Photographers have a great deal of creative control. Put the same camera in your hands versus a professional and you will get different results even with the same subject. You taking a snapshot in the woods are not Ansel Adams, nor are you taking a selfie Annie Leibovitz. The skill and artistic intent of the human being using the tool matters.
Meanwhile with AI, given the same model and inputs - including a prompt which may include the names of specific artists "in the style of x" - one can reproduce mathematically equivalent results, regardless of the person using it. If one can perfectly replicate the work by simply replicating the tools, then the human using the tool adds nothing of unique personal value to the end result. Even if one were to concede that AI generated content were art, it still wouldn't be the art of the user, it would be the art of the model.
there must be a fallacy name for 'more of a good thing is always a good thing' line of reasoning. almost every good out there is good in a certain range. outside of that range it becomes detrimental, possibly deadly. there is even a Swedish word for it, https://en.wikipedia.org/wiki/Lagom. a few examples.
material:
- water: too little => thirst, too much => drown
- heat: too little => freeze, too much => burn
- food: too little => starvation, too much => obesity
spiritual:
- courage: too little => cowardice, too much => foolhardiness
- diligence: too little => slothfulness, too much => workaholism
- respect: too little => disregard, too much => idolatry
I understand your sentiment entirely, but it's not what I said, I didn't say an abundance is everything we should strive for , I said that having more efficient systems is good.
at scale, delivering cost-effective infrastructure matters to the bottom line, even for seemingly unrelated cash cows like ads. the larger the infrastructure footprint, the better economies of scale and amortization of R&D. there is a race to deploy the largest infrastructure (datacenters) economically sustainable. as external demand dwarfs internal demand, and as infrastructure is delivered to external customers via Google Cloud, Google Cloud is a long term strategic bet.
side note: the datacenter build out race is an early instance of the paperclip optimizer phenomena, with datacenters instead of paperclips.
One of my favorite parts of the Death Stranding (before my overtaxed attention span led to me abandoning it for another game) was that "Likes" were the actual currency in the world
the search business has always been caught between delivering simple and to the point results to users and skewing results to generate return on investment to advertisers.
in its early years google was also refreshingly simple and to the point. the billion then trillion dollars market capitalization placed pressure on them to deliver financial results, the ads spam grew like a cancer. openai is destined for the same trajectory, if only faster. it will be poetic to watch all the 'ethical' censorship machinery repurposed to subtly weigh conversations in favor of this or other brand. pragmatically, the trillion dollar question is what will be the openai take on adwords.
Ads are supposed to reduce transaction cost by spreading information to allow consumers to efficiently make decisions about purchases, many of which entail complex trade-offs.
In other words, people already want to buy things.
I would love to be able to ask an intelligence with access to the world's information questions to help me efficiently make purchasing decisions. I've tried this a few times with GPT-4 and it seems to bias heavily toward whatever came up in the first few pages of web results, and rarely "knows" anything useful about the products.
A sufficiently good product or service will market itself and it is rarely necessary for marketing spend or brand marketing for those rare exceptional products and services.
For the rest of the space of products and services, ad spend is a signal that the product is not good enough that the customer would have already heard about it.
With an AI assistant, getting a sense of the space of available products and services should be simple and concise, without the noise and imprecision of ads and clutter of "near miss" products and services ("reach" that companies paid for) cluttering things up.
The bigger question is which AI assistant people will trust they can ask important questions to and get unbiased and helpful results. "Which brand of Moka pot under $20 is the highest quality?" or "Help me decide which car to buy" are the kinds of questions that require a solid analytical framework and access to quality data to answer correctly.
AI assistants will act like the invisible hand and shoudl not have a thumb on the scale. I would pay more than $20 per month to use such an AI. I find it hard to believe that OpenAI would have to resort to any model other than a paid subscription if the information and analysis is truly high quality (which it appears to be so far).
