[HN Gopher] Andrew Ng: Building Faster with AI [video]
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Andrew Ng: Building Faster with AI [video]
Author : sandslash
Score : 283 points
Date : 2025-07-10 14:02 UTC (2 days ago)
(HTM) web link (www.youtube.com)
(TXT) w3m dump (www.youtube.com)
| bgwalter wrote:
| [flagged]
| reactordev wrote:
| He doesn't have to at this point, he just throws money at
| younger ones that will build it.
|
| I want an Andrew Ng Agent.
| Bluestein wrote:
| ... in essence, an "A-Ngent".-
|
| (I'll see myself out ...)
| arkmm wrote:
| Not affiliated, but someone's already working on that for
| you: https://www.realavatar.ai/
| reactordev wrote:
| I'm serious, the man's a genius...
| hoegarden wrote:
| Baidu.
| bgwalter wrote:
| The video's description is about _building_ startups through
| vibe coding, not _using_ "AI" like self-driving or chatbots
| in startups.
|
| Additionally, Baidu wasn't a startup when he joined in 2014.
| hoegarden wrote:
| Ng built baidu's AI department and began their start in
| various sectors with actual AI system design, so yes, he
| isn't a failed startup entrepreneur like any vibe startup
| maker who already wants to stop and give advice.
|
| Maybe you can help me hire a vibe coder with 10 years
| experience?
| bgwalter wrote:
| He built it _without_ LLMs in 2014 and now he is selling
| LLMs for coding to the young. That is the entire point of
| this subthread.
| hoegarden wrote:
| Right.. He's just a giant, not a midget with a step
| ladder.
|
| But I do question why anyone who played a significant
| role in the foundation of the current AI generation would
| teach an obvious new Zuckerberg generation who will
| apparently think they are the start of everything if they
| get a style working in the prompt.
|
| If not for 3 people in 2012, I find it highly unlikely a
| venture like OpenAI could have occurred and without Ng in
| particular I wouldn't be surprised if the field would
| have been missing a few technical pieces as well as the
| hire-able engineers.
| crystal_revenge wrote:
| A good chunk of Ng's work these days seems to be around AI Fund
| [0] which he explicitly mentioned in the video, in the first 5
| seconds, involves co-founding these startups and being in the
| weeds with the initial development.
|
| Additionally, he does engage pretty closely with the teams
| behind the content of his deeplearning.ai lectures and does
| make sure he has a deep understanding of the products these
| companies are highlighting.
|
| He certainly is a businessman, but that doesn't exlcudethe
| possibility that he remains highly knowledgeable about this
| space.
| dcreater wrote:
| He's lost credibility in my eyes given that his courses
| essentially have a pay to play model for startups like
| langchain
| crystal_revenge wrote:
| Except they _aren 't_ pay to play unless you consider doing
| the work for the course the "payment". There's certainly an
| exchange since there is a lot of work involved, but DLAI
| provides a team to help design, structure and polish the
| course and then the team creating the course does the
| majority of the work creating the content, but there's no
| financial exchange.
|
| The DLAI team is also pretty good about ensuring the
| content covers a topic not a product in general.
| dcreater wrote:
| The content is a repackage of previously existing,
| publicly available notebooks, docs, YouTube videos. I
| wouldnt be surprised if the repackaging was done by AI.
| raincole wrote:
| Courses are not academic journals, dude. They're supposed
| to be teaching you existing knowledge.
| crystal_revenge wrote:
| Again this is not true. I've known several people who
| have made courses for DLAI and they all put substantial
| time into creating the courses.
| whattheheckheck wrote:
| He literally builds companies and hires ceos to run them Google
| it
| melenaboija wrote:
| > He literally builds companies
|
| Like with actual mortar, brick by brick?
| mrbonner wrote:
| You become a millionaire by selling books (courses) of how to
| become millionaire to others.
| rubslopes wrote:
| Relevant video
| https://youtu.be/CWMAOzH20mY?si=Kr8vp1vo_PpRNJ8-&utm_source=...
| Koshcheiushko wrote:
| Thanks
| DataDaemon wrote:
| when there is a gold rush, just sell courses how to mine gold
| azan_ wrote:
| He sold courses (great ones!) long before there was AI-gold
| rush. He's one of the OG players in online education and I
| think he deserves praise, not blame for that.
