­ist­ing ver­sion of the video and up­loaded the new ver­sion with a dif­fer­ent URL and a slightly mod­i­fied ti­tle — A Single Step. Venture II in Europe (Revised).

The re­sult was that the re­vised video had 5 new copy­right claims even be­fore up­load pro­cess­ing was com­plete! I haven’t checked but I be­lieve that is every piece of mu­sic in the video. You Tube won’t even pub­lish the video on line un­til all the copy­right claims have been set­tled. They say that mon­e­ti­za­tion will go to the copy­right claimants. I know that all the mu­sic came from Smartsound.com be­cause the orig­i­nal video was made in 2009 and I was­n’t pur­chas­ing mu­sic from any other source at that time. The ti­tles for each mu­sic track has been changed so it is a mis­er­able, time-con­sum­ing task to try to match the new ti­tle with the orig­i­nal from among hun­dreds of mu­sic tracks. I have dis­puted every one of the claims but I know that will lead nowhere — ex­cept re­newed threats.

So now You Tube has ef­fec­tively made it im­pos­si­ble even to ad­just to al­ready-phony copy­right claims. Copyright claimers clearly au­to­mat­i­cally tar­get every new video up­loaded — es­pe­cially the longer ones — in the hope that that the cre­ator will find it too oner­ous to dis­pute every track of mu­sic in every video and they will be able to ben­e­fit from ad rev­enue from some­one else’s work. The fact that to go to the trou­ble of pur­chas­ing le­git mu­sic from le­git sources does not pro­vide pro­tec­tion from these scams places the busi­ness model of those con­tent providers at risk.

For my­self, I have made the de­ci­sion that I will never again up­load a video onto YouTube. From now on I will stay 100% with Vimeo but, as we know, they have a frac­tion of the view­er­ship of You Tube — pri­mar­ily be­cause so few peo­ple know they even ex­ist.

I’m sorry this is so long. As I said, I don’t ex­pect you to ac­tu­ally do any­thing but I did want you to know just had bad the sit­u­a­tion has be­come.

Tony reached out to Bjorn Lynne with Shockwave-Sound and 1SoundFX.com, who replied:

Hi, Tony. I un­der­stand your sit­u­a­tion. And that’s frus­trat­ing. Nobody fore­saw this Content-ID stuff 15 years ago.

At least, if it’s mu­sic you got from us at Shockwave-Sound, I can help you and I have never not been suc­cess­ful in get­ting a claim re­leased for a cus­tomer.

If you are in doubt about whether you got a piece of mu­sic from us, you can al­ways con­tact me and I should be able to find out for you. You can also log into your user-ac­count at Shockwave-Sound and find a list of all your past or­ders there.

Thanks a lot for your mail. I ex­plained the mat­ter to BMG and they told me they would di­rectly re­lease that video… due to the fact that the mu­sic it­self is pub­lic do­main and the claim was prob­a­bly a false pos­i­tive.

So, I won­der why they never re­leased it?

Please pro­vide me with the de­tails on that claim (screenshots, e.g.) of all mu­sic tracks within one of your videos so we can hunt this down — it is sim­ply not ac­cept­able that YouTube/3rd par­ties claim what­ever they want and we wil­let your chan­nel on their al­lowlist and ask them to re­move that con­tent from Content ID.

Recently, we even ex­pe­ri­enced sound ef­fects get­ting claimed, which is against all YouTube poli­cies. We also started in­volv­ing our law firm to get those claims dis­puted as it seems to be the Wild West in a way.

Many thanks for your prompt re­ply. I will try to at­tach every­thing I can re the lat­est five claims. The prob­lem is that, as in the case with Irish Reel, the claimant changes the name of the mu­sic. Previously I have had to re­sort to record­ing each piece of sus­pect mu­sic on my iPhone and then com­pare with hun­dreds of tracks in my mu­sic li­brary try­ing to find a match. This can take hours and, frankly, I have grown weary of it!

I should tell you that we have never been in­ter­ested in mon­e­tiz­ing our videos. We hated the idea of crappy ads ap­pear­ing be­fore the video played. A few months ago, YouTube said they were go­ing to place ads on all videos whether the cre­ator wanted them or not. After suf­fer­ing this for a while, we made the de­ci­sion to mon­e­tize our videos on the ba­sis that if some­one was go­ing to earn money from our videos (like it or not) we may as well get some of it. Our videos have been mon­e­tized for August and September but we made the de­ci­sion yes­ter­day to re­verse this de­ci­sion.

The prob­lem is that I don’t be­lieve this will solve the cur­rent prob­lem be­cause we ex­pe­ri­enced it — al­beit to a lesser de­gree — be­fore we mon­e­tized.

Larry adds: Thanks, Tony, for shar­ing your let­ter. I agree, this is an in­creas­ingly scary sit­u­a­tion. I did not re­al­ize how YouTube as­sumes that every claim is le­git­i­mate and then makes it very, very dif­fi­cult for film­mak­ers who prop­erly pur­chased rights to the mu­sic they use to get their videos ap­proved.

It is ridicu­lous that YouTube has you con­tact the per­son mak­ing the claim for re­dress. As if that will de­ter scam­mers. In­stead, it seems YouTube takes the easy way out by re­mov­ing all videos, then com­pound­ing the prob­lem by of­fer­ing in­vis­i­ble cus­tomer ser­vice for cre­ators to fix prob­lems.

For other film­mak­ers read­ing this, PLEASE pre­vent prob­lems when post­ing your movies:

* Use only mu­sic for which you pur­chased the rights

* Track every piece of mu­sic in your pro­gram, in­clud­ing ti­tle, source and time­code lo­ca­tion

* Learn how to ap­peal a take-down rul­ing from YouTube

Feel free to share your ex­pe­ri­ences — and so­lu­tions — in the com­ments be­low.

Bookmark the perma­link.

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on your first pur­chase.

Johns Hopkins Medicine was awarded a grant from the National Institutes of Health (NIH) to ex­plore the po­ten­tial im­pacts of psilo­cy­bin on to­bacco ad­dic­tion. This is the first NIH grant awarded in over a half cen­tury to di­rectly in­ves­ti­gate the ther­a­peu­tic ef­fects of a clas­sic psy­che­delic, con­sis­tent with a re­cent study pub­lished on­line that searched NIH fund­ing and found zero grants were awarded be­tween 2006 and 2020. Johns Hopkins Medicine will lead the mul­ti­site, three-year study in col­lab­o­ra­tion with University of Alabama at Birmingham and New York University. The study will be con­ducted si­mul­ta­ne­ously at the three in­sti­tu­tions to di­ver­sify the pool of par­tic­i­pants and in­crease con­fi­dence that re­sults ap­ply to a wide range of peo­ple who smoke. The grant, to­tal­ing nearly $4 mil­lion, is funded by NIH’s National Institute on Drug Abuse.