I did exactly that with a custom GPT and it works pretty well. I did my best to push it to respond with its training knowledge about brand reputation and avoid searches. When it has to resort to searches I pushed it to use trusted product information sources and avoid spammy or ad-ridden sites.
It allowed me to spot the best brands and sometimes even products in verticals I knew nothing about beforehand. It’s not perfect but already very efficient.
the man has left, or the man was pushed away. the man has left, or the man is providing financial support, while being deprived of contact with his children.
when 1 in 3 children are raised in a single parent household, compared to the traditional norm of 1 in 20, everybody could use a reminder to act responsible when it comes to the life they create.
for example goog, +50% yoy, -12k layoffs in 2023, more in jan 2024. btw, this is expected impact of recent ai breakthroughs. no wonder the press picks up the vibe, there is a real fire beneath all that smoke.
It's a really good post, "The secret truth of business advice is that it's mostly about how to grimly extract residual value from the luck you already had, and the unearned love you were already unguardedly given, because there's really no method for making more of it."
If I am looking for a co-founder, who that person and their very unique combination of skills, perspectives, etc, matters a ton. If I am looking for SRE #20,000 for a FAANG, I need them to do the basic SRE role in a way that aligns with how we think about our caliber and culture. Obviously the person can do that in their own way to an extent, but we're not really looking for them to have insights like "actually, I don't think reliability matter - I am gonna stop doing it"
I don't think that's necessarily what the author is trying to communicate.
First of all, "If I am looking for a co-founder, who that person and their very unique combination of skills, perspectives, etc, matters a ton." Is that not just a well defined slot by another name? It sounds like your whole process is slots from conception to death.
"Obviously the person can do that in their own way to an extent", can they? This feels like a platitude. I can't speak to the SRE roll but most places I've worked you might get to choose from a list of pre-written algorithms and a few small UX details. And when you talk about collaborating on larger problems most individuals don't usually even get a choice of code style.
At a mature company I will agree that no, you shouldn't be hiring SRE's for product insight. Unless they fit into an insanely small bucket insight into your product is well outside of their scope. A contracting firm like McKinsey & Company would be a better fit for that kind of guidance. But tech insight? yes, you should be getting that from your SREs.
Also your example "insight" just feels... bad faith.
agreed! at small scale, we are persons engaged in meaningful relationships. at large scale, we are faceless cogs in the machine, numbers on a spreadsheet. being a faceless cog is dehumanizing, stripping our very identity away. how to reconcile the efficiencies brought by scale with the dehumanization it inflicts is a unique challenge for the technological society. I have yet to see a plausible answer.
Unlikely it will ever get better. First to market with a prototype tool, gains market share and momentum. Eventually the enthusiasm fades off and people start hating it, for good and sometimes bad reasons. Yet users are stuck because change is expensive and risky. The team is stuck because any change risks becoming the straw that broke the camel's back, possibly cascading through the user population. Story of our young industry.
I think you're part right in that I don't think they will make any backwards incompatible changes. But they could still make things a lot better in two simple ways:
* Fix error reporting. Nobody is doing anything that relies on the current error reporting anyway because it's near useless.
* Add a slight templating change that means "after this parses as valid YAML, expand this bit, and check that the expansion is itself valid YAML before merging it in" with options to either replace the node, or merge in adjacent (the latter to insert in lists etc). You can do that without backwards incompatible changes by making a syntax change that still uses the go {{ ... }} blocks, but that starts with a directive they can make simply expand to a new template processing directive in the first pass. Then just add a second pass that operates on a parse tree (I've just written a template expansion mechanism that works on json/yaml parse trees, in fact; if we didn't need Helm charts primarily for distribution to partners that I don't want to make use a custom deployment tool, I'd be tempted to replace our Helm charts with an expansion of that.
Better error reporting and being able to avoid the incessant "| indent .." blocks and ensuring the output either generates valid yaml or can "contain" the error report to the generated sub-block would make it so much easier to use.