| w10-1 wrote:
| Not sure why this has drawn silence and attacks - whence the
| animus to Ng? His high-level assessments seem accurate, he's a
| reasonable champion of AI, and he speaks credibly based on
| advising many companies. What am I missing? (He does fall on the
| side of open models (as input factors): is that the threat?)
|
| He argues that landscape is changing (at least quarterly), and
| that services are (best) replaceable (often week-to-week) because
| models change, but that orchestration is harder to replace, and
| that there are relatively few orchestration platforms.
|
| So: what platforms are available? Are there other HN posts that
| assess the current state of AI orchestration?
|
| (What's the AI-orchestration acronym? not PAAS but AIOPAAS? AOP?
| (since aspect-oriented programming is history))
| handfuloflight wrote:
| We've defined agents. Let's now define orchestration.
| ramraj07 wrote:
| Bold claim. I am not convinced anyone's done a good job
| defining agents and if they did 99% of the population has a
| different interpretation.
| handfuloflight wrote:
| Okay. We've tried to define agents. Now let's try to define
| orchestration.
| lhuser123 wrote:
| And make it more complicated than K8s
| jliptzin wrote:
| Not possible
| vajrabum wrote:
| The platforms I've seen live on top of kubernetes so I'm
| afraid it is possible. nvidia-docker, all the cuda
| libraries and drivers, nccl, vllm,... Large scale
| distributed training and inference are complicated
| beasties and the orchestration for them is too.
| stego-tech wrote:
| > So: what platforms are available?
|
| I couldn't tell you, but what I _can_ contribute to that
| discussion is that orchestration of AI in its current form
| would focus on one of two approaches: consistent output despite
| the non-deterministic state of LLMs, or consistent inputs that
| leans into the non-deterministic state of LLMs. The problem
| with the former (output) is that you cannot guarantee the
| output of an AI on a consistent basis, so a lot of the
| "orchestration" of outputs is largely just brute-forcing tokens
| until you get an answer within that acceptable range; think the
| glut of recent "Show HN" stuff where folks built a slop-app by
| having agents bang rocks together until the code worked.
|
| On the input side of things, orchestration is less about AI
| itself and more about ensuring your data and tooling is
| consistently and predictably accessible to the AI such that the
| output is similarly predictable or consistent. If you ask an AI
| what 2+2 is a hundred _different_ ways, you increase the
| likelihood of hallucinations; on the other hand, ensuring the
| agent /bot gets the same prompt with the same data formats and
| same desired outputs every single time makes it more likely
| that it'll stay on task and not make shit up.
|
| My engagement with AI has been more of the input-side, since
| that's scalable with existing tooling and skillsets in the
| marketplace instead of the output side, which requires niche
| expertise in deep learning, machine learning, model training
| and fine-tuning, etc. In other words, one set of skills is
| cheaper and more plentiful while also having impacts throughout
| the organization (because _everyone_ benefits from consistent
| processes and clean datasets), while the other is incredibly
| expensive and hard to come by with minimal impacts elsewhere
| unless a profound revolution is achieved.
|
| One thing to note is that Dr. Ng gives the game away at the Q&A
| portion fairly early on: "In the future, the people who are the
| most powerful are the people who can make computers do exactly
| what you want it to do." In that context, the current AI slop
| is antithetical to what he's pitching. Sure, AI can improve
| speed on execution, prototyping, and rote processes, but the
| real power remains in the hands of those who can build with
| precision instead of brute-force. As we continue to hit
| barriers in the physical capabilities of modern hardware and
| wrestle with the effects of climate change and/or poor energy
| policies, efficiency and precision will gradually become more
| important than speed - at least that's my thinking.
| handfuloflight wrote:
| This is great thinking, thank you for writing this.
| vlovich123 wrote:
| > The problem with the former (output) is that you cannot
| guarantee the output of an AI on a consistent basis
|
| Do you mean you cannot guarantee the result based on a task
| request with a random query? Or something else? I was under
| the impression that LLMs are very deterministic if you
| provide a fixed seed for the samplers, fixed model weights,
| and fixed context. In cloud providers you can't guarantee
| this because of how they implement this (batching unrelated
| requests together and doing math). Now you can't guarantee
| the quality of the result from that and changing the seed or
| context can result in drastically different quality. But
| maybe you really mean non-deterministic but I'm curious where
| this non-determinism would come from.