Johns Hopkins Medicine was awarded a grant from the National Institutes of Health (NIH) to ex­plore the po­ten­tial im­pacts of psilo­cy­bin on to­bacco ad­dic­tion. This is the first NIH grant awarded in over a half cen­tury to di­rectly in­ves­ti­gate the ther­a­peu­tic ef­fects of a clas­sic psy­che­delic, con­sis­tent with a re­cent study pub­lished on­line that searched NIH fund­ing and found zero grants were awarded be­tween 2006 and 2020. Johns Hopkins Medicine will lead the mul­ti­site, three-year study in col­lab­o­ra­tion with University of Alabama at Birmingham and New York University. The study will be con­ducted si­mul­ta­ne­ously at the three in­sti­tu­tions to di­ver­sify the pool of par­tic­i­pants and in­crease con­fi­dence that re­sults ap­ply to a wide range of peo­ple who smoke. The grant, to­tal­ing nearly $4 mil­lion, is funded by NIH’s National Institute on Drug Abuse.

“The his­tor­i­cal im­por­tance of this grant is mon­u­men­tal,” says prin­ci­pal in­ves­ti­ga­tor Matthew Johnson, Ph. D., Susan Hill Ward Professor in Psychedelics and Consciousness in the Department of Psychiatry and Behavioral Sciences at the Johns Hopkins University School of Medicine. “We knew it was only a mat­ter of time be­fore the NIH would fund this work be­cause the data are so com­pelling, and be­cause this work has demon­strated to be safe. Psilocybin does have very real risks, but these risks are squarely mit­i­gated in con­trolled set­tings through screen­ing, prepa­ra­tion, mon­i­tor­ing and fol­low-up care.”

Over the last 20 years, there has been a grow­ing re­nais­sance of re­search with clas­sic psy­che­delics, which are the phar­ma­co­log­i­cal class of com­pounds that in­cludes psilo­cy­bin and LSD. These stud­ies have been largely funded by phil­an­thropy, re­sult­ing in im­pres­sive clin­i­cal find­ings for can­cer-re­lated ex­is­ten­tial dis­tress, ma­jor de­pres­sive dis­or­der and sub­stance use dis­or­ders.

Johnson ini­ti­ated this line of re­search test­ing psilo­cy­bin for to­bacco smok­ing ces­sa­tion 13 years ago. A pi­lot study pub­lished in 2014 showed very high ab­sti­nence rates, much larger than those seen with tra­di­tional smok­ing ces­sa­tion med­ica­tions and ther­a­pies.

The cur­rent dou­ble-blind ran­dom­ized trial in­volves psilo­cy­bin ses­sions as well as cog­ni­tive be­hav­ioral ther­apy — a type of talk ther­apy (psychotherapy) fo­cused on pin­point­ing neg­a­tive pat­terns of thought that can lead to be­hav­ioral and men­tal health prob­lems. The re­searchers sug­gest psilo­cy­bin might help break the ad­dic­tive pat­tern of thoughts and be­hav­iors that has be­come in­grained af­ter years of smok­ing, thus help­ing peo­ple to quit the habit.

Psilocybin, a com­pound found in so-called magic mush­rooms, pro­duces vi­sual and au­di­tory il­lu­sions and pro­found changes in con­scious­ness. Combined with prepa­ra­tion and struc­tured sup­port, psilo­cy­bin has shown promise for treat­ing a range of ad­dic­tions and men­tal health dis­or­ders.

Johnson is avail­able for in­ter­views.

This re­search is sup­ported by the National Institute on Drug Abuse of the National Institutes of Health un­der award num­ber U01DA052174. The con­tent is solely the re­spon­si­bil­ity of the au­thors and does not nec­es­sar­ily rep­re­sent the of­fi­cial views of the National Institutes of Health.

The wealth­i­est 10% of Americans now own 89% of all U. S. stocks held by house­holds, a record high that high­lights the stock mar­ket’s role in in­creas­ing wealth in­equal­ity.

The top 1% gained more than $6.5 tril­lion in cor­po­rate eq­ui­ties and mu­tual fund wealth dur­ing the Covid-19 pan­demic, while the bot­tom 90% added $1.2 tril­lion, ac­cord­ing to the lat­est data from the Federal Reserve. The share of cor­po­rate eq­ui­ties and mu­tual funds owned by the top 10% reached the record high in the sec­ond quar­ter, while the bot­tom 90% of Americans held about 11% of in­di­vid­u­ally held stocks, down from 12% be­fore the pan­demic.

The stock mar­ket, which has nearly dou­bled since the March 2020 drop and is up nearly 40% since January 2020, was the main source of wealth cre­ation in America dur­ing the pan­demic — as well as the main dri­ver of in­equal­ity. The to­tal wealth of the top 1% now tops 32%, a record, ac­cord­ing to the Fed data. Nearly 70% of their wealth gains over the past year and a half — one of the fastest wealth booms in re­cent his­tory — came from stocks.

“The top 1% own a lot of stock, the rest of us own a lit­tle,” said Steven Rosenthal, se­nior fel­low, Urban-Brookings Tax Policy Center.

The grow­ing con­cen­tra­tion of wealth comes de­spite mil­lions of new in­vestors com­ing into the stock mar­ket for the first time dur­ing the pan­demic, lead­ing to what many have la­beled “the de­moc­ra­ti­za­tion” of stocks. Robinhood added more than 10 mil­lion new ac­counts over the past two years and now has over 22 mil­lion — many of them held by younger, first-time in­vestors.

Yet while the mar­ket may be owned more broadly, the gains and wealth it cre­ates are not be­ing more widely dis­trib­uted. Rosenthal said that while the army of new in­vestors may be nu­mer­ous, they are also still small, with the av­er­age ac­count size at Robinhood at about $4,500. When mar­kets rise, they will have far smaller dol­lar gains than wealth­ier in­vestors with hun­dreds of thou­sands or even mil­lions in stock hold­ings.

“Many of the younger in­vestors also bought in at higher prices, com­pared to big­ger in­vestors who have been in the mar­ket for years and see larger gains,” Rosenthal said.

Also, many of the new in­vestors have more of a trad­ing men­tal­ity — buy­ing and sell­ing stocks rapidly, with lever­age, in hopes of quick gains. While the strat­egy can cre­ate big win­ners, oth­ers may see lower re­turns than those of in­vestors who sim­ply buy and hold for the long term.

The top 10% saw the value of their stocks gain 43% be­tween January 2020 and June of 2021, ac­cord­ing to the Fed. The bot­tom 90% saw stock wealth rise at a lower rate — 33%.

“They might ac­count for a larger share of trad­ing ac­tiv­ity, but that’s dif­fer­ent from own­er­ship and wealth,” Rosenthal said.

When you walk, your feet make con­tact with the ground. When you write, your fin­gers make con­tact with the pen. Physical con­tacts are what makes in­ter­ac­tion with the world pos­si­ble. Yet, for such a com­mon oc­cur­rence, con­tact is a sur­pris­ingly com­plex phe­nom­e­non. Taking place at mi­cro­scopic scales at the in­ter­face of two bod­ies, con­tacts can be soft or stiff, bouncy or spongy, slip­pery or sticky. It’s no won­der our fin­ger­tips have four dif­fer­ent types of touch-sen­sors. This sub­tle com­plex­ity makes sim­u­lat­ing phys­i­cal con­tact — a vi­tal com­po­nent of ro­bot­ics re­search — a tricky task.