| stego-tech wrote:
| > I was under the impression that LLMs are very
| deterministic if you provide a fixed seed for the samplers,
| fixed model weights, and fixed context.
|
| That's all input-side, though. On the output side, you can
| essentially give an LLM anxiety by asking the exact same
| question in different ways, and the machine doesn't
| understand anymore that you're asking _the exact same
| question_.
|
| For instance, take one of these fancy "reasoning" models
| and ask it variations on 2+2. Try two plus two, 2 plus two,
| deux plus 2, TwO pLuS 2, etc, and observe its "reasoning"
| outputs to see the knots it ties itself up in trying to
| understand why you keep asking the same calculation over
| and over again. Running an older DeepSeek model locally,
| the "reasoning" portion continued growing in time and
| tokens as it struggled to provide context that didn't exist
| to a simple problem that older/pre-AI models wouldn't bat
| an eye at and spit out "4".
|
| Trying to wrangle consistent, reproducible outputs from
| LLMs without guaranteeing consistent inputs is a fool's
| errand.
| vlovich123 wrote:
| Ok yes. I call that robustness of the model as opposed to
| determinism which to me implies different properties. And
| yes, I too have been frustrated by the lack of robustness
| of models to minor variations in input or even using a
| different seed for the same input.
| contrast wrote:
| Pointing out that LLMs are deterministic as long as you
| lock down everything, is like saying an extra bouncy ball
| doesn't bounce if you leave it on flat surface, reduce the
| temperature to absolute zero, and make sure the surface and
| the ball are at rest before starting the experiment.
|
| It's true but irrelevant.
|
| One of the GP's main points was that even the simplest
| questions can lead to hundreds of different contexts; they
| probably already know that you could get different outcomes
| if you could instead have a fixed context.
| void-star wrote:
| Really valid points. I agree with the bits about "expertise
| in getting the computer to do what you want" being the way of
| the future, but he also raises really valid points about
| people having strong domain knowledge (a la his colleague
| with extensive art history knowledge being better at
| midjourney than him) after saying it's okay to tell people to
| just let the LLM write code for you and learn to code that
| way. I am having a hard time with the contradictions, maybe
| it's me. Not meaning to rag on Dr. Ng, just further the
| conversation. (Which is super interesting to me.)
|
| EDIT: rereading and realizing I think what resonates most is
| we are in agreement about the antithetical aspects of the
| talk. I think this is the crux of the issue.
| lubujackson wrote:
| I'm guessing because this is basically an AI for Dummies
| overview, while half of HN is deep in the weeds with AI
| already. Nothing wrong with the talk! Except his focus on "do
| everything" agents already feels a bit stale as the move seems
| to be going in the direction of limited agents with a much
| stronger focus on orchestration of tools and context.
| hakanderyal wrote:
| From the recent threads, it feels like the other half is
| totally, willfully ignorant. Hence the responses.
| rhizome31 wrote:
| As someone who is part of that other half, I agree.
| davorak wrote:
| > I'm guessing because this is basically an AI for Dummies
|
| I second this, for the silence at least, I listened to the
| talk because it was Andrew Ng and it is good or at least fun
| to listen to talks by famous people, but I did not walk away
| with any new key insights, which is fine, most talks are not
| that.
| fullstackchris wrote:
| > deep in the weeds with AI already
|
| I doubt even 10% have written a custom MCP tool... and
| probably some who don't even know what that means
| jart wrote:
| I like Andrew Ng. He's like the Mister Rogers of AI. I always
| listen when he has something to say.
| koakuma-chan wrote:
| Is he affiliated with nghttp?
| dmoy wrote:
| No?
|
| ng*, ng-*, or *-ng is typically "Next Generation" in
| software nomenclature. Or, star trek (TNG). Alternatively,
| "ng-" is also from angular-js.
|
| Ng in Andrew Ng is just his name, like Wu in Chinese.
| janderson215 wrote:
| Wu from Wu-Tang?
| yorwba wrote:
| No, Wu-Tang ultimately derives from the Wudang Mountains,
| with the corresponding Cantonese being Moudong https://en
| .wiktionary.org/wiki/%E6%AD%A6%E7%95%B6%E5%B1%B1
| 57473m3n7Fur7h3 wrote:
| And between that and the rap group there's this important
| movie:
|
| Shaolin and Wu Tang (1983)
|
| > The film is about the rivalry between the Shaolin (East
| Asian Mahayana) and Wu-Tang (Taoist Religion) martial
| arts schools. [...]