The rich-yet-ef­fi­cient con­tact model of the MuJoCo physics sim­u­la­tor has made it a lead­ing choice by ro­bot­ics re­searchers and to­day, we’re proud to an­nounce that, as part of DeepMind’s mis­sion of ad­vanc­ing sci­ence, we’ve ac­quired MuJoCo and are mak­ing it freely avail­able for every­one, to sup­port re­search every­where. Already widely used within the ro­bot­ics com­mu­nity, in­clud­ing as the physics sim­u­la­tor of choice for DeepMind’s ro­bot­ics team, MuJoCo fea­tures a rich con­tact model, pow­er­ful scene de­scrip­tion lan­guage, and a well-de­signed API. Together with the com­mu­nity, we will con­tinue to im­prove MuJoCo as open-source soft­ware un­der a per­mis­sive li­cence. As we work to pre­pare the code­base, we are mak­ing MuJoCo freely avail­able as a pre­com­piled li­brary.

A bal­anced model of con­tact. MuJoCo, which stands for Multi-Joint Dynamics with Contact, hits a sweet spot with its con­tact model, which ac­cu­rately and ef­fi­ciently cap­tures the salient fea­tures of con­tact­ing ob­jects. Like other rigid-body sim­u­la­tors, it avoids the fine de­tails of de­for­ma­tions at the con­tact site, and of­ten runs much faster than real time. Unlike other sim­u­la­tors, MuJoCo re­solves con­tact forces us­ing the con­vex Gauss Principle. Convexity en­sures unique so­lu­tions and well-de­fined in­verse dy­nam­ics. The model is also flex­i­ble, pro­vid­ing mul­ti­ple pa­ra­me­ters which can be tuned to ap­prox­i­mate a wide range of con­tact phe­nom­ena.

Introducing M1 Pro and M1 Max: the most pow­er­ful chips Apple has ever built

Powering the all-new MacBook Pro, new chips fea­ture up to a 10-core CPU, 32-core GPU, 64GB of uni­fied mem­ory, ProRes acceleration, and in­dus­try-lead­ing power ef­fi­ciency

Apple to­day an­nounced M1 Pro and M1 Max, the next break­through chips for the Mac. Scaling up M1’s trans­for­ma­tional ar­chi­tec­ture, M1 Pro of­fers amaz­ing per­for­mance with in­dus­try-lead­ing power ef­fi­ciency, while M1 Max takes these ca­pa­bil­i­ties to new heights. The CPU in M1 Pro and M1 Max de­liv­ers up to 70 per­cent faster CPU per­for­mance than M1, so tasks like com­pil­ing pro­jects in Xcode are faster than ever. The GPU in M1 Pro is up to 2x faster than M1, while M1 Max is up to an as­ton­ish­ing 4x faster than M1, al­low­ing pro users to fly through the most de­mand­ing graph­ics work­flows.

M1 Pro and M1 Max in­tro­duce a sys­tem-on-a-chip (SoC) ar­chi­tec­ture to pro sys­tems for the first time. The chips fea­ture fast uni­fied mem­ory, in­dus­try-lead­ing per­for­mance per watt, and in­cred­i­ble power ef­fi­ciency, along with in­creased mem­ory band­width and ca­pac­ity. M1 Pro of­fers up to 200GB/s of mem­ory band­width with sup­port for up to 32GB of uni­fied mem­ory. M1 Max de­liv­ers up to 400GB/s of mem­ory band­width — 2x that of M1 Pro and nearly 6x that of M1 — and sup­port for up to 64GB of uni­fied mem­ory. And while the lat­est PC lap­tops top out at 16GB of graph­ics mem­ory, hav­ing this huge amount of mem­ory en­ables graph­ics-in­ten­sive work­flows pre­vi­ously unimag­in­able on a note­book. The ef­fi­cient ar­chi­tec­ture of M1 Pro and M1 Max means they de­liver the same level of per­for­mance whether MacBook Pro is plugged in or us­ing the bat­tery. M1 Pro and M1 Max also fea­ture en­hanced me­dia en­gines with ded­i­cated ProRes ac­cel­er­a­tors specif­i­cally for pro video pro­cess­ing. M1 Pro and M1 Max are by far the most pow­er­ful chips Apple has ever built.

“M1 has trans­formed our most pop­u­lar sys­tems with in­cred­i­ble per­for­mance, cus­tom tech­nolo­gies, and in­dus­try-lead­ing power ef­fi­ciency. No one has ever ap­plied a sys­tem-on-a-chip de­sign to a pro sys­tem un­til to­day with M1 Pro and M1 Max,” said Johny Srouji, Apple’s se­nior vice pres­i­dent of Hardware Technologies. “With mas­sive gains in CPU and GPU per­for­mance, up to six times the mem­ory band­width, a new me­dia en­gine with ProRes ac­cel­er­a­tors, and other ad­vanced tech­nolo­gies, M1 Pro and M1 Max take Apple sil­i­con even fur­ther, and are un­like any­thing else in a pro note­book.”

M1 Pro fea­tures 33.7 bil­lion tran­sis­tors — more than 2x the amount in M1. The chip can be con­fig­ured with up to 32GB of fast uni­fied mem­ory.

M1 Max is the largest chip Apple has ever built: 57 bil­lion tran­sis­tors and up to 64GB of fast uni­fied mem­ory.

A graph shows that M1 Max and its up-to-32-core GPU de­liv­ers the same graph­ics per­for­mance as that in a high-end com­pact PC pro lap­top while us­ing up to 40 per­cent less power.

A graph shows that M1 Max de­liv­ers sim­i­lar graph­ics per­for­mance as the largest PC lap­tops while us­ing up to 100 watts less power.

M1 Max has an up-to-32-core GPU that de­liv­ers graph­ics per­for­mance com­pa­ra­ble to that in a high-end com­pact PC pro lap­top us­ing up to 40 per­cent less power.

Compared with the high­est-end dis­crete GPU in the largest PC lap­tops, M1 Max de­liv­ers sim­i­lar graph­ics per­for­mance us­ing up to 100 watts less power.

Four Mac screens are shown with the new Apple sil­i­con.

M1, M1 Pro, and M1 Max chips are shown next to each other.

The Mac is now one year into its two-year tran­si­tion to Apple sil­i­con.

M1, M1 Pro, and M1 Max form a fam­ily of chips that lead the in­dus­try in per­for­mance, cus­tom tech­nolo­gies, and power ef­fi­ciency.

More than 10,000 Universal apps and plug-ins, in­clud­ing Adobe Photoshop (above), are avail­able to take ad­van­tage of the pow­er­ful M1 Pro and M1 Max.

The com­bi­na­tion of ma­cOS with M1, M1 Pro, or M1 Max also de­liv­ers in­dus­try-lead­ing se­cu­rity pro­tec­tions, in­clud­ing hard­ware-ver­i­fied se­cure boot, run­time anti-ex­ploita­tion tech­nolo­gies, and fast, in-line en­cryp­tion for files. All of Apple’s Mac apps are op­ti­mized for — and run na­tively on — Apple sil­i­con, and there are over 10,000 Universal apps and plug-ins avail­able. Existing Mac apps that have not yet been up­dated to Universal will run seam­lessly with Apple’s Rosetta 2 tech­nol­ogy, and users can also run iPhone and iPad apps di­rectly on the Mac, open­ing a huge new uni­verse of pos­si­bil­i­ties.

Introducing M1 Pro and M1 Max: the most pow­er­ful chips Apple has ever built

Powering the all-new MacBook Pro, new chips fea­ture up to a 10-core CPU, 32-core GPU, 64GB of uni­fied mem­ory, ProRes acceleration, and in­dus­try-lead­ing power ef­fi­ciency

CUPERTINO, CALIFORNIA Apple to­day an­nounced M1 Pro and M1 Max, the next break­through chips for the Mac. Scaling up M1’s trans­for­ma­tional ar­chi­tec­ture, M1 Pro of­fers amaz­ing per­for­mance with in­dus­try-lead­ing power ef­fi­ciency, while M1 Max takes these ca­pa­bil­i­ties to new heights. The CPU in M1 Pro and M1 Max de­liv­ers up to 70 per­cent faster CPU per­for­mance than M1, so tasks like com­pil­ing pro­jects in Xcode are faster than ever. The GPU in M1 Pro is up to 2x faster than M1, while M1 Max is up to an as­ton­ish­ing 4x faster than M1, al­low­ing pro users to fly through the most de­mand­ing graph­ics work­flows.

M1 Pro and M1 Max in­tro­duce a sys­tem-on-a-chip (SoC) ar­chi­tec­ture to pro sys­tems for the first time. The chips fea­ture fast uni­fied mem­ory, in­dus­try-lead­ing per­for­mance per watt, and in­cred­i­ble power ef­fi­ciency, along with in­creased mem­ory band­width and ca­pac­ity. M1 Pro of­fers up to 200GB/s of mem­ory band­width with sup­port for up to 32GB of uni­fied mem­ory. M1 Max de­liv­ers up to 400GB/s of mem­ory band­width — 2x that of M1 Pro and nearly 6x that of M1 — and sup­port for up to 64GB of uni­fied mem­ory. And while the lat­est PC lap­tops top out at 16GB of graph­ics mem­ory, hav­ing this huge amount of mem­ory en­ables graph­ics-in­ten­sive work­flows pre­vi­ously unimag­in­able on a note­book. The ef­fi­cient ar­chi­tec­ture of M1 Pro and M1 Max means they de­liver the same level of per­for­mance whether MacBook Pro is plugged in or us­ing the bat­tery. M1 Pro and M1 Max also fea­ture en­hanced me­dia en­gines with ded­i­cated ProRes ac­cel­er­a­tors specif­i­cally for pro video pro­cess­ing. M1 Pro and M1 Max are by far the most pow­er­ful chips Apple has ever built.

“M1 has trans­formed our most pop­u­lar sys­tems with in­cred­i­ble per­for­mance, cus­tom tech­nolo­gies, and in­dus­try-lead­ing power ef­fi­ciency. No one has ever ap­plied a sys­tem-on-a-chip de­sign to a pro sys­tem un­til to­day with M1 Pro and M1 Max,” said Johny Srouji, Apple’s se­nior vice pres­i­dent of Hardware Technologies. “With mas­sive gains in CPU and GPU per­for­mance, up to six times the mem­ory band­width, a new me­dia en­gine with ProRes ac­cel­er­a­tors, and other ad­vanced tech­nolo­gies, M1 Pro and M1 Max take Apple sil­i­con even fur­ther, and are un­like any­thing else in a pro note­book.”

M1 Pro: A Whole New Level of Performance and Capability

Utilizing the in­dus­try-lead­ing 5-nanometer process tech­nol­ogy, M1 Pro packs in 33.7 bil­lion tran­sis­tors, more than 2x the amount in M1. A new 10-core CPU, in­clud­ing eight high-per­for­mance cores and two high-ef­fi­ciency cores, is up to 70 per­cent faster than M1, re­sult­ing in un­be­liev­able pro CPU per­for­mance. Compared with the lat­est 8-core PC lap­top chip, M1 Pro de­liv­ers up to 1.7x more CPU per­for­mance at the same power level and achieves the PC chip’s peak per­for­mance us­ing up to 70 per­cent less power.1 Even the most de­mand­ing tasks, like high-res­o­lu­tion photo edit­ing, are han­dled with ease by M1 Pro.

M1 Pro has an up-to-16-core GPU that is up to 2x faster than M1 and up to 7x faster than the in­te­grated graph­ics on the lat­est 8-core PC lap­top chip.1 Compared to a pow­er­ful dis­crete GPU for PC note­books, M1 Pro de­liv­ers more per­for­mance while us­ing up to 70 per­cent less power.2 And M1 Pro can be con­fig­ured with up to 32GB of fast uni­fied mem­ory, with up to 200GB/s of mem­ory band­width, en­abling cre­atives like 3D artists and game de­vel­op­ers to do more on the go than ever be­fore.

M1 Max: The World’s Most Powerful Chip for a Pro Notebook

M1 Max fea­tures the same pow­er­ful 10-core CPU as M1 Pro and adds a mas­sive 32-core GPU for up to 4x faster graph­ics per­for­mance than M1. With 57 bil­lion tran­sis­tors — 70 per­cent more than M1 Pro and 3.5x more than M1 — M1 Max is the largest chip Apple has ever built. In ad­di­tion, the GPU de­liv­ers per­for­mance com­pa­ra­ble to a high-end GPU in a com­pact pro PC lap­top while con­sum­ing up to 40 per­cent less power, and per­for­mance sim­i­lar to that of the high­est-end GPU in the largest PC lap­tops while us­ing up to 100 watts less power.2 This means less heat is gen­er­ated, fans run qui­etly and less of­ten, and bat­tery life is amaz­ing in the new MacBook Pro. M1 Max trans­forms graph­ics-in­ten­sive work­flows, in­clud­ing up to 13x faster com­plex time­line ren­der­ing in Final Cut Pro com­pared to the pre­vi­ous-gen­er­a­tion 13-inch MacBook Pro.

M1 Max also of­fers a higher-band­width on-chip fab­ric, and dou­bles the mem­ory in­ter­face com­pared with M1 Pro for up to 400GB/s, or nearly 6x the mem­ory band­width of M1. This al­lows M1 Max to be con­fig­ured with up to 64GB of fast uni­fied mem­ory. With its un­par­al­leled per­for­mance, M1 Max is the most pow­er­ful chip ever built for a pro note­book.

M1 Pro and M1 Max in­clude an Apple-designed me­dia en­gine that ac­cel­er­ates video pro­cess­ing while max­i­miz­ing bat­tery life. M1 Pro also in­cludes ded­i­cated ac­cel­er­a­tion for the ProRes pro­fes­sional video codec, al­low­ing play­back of mul­ti­ple streams of high-qual­ity 4K and 8K ProRes video while us­ing very lit­tle power. M1 Max goes even fur­ther, de­liv­er­ing up to 2x faster video en­cod­ing than M1 Pro, and fea­tures two ProRes ac­cel­er­a­tors. With M1 Max, the new MacBook Pro can transcode ProRes video in Compressor up to a re­mark­able 10x faster com­pared with the pre­vi­ous-gen­er­a­tion 16-inch MacBook Pro.