|
| > East Coast hip-hop group Wu-Tang Clan has cited the
| film as an early inspiration. The film is one of Wu-Tang
| Clan founder RZA's favorite films of all time. Founders
| RZA and Ol' Dirty Bastard first saw the film in 1992 in a
| grindhouse cinema on Manhattan's 42nd Street and would
| found the group shortly after with GZA. The group would
| release its debut album Enter the Wu-Tang (36 Chambers),
| featuring samples from the film's English dub; the
| album's namesake is an amalgamation of Enter the Dragon
| (1973), Shaolin and Wu Tang, and The 36th Chamber of
| Shaolin (1978).
|
| https://en.wikipedia.org/wiki/Shaolin_and_Wu_Tang
| dmoy wrote:
| Yea haha the chinese-to-english gets confusing, because
| it's not a 1:1, it's an N:1 thing, for the number
| different Chinese languages, different tones, and semi-
| malicious US immigration agents who botched the shit out
| of people's names in the late 19th and early 20th
| century.
|
| Wu and Ng in Mandarin and Cantonese may be the same
| character. But Wu the common surname and Wu for some
| other thing (e.g. that mountain) may be different
| characters entirely.
|
| It gets even more confusing when you throw a third
| Chinese language in, say Taishanese:
|
| Wu = Ng (typically) for Mandarin and Cantonese et al. But
| if it's someone who went to America earlier, suddenly
| it's Woo. But even though they're both yue Chinese
| languages, Woo != Woo in Cantonese and Taishanese. For
| that name, it's Hu (Mandarin) = Wu / Wuh (Cantonese) =
| Woo (Taishanese, in America). Sometimes. Lol. Sometimes
| not.
|
| Similarly, Mei = Mai = Moy
| mnky9800n wrote:
| And he's been doing it forever and all from the original idea
| that he could offer a Stanford education on ai for free on
| the Internet thus he created coursera. The dude is cool.
| tomrod wrote:
| No need to add AI to the name, especially if it works. PaaS and
| IaaS are sufficient.
| lloeki wrote:
| > AOP? (since aspect-oriented programming is history)
|
| AOP is very much alive, people that do AOP have just forgotten
| what the name is, and many have simply reinvented it poorly.
| nivertech wrote:
| AOP always felt like a hack. I used it with C++ early on, and
| it was a preprocessor inserting ("weaving") aspects in the
| function entries/exits. Mostly was useful for logging. But
| that can be somewhat emulated using C++
| constructors/destructors.
|
| Maybe it can be also useful for DbC (Design-by-Contract) when
| sets of functions/methods have common pre/post-conditions
| and/or invariants.
|
| https://en.wikipedia.org/wiki/Aspect-
| oriented_programming#Cr...
| alex_smart wrote:
| Also very much alive and called that in the Java/Spring
| ecosystem
| pchristensen wrote:
| I have had reservation about Ng from a lot of his past hype, but
| I thought this talk was extremely practical and tactical. I
| recommend watching it before passing judgement.
| croes wrote:
| I haven't watched the video yet, but title does sound like
| quantity over quality.
|
| Why faster and not better with AI?
| pinkmuffinere wrote:
| I think this is an interesting question, and I'd like to
| genuinely attempt an answer.
|
| I essentially think this is because people prefer to optimize
| what they can measure.
|
| It is hard to measure the quality of work. People have
| subjective opinions, the size of opportunities can be
| different, etc, making quality hard to pin down. It is much
| easier to measure the time required for each iteration on a
| concept. Additionally, I think it is generally believed that a
| project with more iterations tends to have higher quality than
| a project with less, even putting aside the concern about
| measuring quality itself. Therefore, we put aside the
| discussion of quality (which we'd really like to improve), and
| instead make the claim that we can actually measure (time to do
| something), with the strong implication that this _also_ will
| tend to increase quality.
| croes wrote:
| I think speed isn't our problem.
|
| Most of the time the problem it's quality but everyone only
| seems eager to ship as fast as possible.
|
| Move fast and break things already happened and now we are
| adding more speed.
|
| ,,Your scientists were so preoccupied with whether they
| could, they didn't stop to think if they should."