Both M1 Pro and M1 Max are loaded with ad­vanced cus­tom tech­nolo­gies that help push pro work­flows to the next level:

Apple’s cus­tom im­age sig­nal proces­sor, along with the Neural Engine, uses com­pu­ta­tional video to en­hance im­age qual­ity for sharper video and more nat­ural-look­ing skin tones on the built-in cam­era.

A Huge Step in the Transition to Apple Silicon

The Mac is now one year into its two-year tran­si­tion to Apple sil­i­con, and M1 Pro and M1 Max rep­re­sent an­other huge step for­ward. These are the most pow­er­ful and ca­pa­ble chips Apple has ever cre­ated, and to­gether with M1, they form a fam­ily of chips that lead the in­dus­try in per­for­mance, cus­tom tech­nolo­gies, and power ef­fi­ciency.

ma­cOS and Apps Unleash the Capabilities of M1 Pro and M1 Max

ma­cOS Monterey is en­gi­neered to un­leash the power of M1 Pro and M1 Max, de­liv­er­ing break­through per­for­mance, phe­nom­e­nal pro ca­pa­bil­i­ties, and in­cred­i­ble bat­tery life. By de­sign­ing Monterey for Apple sil­i­con, the Mac wakes in­stantly from sleep, and the en­tire sys­tem is fast and in­cred­i­bly re­spon­sive. Developer tech­nolo­gies like Metal let apps take full ad­van­tage of the new chips, and op­ti­miza­tions in Core ML uti­lize the pow­er­ful Neural Engine so ma­chine learn­ing mod­els can run even faster. Pro app work­load data is used to help op­ti­mize how ma­cOS as­signs multi-threaded tasks to the CPU cores for max­i­mum per­for­mance, and ad­vanced power man­age­ment fea­tures in­tel­li­gently al­lo­cate tasks be­tween the per­for­mance and ef­fi­ciency cores for both in­cred­i­ble speed and bat­tery life.

The com­bi­na­tion of ma­cOS with M1, M1 Pro, or M1 Max also de­liv­ers in­dus­try-lead­ing se­cu­rity pro­tec­tions, in­clud­ing hard­ware-ver­i­fied se­cure boot, run­time anti-ex­ploita­tion tech­nolo­gies, and fast, in-line en­cryp­tion for files. All of Apple’s Mac apps are op­ti­mized for — and run na­tively on — Apple sil­i­con, and there are over 10,000 Universal apps and plug-ins avail­able. Existing Mac apps that have not yet been up­dated to Universal will run seam­lessly with Apple’s Rosetta 2 tech­nol­ogy, and users can also run iPhone and iPad apps di­rectly on the Mac, open­ing a huge new uni­verse of pos­si­bil­i­ties.

Today, Apple is car­bon neu­tral for global cor­po­rate op­er­a­tions, and by 2030, plans to have net-zero cli­mate im­pact across the en­tire busi­ness, which in­cludes man­u­fac­tur­ing sup­ply chains and all prod­uct life cy­cles. This also means that every chip Apple cre­ates, from de­sign to man­u­fac­tur­ing, will be 100 per­cent car­bon neu­tral.

Apple rev­o­lu­tion­ized per­sonal tech­nol­ogy with the in­tro­duc­tion of the Macintosh in 1984. Today, Apple leads the world in in­no­va­tion with iPhone, iPad, Mac, Apple Watch, and Apple TV. Apple’s five soft­ware plat­forms — iOS, iPa­dOS, ma­cOS, watchOS, and tvOS — pro­vide seam­less ex­pe­ri­ences across all Apple de­vices and em­power peo­ple with break­through ser­vices in­clud­ing the App Store, Apple Music, Apple Pay, and iCloud. Apple’s more than 100,000 em­ploy­ees are ded­i­cated to mak­ing the best prod­ucts on earth, and to leav­ing the world bet­ter than we found it.

Testing con­ducted by Apple in August and September 2021 us­ing pre­pro­duc­tion 16-inch MacBook Pro sys­tems with Apple M1 Max, 10-core CPU, 32-core GPU, and 64GB of RAM, and pre­pro­duc­tion 16-inch MacBook Pro sys­tems with Apple M1 Pro, 10-core CPU, 16-core GPU, and 32GB of RAM. Performance mea­sured us­ing se­lect in­dus­try‑stan­dard bench­marks. 8-core PC lap­top chip per­for­mance data from test­ing MSI GP66 Leopard (11UG-018). Performance tests are con­ducted us­ing spe­cific com­puter sys­tems and re­flect the ap­prox­i­mate per­for­mance of MacBook Pro.

Testing con­ducted by Apple in August and September 2021 us­ing pre­pro­duc­tion 16-inch MacBook Pro sys­tems with Apple M1 Max, 10-core CPU, 32-core GPU, and 64GB of RAM, and pre­pro­duc­tion 16-inch MacBook Pro sys­tems with Apple M1 Pro, 10-core CPU, 16-core GPU, and 32GB of RAM. Performance mea­sured us­ing se­lect in­dus­try‑stan­dard bench­marks. Discrete PC lap­top graph­ics per­for­mance data from test­ing Lenovo Legion 5 (82JW0012US). High-end dis­crete PC lap­top graph­ics per­for­mance data from test­ing MSI GE76 Raider (11UH-053). PC com­pact pro lap­top per­for­mance data from test­ing Razer Blade 15 Advanced (RZ09-0409CE53-R3U1). Performance tests are con­ducted us­ing spe­cific com­puter sys­tems and re­flect the ap­prox­i­mate per­for­mance of MacBook Pro.

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Testing con­ducted by Apple in August and September 2021 us­ing pre­pro­duc­tion 16-inch MacBook Pro sys­tems with Apple M1 Max, 10-core CPU, 32-core GPU, and 64GB of RAM, and pre­pro­duc­tion 16-inch MacBook Pro sys­tems with Apple M1 Pro, 10-core CPU, 16-core GPU, and 32GB of RAM. Performance mea­sured us­ing se­lect in­dus­try‑stan­dard bench­marks. 8-core PC lap­top chip per­for­mance data from test­ing MSI GP66 Leopard (11UG-018). Performance tests are con­ducted us­ing spe­cific com­puter sys­tems and re­flect the ap­prox­i­mate per­for­mance of MacBook Pro.

Testing con­ducted by Apple in August and September 2021 us­ing pre­pro­duc­tion 16-inch MacBook Pro sys­tems with Apple M1 Max, 10-core CPU, 32-core GPU, and 64GB of RAM, and pre­pro­duc­tion 16-inch MacBook Pro sys­tems with Apple M1 Pro, 10-core CPU, 16-core GPU, and 32GB of RAM. Performance mea­sured us­ing se­lect in­dus­try‑stan­dard bench­marks. Discrete PC lap­top graph­ics per­for­mance data from test­ing Lenovo Legion 5 (82JW0012US). High-end dis­crete PC lap­top graph­ics per­for­mance data from test­ing MSI GE76 Raider (11UH-053). PC com­pact pro lap­top per­for­mance data from test­ing Razer Blade 15 Advanced (RZ09-0409CE53-R3U1). Performance tests are con­ducted us­ing spe­cific com­puter sys­tems and re­flect the ap­prox­i­mate per­for­mance of MacBook Pro.

It’s com­mon to see peo­ple ad­vo­cate for learn­ing skills that they have or us­ing processes that they use. For ex­am­ple, Steve Yegge has a set of blog posts where he rec­om­mends read­ing com­piler books and learn­ing about com­pil­ers. His rea­son­ing is ba­si­cally that, if you un­der­stand com­pil­ers, you’ll see com­piler prob­lems every­where and will rec­og­nize all of the cases where peo­ple are solv­ing a com­piler prob­lem with­out us­ing com­piler knowl­edge. Instead of hack­ing to­gether some half-baked so­lu­tion that will never work, you can ap­ply a bit of com­puter sci­ence knowl­edge to solve the prob­lem in a bet­ter way with less ef­fort. That’s not un­true, but it’s also not a rea­son to study com­pil­ers in par­tic­u­lar be­cause you can say that about many dif­fer­ent ar­eas of com­puter sci­ence and math. Queuing the­ory, com­puter ar­chi­tec­ture, math­e­mat­i­cal op­ti­miza­tion, op­er­a­tions re­search, etc.

One re­sponse to that kind of ob­jec­tion is to say that one should study every­thing. While be­ing an ex­tremely broad gen­er­al­ist can work, it’s got­ten much harder to “know a bit of every­thing” and be ef­fec­tive be­cause there’s more of every­thing over time (in terms of both breadth and depth). And even if that weren’t the case, I think say­ing “should” is too strong; whether or not some­one en­joys hav­ing that kind of breadth is a mat­ter of taste. Another ap­proach that can also work, one that’s more to my taste, is to, as Gian Carlo Rota put it, learn a few tricks:

A long time ago an older and well known num­ber the­o­rist made some dis­parag­ing re­marks about Paul Erdos’ work. You ad­mire con­tri­bu­tions to math­e­mat­ics as much as I do, and I felt an­noyed when the older math­e­mati­cian flatly and de­fin­i­tively stated that all of Erdos’ work could be re­duced to a few tricks which Erdos re­peat­edly re­lied on in his proofs. What the num­ber the­o­rist did not re­al­ize is that other math­e­mati­cians, even the very best, also rely on a few tricks which they use over and over. Take Hilbert. The sec­ond vol­ume of Hilbert’s col­lected pa­pers con­tains Hilbert’s pa­pers in in­vari­ant the­ory. I have made a point of read­ing some of these pa­pers with care. It is sad to note that some of Hilbert’s beau­ti­ful re­sults have been com­pletely for­got­ten. But on read­ing the proofs of Hilbert’s strik­ing and deep the­o­rems in in­vari­ant the­ory, it was sur­pris­ing to ver­ify that Hilbert’s proofs re­lied on the same few tricks. Even Hilbert had only a few tricks!

If you look at how peo­ple suc­ceed in var­i­ous fields, you’ll see that this is a com­mon ap­proach. For ex­am­ple, this analy­sis of world-class judo play­ers found that most rely on a small hand­ful of throws, con­clud­ing

Judo is a game of spe­cial­iza­tion. You have to use the skills that work best for you. You have to stick to what works and prac­tice your skills un­til they be­come au­to­matic re­sponses.

If you watch an anime or a TV se­ries “about” fight­ing, peo­ple of­ten im­prove by in­creas­ing the num­ber of tech­niques they know be­cause that’s an easy thing to de­pict but, in real life, get­ting bet­ter at tech­niques you al­ready know is of­ten more ef­fec­tive than hav­ing a port­fo­lio of hun­dreds of “moves”.

One piece of ad­vice I got at some point was to am­plify my strengths. All of us have strengths and weak­nesses and we spend a lot of time talk­ing about ‘areas of im­prove­ment.’ It can be easy to feel like the best way to ad­vance is to elim­i­nate all of those. However, it can re­quire a lot of work and en­ergy to barely move the nee­dle if it’s truly an area we’re weak in. Obviously, you still want to make sure you don’t have any truly bad ar­eas, but as­sum­ing you’ve got­ten that, in­stead fo­cus on am­pli­fy­ing your strengths. How can you turn some­thing you’re good at into your su­per­power?

I’ve per­son­ally found this to be true in a va­ri­ety of dis­ci­plines. While it’s re­ally dif­fi­cult to mea­sure pro­gram­mer ef­fec­tive­ness in any­thing re­sem­bling an ob­jec­tive man­ner, this is­n’t true of some things I’ve done, like com­pet­i­tive video games (a very long time ago at this point, back be­fore there was “real” money in com­pet­i­tive gam­ing), the thing that took me from be­ing a pretty de­cent player to a very good player was aban­don­ing prac­tic­ing things I was­n’t par­tic­u­larly good at and fo­cus­ing on in­creas­ing the edge I had over every­body else at the few things I was un­usu­ally good at.

This can work for games and sports be­cause you can get bet­ter ma­neu­ver­ing your­self into po­si­tions that take ad­van­tage of your strengths as well as avoid­ing sit­u­a­tions that ex­pose your weak­nesses. I think this is ac­tu­ally more ef­fec­tive at work than it is in sports or gam­ing since, un­like in com­pet­i­tive en­deav­ors, you don’t have an op­po­nent who will try to ex­pose your weak­nesses and force you into po­si­tions where your strengths are ir­rel­e­vant. If I study queu­ing the­ory in­stead of com­pil­ers, a ri­val co-worker is­n’t go­ing to stop me from work­ing on pro­jects where queu­ing the­ory knowl­edge is help­ful and leave me fac­ing a field full of pro­jects that re­quire com­piler knowl­edge.

One thing that’s worth not­ing is that skills don’t have to be things peo­ple would con­sider fields of study or dis­crete tech­niques. For the past three years, the main skill I’ve been ap­ply­ing and im­prov­ing is some­thing you might call “looking at data”; the term is in quotes be­cause I don’t know of a good term for it. I don’t think it’s what most peo­ple would think of as “statistics”, in that I don’t of­ten need to do any­thing as so­phis­ti­cated as lo­gis­tic re­gres­sion, let alone ac­tu­ally so­phis­ti­cated. Perhaps one could ar­gue that this is some­thing data sci­en­tists do, but if I look at what I do vs. what data sci­en­tists we hire do as well as what we screen for in data sci­en­tist in­ter­views, we don’t ap­pear to want to hire data sci­en­tists with the skill I’ve been work­ing on nor do they do what I’m do­ing (this is a long enough topic that I might turn it into its own post at some point).

Unlike Matt Might or Steve Yegge, I’m not go­ing to say that you should take a par­tic­u­lar ap­proach, but I’ll say that work­ing on a few things and not be­ing par­tic­u­larly well rounded has worked for me in mul­ti­ple dis­parate fields and it ap­pears to work for a lot of other folks as well.

If you want to take this ap­proach, this still leaves the ques­tion of what skills to learn. This is one of the most com­mon ques­tions I get asked and I think my an­swer is prob­a­bly not re­ally what peo­ple are look­ing for and not very sat­is­fy­ing since it’s both ob­vi­ous and dif­fi­cult to put into prac­tice.