|
| Or for the more sophisticated
|
| https://en.wikipedia.org/wiki/The_Physicists
|
| Energy consumption and data protection were a thing and then
| came AI and all of a sudden it doesn't matter anymore.
|
| Between all the good things people create with AI I see a lot
| more useless or even harmful things. Scams and fake news get
| better and harder to distinguish to a point where reality
| doesn't matter anymore.
| markerz wrote:
| I think quality takes time and refinement which is not
| something that LLMs have solved very well today. They are very
| okay at it, except for very specific targeted refinements
| (Grammerly, SQL editors).
|
| However, they are excellent at building from 0->1, and the
| video is suggesting that this is perfect for startups. In the
| context of startups, faster is better.
| croes wrote:
| Depends on the startup. For medical or financial things
| faster isn't better.
|
| DOGE acts like a startup and we all fear the damage.
|
| I would prefer better startups over faster at anytime.
|
| Now I fear AI will just make the haystack bigger and the
| needles harder to find.
|
| Same with artists, writers, musicians. They drown in the
| flood of the AI created masses.
| androng wrote:
| https://toolong.link/v?w=RNJCfif1dPY&l=en
| Keyframe wrote:
| strong MLM energy vibe in that talk.
| imranq wrote:
| My two takeaways is you build 1) Having a precise vision of what
| you want to achieve 2) Being able to control / steer AI towards
| that vision
|
| Teams that can do both of these things, especially #1 will move
| much faster. Even if they are wrong its better than vague ideas
| that get applause but not customers
| void-star wrote:
| Yes this! The observation that being specific versus general in
| the problems you want to solve is a better start-up plan is
| true for all startups ever, not just ones that use LLMs to
| solve them. Anecdotal/personal startup experiences support this
| strongly and I read enough on here to know that I am not
| alone...
| techpineapple wrote:
| What's the balance between being specific in a way that's
| positive and allows you to solve good problems, and not
| getting pigeonhold and not being able to pivot? I wonder if
| companies who pivot are the norm or if you just here of the
| most popular cases.
| skipants wrote:
| I'm 20 minutes into the video and it does seem mostly basic and
| agreeable.
|
| Two arguments from Ng that really stuck out that is really
| tripping my skepticism alarm are:
|
| 1) He mentions how fast prototyping has begun because generating
| a simple app has become easier with AI. This, to me, has always
| been quick and never the bottleneck for any company I've been at,
| including startups. Validating an idea was simple enough via
| wireframing. I can maybe see it for selling an idea where you
| need some amount of fidelity yo impress potential investors...
| but I would hope places like YC can see the tech behind the idea
| without seeing the tech itself. Or at least can ignore low
| fidelity if a prototype shows the meat of the product.
|
| 2) Ng talks about how everyone in his company codes, from the
| front desk to the executives. The "everyone should code" idea has
| been done and shown to fail for the past 15 years. In fact I've
| seen it be more damaging than helpful because it gave people
| false confidence that they could tell engineers how to do their
| job rather than a more empathetic understanding.
| apwell23 wrote:
| even prototyping hasn't become "fast" because you cannot purely
| vibecode even a prototype.
| marcosdumay wrote:
| On point 1, it's worse than that. Adding detail and veracity to
| a prototype is well known to bring negative value.
|
| Prototypes must be exactly a sketchy as the ideas they
| represent, otherwise they mislead people into thinking the
| software is built and your ideas can't be changed.
| macNchz wrote:
| I've always said this as well, having done lots and lots of
| early stage building and prototyping, and suffering plenty of
| proto-duction foibles, however my view has shifted on this a
| lot in the last year or so.
|
| With current models I'm able to throw together fully working
| web app prototypes so quickly and iterate often-sweeping UI
| and architectural changes so readily that I'm finding it has
| changed my whole workflow. The idea of trying to keep things
| low-fidelity at the start is predicated on the understanding
| that changes later in the process are much more difficult or
| expensive, which I think is increasingly no longer the case
| in many circumstances. Having a completely working prototype
| and then totally changing how it works in just a few
| sentences is really quite something.
|
| The key to sustainability in this pattern, in my opinion, is
| not letting the AI dictate project structure or get too far
| ahead of your own understanding/oversight of the general
| architecture. That's a balancing act to be sure, since purely
| vibe-coding is awfully tempting, but it's still far too easy
| to wind up with a big ball of wax that neither human nor AI
| can further improve.