For me, two in­gre­di­ents for fig­ur­ing out what to spend time learn­ing are hav­ing a rel­a­tive ap­ti­tude for some­thing (relative to other things I might do, not rel­a­tive to other peo­ple) and also hav­ing a good en­vi­ron­ment in which to learn. To say that some­one should look for those things is so vague that’s it’s nearly use­less, but it’s still bet­ter than the usual ad­vice, which boils down to “learn what I learned”, which re­sults in ad­vice like “Career pro tip: if you want to get good, REALLY good, at de­sign­ing com­plex and state­ful dis­trib­uted sys­tems at scale in real-world en­vi­ron­ments, learn func­tional pro­gram­ming. It is an al­most per­fectly iden­ti­cal skillset.” or the even more ex­treme claims from some lan­guage com­mu­ni­ties, like Chuck Moore’s claim that Forth is at least 100x as pro­duc­tive as bor­ing lan­guages.

I took generic in­ter­net ad­vice early in my ca­reer, in­clud­ing lan­guage ad­vice (this was when much of this kind of ad­vice was rel­a­tively young and it was not yet pos­si­ble to eas­ily ob­serve that, de­spite many peo­ple tak­ing ad­vice like this, peo­ple who took this kind of ad­vice were not par­tic­u­larly ef­fec­tive and peo­ple who are par­tic­u­larly ef­fec­tive were not likely to have taken this kind of ad­vice). I learned Haskell, Lisp, Forth, etc. At one point in my ca­reer, I was on a two per­son team that im­ple­mented what might still be, a decade later, the high­est per­for­mance Forth proces­sor in ex­is­tence (it was a 2GHz IPC-oriented proces­sor) and I pro­grammed it as well (there were good rea­sons for this to be a stack proces­sor, so Forth seemed like as good a choice as any). Like Yossi Kreinin, I think I can say that I spent more ef­fort than most peo­ple have be­com­ing pro­fi­cient in Forth, and like him, not only did I not find it find it to be a 100x pro­duc­tiv­ity tool, it was­n’t clear that it would, in gen­eral, even be 1x on pro­duc­tiv­ity. To be fair, a num­ber of other tools did bet­ter than 1x on pro­duc­tiv­ity but, over­all, I think fol­low­ing in­ter­net ad­vice was very low ROI and the things that I learned that were high ROI weren’t things peo­ple were rec­om­mend­ing.

In ret­ro­spect, when peo­ple said things like “Forth is very pro­duc­tive”, what I sus­pect they re­ally meant was “Forth makes me very pro­duc­tive and I have not con­sid­ered how well this gen­er­al­izes to peo­ple with dif­fer­ent ap­ti­tudes or who are op­er­at­ing in dif­fer­ent con­texts”. I find it to­tally plau­si­ble that Forth (or Lisp or Haskell or any other tool or tech­nique) does work very well for some par­tic­u­lar peo­ple, but I think that peo­ple tend to over­es­ti­mate how much some­thing work­ing for them means that it works for other peo­ple, mak­ing ad­vice gen­er­ally use­less be­cause it does­n’t dis­tin­guish be­tween ad­vice that’s ap­ti­tude or cir­cum­stance spe­cific and gen­er­al­iz­able ad­vice, which is in stark con­trast to fields where peo­ple ac­tu­ally dis­cuss the pros and cons of par­tic­u­lar tech­niques.

While a coach can give you ad­vice that’s tai­lored to you 1 on 1 or in small groups, that’s dif­fi­cult to do on the in­ter­net, which is why the best I can do here is the use­lessly vague “pick up skills that are suit­able for you”. Just for ex­am­ple, two skills that clicked for me are “having an ad­ver­sar­ial mind­set” and “looking at data”. A per­haps less use­less piece of ad­vice is that, if you’re hav­ing a hard time iden­ti­fy­ing what those might be, you can ask peo­ple who know you very well, e.g., my man­ager and Ben Kuhn in­de­pen­dently named com­ing up with so­lu­tions that span many lev­els of ab­strac­tion as a skill of mine that I fre­quently ap­ply (and I did­n’t re­al­ize I was do­ing that un­til they pointed it out).

Another way to find these is to look for things you can’t help but do that most other peo­ple don’t seem to do, which is true for me of both “looking at data” and “having an ad­ver­sar­ial mind­set”. Just for ex­am­ple, on hav­ing an ad­ver­sar­ial mind­set, when a com­pany I was work­ing for was beta test­ing a new cus­tom bug tracker, I filed some of the first bugs on it and put un­usual things into the fields to see if it would break. Some peo­ple re­ally did­n’t un­der­stand why any­one would do such a thing and were baf­fled, dis­gusted, or hor­ri­fied, but a few peo­ple (including the au­thors, who I knew would­n’t mind), re­ally got it and were happy to see the sys­tem pushed past its lim­its. Poking at the lim­its of a sys­tem to see where it falls apart does­n’t feel like work to me; it’s some­thing that I’d have to stop my­self from do­ing if I wanted to not do it, which made spend­ing a decade get­ting bet­ter at test­ing and ver­i­fi­ca­tion tech­niques felt like some­thing hard not to do and not work. Looking deeply into data is one I’ve spent more than a decade on at this point and it’s an­other one that, to me, emo­tion­ally feels al­most wrong to not im­prove at.

That these things are suited to me is ba­si­cally due to my per­son­al­ity, and not some­thing in­her­ent about hu­man be­ings. Other peo­ple are go­ing to have dif­fer­ent things that re­ally feel easy/​right for them, which is great, since if every­one was into look­ing at data and no one was into build­ing things, that would be very prob­lem­atic (although, IMO, look­ing at data is, on av­er­age, un­der­rated).

The other ma­jor in­gre­di­ent in what I’ve tried to learn is find­ing en­vi­ron­ments that are con­ducive to learn­ing things that line up with my skills that make sense for me. Although sug­gest­ing that other peo­ple do the same sounds like ad­vice that’s so ob­vi­ous that it’s use­less, based on how I’ve seen peo­ple se­lect what team and com­pany to work on, I think that al­most no­body does this and, as a re­sult, dis­cussing this may not be com­pletely use­less.

An ex­am­ple of not do­ing this which typ­i­fies what I usu­ally see is a case I just hap­pened to find out about be­cause I chat­ted with a man­ager about why their team had lost their new full-time in­tern con­ver­sion em­ployee. I asked them about it since it was un­usual for that man­ager to lose any­one since they’re very good at re­tain­ing peo­ple and have low turnover on their teams. It turned out that their in­tern had wanted to work on in­fra, but had joined this man­ager’s prod­uct team be­cause they did­n’t know that they could ask to be on a team that matched their pref­er­ences. After the man­ager found out, the man­ager wanted the in­tern to be happy and fa­cil­i­tated a trans­fer to an in­fra team. In this case, this was a dou­ble whammy since the new hire dou­bly did­n’t con­sider work­ing in an en­vi­ron­ment con­ducive for learn­ing the skills they wanted. They made no at­tempt to work in the area they were in­ter­ested in and then they joined a com­pany that has a dys­func­tional in­fra org that gen­er­ally has poor de­sign and op­er­a­tional prac­tices, mak­ing the com­pany a rel­a­tively dif­fi­cult place to learn about in­fra on top of not even try­ing to land on an in­fra team. While that’s an un­usu­ally bad ex­am­ple, in the me­dian case that I’ve seen, peo­ple don’t make de­ci­sions that re­sult in par­tic­u­larly good out­comes with re­spect to learn­ing even though good op­por­tu­ni­ties to learn are one of the top things peo­ple say that they want.