| mteoharov wrote:
| At my company everybody codes, including PMs and business
| people. It can definitely be damaging done in the long run
| without any supervision from an actual programmer. This is why
| we assign an engineer to review every PR of a vibe coded
| project and they don't really need all of the context to detect
| bs approaches that will surely fail.
|
| About prototyping - its much faster and i dont know how anyone
| can argue this. PMs can get a full blown prototype for an MVP
| working in a day with AI assistance. Sure - they will be thrown
| in the trash after the demo, but they carry out their purpose
| of proving a concept. The code is janky but it works for its
| purpose.
| willahmad wrote:
| > This is why we assign an engineer to review every PR of a
| vibe coded project and they don't really need all of the
| context to detect bs approaches that will surely fail.
|
| I see this trend in many companies as well, just curious, how
| do you make sure engineering time is not wasted reviewing so
| many PRs? Because, some of them will be good, you only need
| couple of your bets to take off, some definitely bad
| sensanaty wrote:
| Good lord I think I'd rather eat a shotgun than be forced to
| review a billion garbage PRs made by PMs and other non-
| technical colleagues. It's bad enough reviewing PRs
| backenders write for FE features badly with AI (and vice
| versa), I cannot even imagine the pits of hell this crap is
| like.
|
| What happens when inevitably the PR/code is horrid? Do they
| just keep prompting and pushing out slop that some poor
| overworked dev is now forced to sit through lest he get PIP'd
| for not being brainwashed by LLMs yet?
| torginus wrote:
| The "everyone should code" idea has been done and shown to fail
| for the past 15 years - I pretty much completely agree, and
| this idea shows the outsized importance on programming as some
| kind of inherently superior activity, and bringing the ability
| to program to the masses as some kind of ultimate good.
|
| If you've worked long enough and had interacted with people
| with varied skillsets, people who don't code aren't only there
| for show, in fact, depending on the type of company you work
| at, their jobs might be genuinely more important for the
| company's success than yours.
| nikolayasdf123 wrote:
| not a single word about overwhelming replacement of humans with
| AI. nothing about countless jobs lost. nothing about ever
| increasing competition and rat-race. (speaking of software, but
| applies to all industries). his rose-glasses view is somewhere in
| between optimism-in-denial to straight-up lunacy. if this is the
| leader(s) we have been following, this should be a wake up call.
| lbrito wrote:
| How dare you insinuate that there might be negatives in a new
| technology. Outrageous. AI good.
| mehulashah wrote:
| This talk is deceptively simple. The most sage advice that
| founders routinely forget is what concrete idea are you going to
| implement and why do you think it will work? There has be a way
| to invalidate your idea and as a corollary you must have the
| focus to collect the data and properly invalidate it.
| nextworddev wrote:
| Hey Mehul, crossed paths with you at AWS. Good to see you are
| doing your own thing now. We could connect sometime
| cachecrab wrote:
| 1 product manager to 0.5 engineers for a project? That seems...
| off.
| sensanaty wrote:
| > 1 product manager to 0.5 engineers
|
| I would love to have access to whatever this guy is smoking,
| cause that is some grade-A mind rotted insanity right there. I
| can count on half of 1 hand the number of good PMs I've had
| trough my career who weren't a net negative on the
| projects/companies, and even they most definitely cannot build
| jackshit by throwing a bunch of LLM-hallucinated crap at the wall
| and seeing what sticks.
|
| But sure, the devs are the ones that are going to be replaced by
| the clueless middle managers who only exist to waste everyone's
| time.
| macawfish wrote:
| Or is it the other way around? Project managers who can't
| actually competently execute won't be able to hang?
|
| In the end, what if technically sharp designers and well
| rounded developers actually end up pushing out incompetent
| managers?
|
| Could be wishful thinking but you never know.
| macawfish wrote:
| Case in point: https://old.reddit.com/r/ProductManagement/com
| ments/1lw9r9h/...
|
| (the comments are especially revealing)
| uses wrote:
| he's saying that the productivity of devs is increasing so
| much, especially during the prototyping phase, that gathering
| feedback is becoming the bottleneck, hence there is more PM
| labor needed. he didn't say anything about reducing the
| quantity of dev labor needed.
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