For ex­am­ple, Steve Yegge has noted:

The most fre­quently-asked ques­tion from col­lege can­di­dates is: “what kind of train­ing and/​or men­tor­ing do you of­fer?” … One UW in­ter­vie­wee just told me about Ford Motor Company’s men­tor­ing pro­gram, which Ford had ap­par­ently used as part of the sales pitch they do for in­ter­vie­wees. [I’ve elided the de­tails, as they weren’t re­ally rel­e­vant. -stevey 3/1/2006] The stu­dent had ab­sorbed it all in amaz­ing de­tail. That does­n’t re­ally sur­prise me, be­cause it’s one of the things can­di­dates care about most.

For my­self, I was lucky that my first job, Centaur, was a great place to de­velop hav­ing an ad­ver­sar­ial mind­set with re­spect to test­ing and ver­i­fi­ca­tion. When I com­pare what the ver­i­fi­ca­tion team there ac­com­plished, it’s com­pa­ra­ble to peer pro­jects at other com­pa­nies that em­ployed much larger teams to do very sim­i­lar things with sim­i­lar or worse ef­fec­tive­ness, im­ply­ing that the team was highly pro­duc­tive, which made that a re­ally good place to learn.

Moreover, I don’t think I could’ve learned as quickly on my own or by try­ing to fol­low ad­vice from books or the in­ter­net. I think that peo­ple who are re­ally good at some­thing have too many bits of in­for­ma­tion in their head about how to do it for that in­for­ma­tion to re­ally be com­press­ible into a book, let alone a blog post. In sports, good coaches are able to con­vey that kind of in­for­ma­tion over time, but I don’t know of any­thing sim­i­lar for pro­gram­ming, so I think the best thing avail­able for learn­ing rate is to find an en­vi­ron­ment that’s full of ex­perts.

For “looking at data”, while I got a lot bet­ter at it from work­ing on that skill in en­vi­ron­ments where peo­ple weren’t re­ally tak­ing data se­ri­ously, the rate of im­prove­ment dur­ing the past few years, where I’m in an en­vi­ron­ment where I can toss ideas back and forth with peo­ple who are very good at un­der­stand­ing the lim­i­ta­tions of what data can tell you as well as good at in­form­ing data analy­sis with deep do­main knowl­edge, has been much higher. I’d say that I im­proved more at this in each in­di­vid­ual year at my cur­rent job than I did in the decade prior to my cur­rent job.

One thing to per­haps note is that the en­vi­ron­ment, how you spend your day-to-day, is in­her­ently lo­cal. My cur­rent em­ployer is prob­a­bly the least data dri­ven of the three large tech com­pa­nies I’ve worked for, but my vicin­ity is a great place to get bet­ter at look­ing at data be­cause I spend a rel­a­tively large frac­tion of my time work­ing with peo­ple who are great with data, like Rebecca Isaacs, and a rel­a­tively small frac­tion of the time work­ing with peo­ple who don’t take data se­ri­ously.

This post has dis­cussed some strate­gies with an eye to­wards why they can be valu­able, but I have to ad­mit that my mo­ti­va­tion for learn­ing from ex­perts was­n’t to cre­ate value. It’s more that I find learn­ing to be fun and there are some ar­eas where I’m mo­ti­vated enough to ap­ply the skills re­gard­less of the en­vi­ron­ment, and learn­ing from ex­perts is such a great op­por­tu­nity to have fun that it’s hard to re­sist. Doing this for a cou­ple of decades has turned out to be use­ful, but that’s not some­thing I knew would hap­pen for quite a while (and I had no idea that this would ef­fec­tively trans­fer to a new in­dus­try un­til I changed from hard­ware to soft­ware).

A lot of ca­reer ad­vice I see is ori­ented to­wards ca­reer or suc­cess or growth. That kind of ad­vice of­ten tells peo­ple to have a long-term goal or strat­egy in mind. It will of­ten have some ar­gu­ment that’s along the lines of “a ran­dom walk will only move you sqrt(n) in some di­rec­tion whereas a di­rected walk will move you n in some di­rec­tion”. I don’t think that’s wrong, but I think that, for many peo­ple, that ad­vice im­plic­itly un­der­es­ti­mates the dif­fi­culty of find­ing an area that’s suited to you, which I’ve ba­si­cally done by trial and er­ror.

One ma­jor topic not dis­cussed is how to bal­ance what “level” of skill to work on, which could be some­thing high level, like “looking at data”, to some­thing lower level, like “Bayesian mul­ti­level mod­els”, to some­thing even lower level, like “typing speed”. That’s a large enough topic that it de­serves its own post that I’d ex­pect to be longer than this one but, for now, here’s a com­ment from Gary Bernhardt about some­thing re­lated that I be­lieve also ap­plies to this topic.

Another ma­jor topic that’s not dis­cussed here is pick­ing skills that are rel­a­tively likely to be ap­plic­a­ble. It’s a lit­tle too naive to just say that some­one should think about learn­ing skills they have an ap­ti­tude for with­out think­ing about ap­plic­a­bil­ity.

But while it’s pretty easy to pick out skills where it’s very dif­fi­cult to ei­ther have an im­pact on the world or make a de­cent amount of money or achieve what­ever goal you might want to achieve, like “basketball” or “boxing”, it’s harder to pick be­tween plau­si­ble skills, like com­puter ar­chi­tec­ture vs. PL.

But I think there semi-rea­son­able sound­ing skills are likely enough to be high re­turn if they’re a good fit for some­one that trial and er­ror among semi-rea­son­able sound­ing skills is fine, al­though it prob­a­bly helps to be able to try things out quickly

Thanks to Ben Kuhn, Alexey Guzey, Marek Majkowski, Nick Bergson-Shilcock, @bekindtopeople2, Aaron Levin, Milosz Danczak, Anja Boskovic, John Doty, Justin Blank, Mark Hansen, and Jamie Brandon for com­ments/​cor­rec­tions/​dis­cus­sion.

Some rea­sons to work on pro­duc­tiv­ity and ve­loc­ity →

I re­cently watched “The Billion Dollar Code” lim­ited-se­ries on Netflix, which claims that Google Earth is a rip-off of a pro­ject called TerraVision, cre­ated by the German art col­lec­tive ART+COM. The show chron­i­cles their law­suit against Google, which ul­ti­mately failed.

I am drawn to sto­ries of in­ven­tors hav­ing their work stolen by greedy ass­holes. I can gen­uinely re­late to the in­ven­tors in this case, and their var­i­ous strug­gles. If some­one is owed credit or money for their in­ven­tion, I want them get it. And I truly re­spect the early and in­no­v­a­tive work done by ART+COM.