andrewyates2020

I worked on ad load optimization and founded a company that specializes in ad load optimization, here's an explainer video: https://www.promoted.ai/videos/unified-ads-search-and-feed-o...).

The cynical comments here align with my experience: when leadership demands now revenue, there is an iterative game where the only correct answer is "increase revenue now." If you run enough A/B tests and ask this question enough times over a large enough organization, more ads and 3p always result.

Most companies are trying hard to produce a great user experience. However, it's hard to measure subtle degradations to buyer experiences, especially when those degradations happen after the purchase or quality metrics corrupted by motivated sellers or advertisers. This is one reason obsession with A/B testing drives this poor user experience: it's hard to measure. Revenue now is easy to measure.

Another aspect you may not see as a buyer is that when the market is down, sellers are SCREAMING at their platforms to fix the problem. Same iterated game: give boost (discount, ad credits etc.), less screaming (for that team right now). What you see as bad as a buyer may be trying to appease sellers.

Not silence. Sequoia posted a letter declaring that their FTX investment is $0 and that this is not a notable fraction of their portfolio. FTX may have been a theoretically sound investment for Sequoia because many venture investments are marked to $0, and FTX got to huge-or-zero much faster than any other investment. A company that slowly goes to $0 is equally bad but worse for time-value.

What hurts my brain is that theoretically this investment strategy is sound, but the implementation of it looks like: FTX. It's horrific. It's like total war logic. Logically it works, probabilistically it's reasonable, living it is grotesque and insane.

Sequoia capital published this. That should be in the title. Without knowing that this was a real article published by real people at Sequoia a couple of months ago, this reads like a try-hard troll. No! It’s real! (Sequoia is like the Stanford of venture capital and is one of ftx’s biggest investors)

Promoted.ai is hiring experienced engineers in data infra, ML, and React to build marketplace optimization. If you have worked at Big Tech in ads or recommendations and know that you could do better, come work with all former staff engineers from Google and Facebook to prove it and own a piece of better for yourself.

We especially like former staff+ ads engineers, EMs, and directors. People who have FB or Google experience plus 'next tier' experience at Instacart, DoorDash, Snapchat, Pinterest, Twitter, Airbnb, etc. who can compare and contrast systems and have a good understanding of where top tech companies are peaking-out technically due to product management issues around pulling forward revenues to track quarterly revenue estimates versus building trustworthy commercial optimization systems.

https://www.ycombinator.com/companies/promoted/jobs

Public wifi can be provided as a way to track your identity and tie it to your mobile device, email, or other identifiers. For example: https://adentro.com/

We start by comparing to the price of hiring engineers, which is at least 300k TAC per engineer

In FPA, you need to strategically lower your bid to get "margin." In GSP, that's also true. I have some details here:

https://medium.com/promoted/when-goog-fb-is-bad-for-your-per...

Gsp does NOT have this dominant strategy property! People think it does which ironically makes it much better at “extracting value.” non repeated second price auction has this property. Gsp will almost always overprice just like fpa except in very unrealistic situations

I don’t follow, what do you mean?

> buy rather than build

Candidly, this is our biggest challenge. If we can surmount this, we'll be huge. This is also why care so much about "upmarket brand". Decisions aren't made by numbers alone. We need to show that we're the smarter way to grow faster because other top companies are doing it.

This is a big bet. Is there a threshold for engineer TAC in search, discovery, and ads? $1M? 2? More? Hire a dozen? One hundred? I've seen these numbers. They happen because the potential value is there and the VC funding is there, but I haven't always seen the delivered engineering results. Us? We've been there, done that. We'd like to focus on delivering the results, and we know from experience that's going to happen better from the outside.

> no crossover between any of your customers

The model architecture and infra are the same, but the literal weights in memory are totally independent. Most of the heavy customization is in allocation rules and blender, and we have a DSL for that. https://github.com/promotedai/schema/blob/main/proto/deliver...

great, let's keep in touch!

Thanks! You should consider us when you're big enough to hire dedicated search engineers. We work best if you have at least several dozen different things to show for every query.

Anybody of any size can use our open source mobile SDK: https://www.promoted.ai/client-metrics-libraries

We already see some of this. I like the admission that there is no magic happening with GSP but I don't think FPA will work for most cases:

1) GSP doesn't promise any specific price. FPA promises "the price you bid." If that's not what people are paying by simple math, it will be confusing. That hurts trust. This could happen if you have a user quality control system that penalizes poor quality. GSP gives you a second control (price) in combination with delivery volume and placement to manage user experience.

2) People expect GSP. Claiming FPA is an admission that you need to build an autobidder system versus letting people discover this for themselves.

Thanks! For build-versus-buy, we have a 3-part strategy:

1) Win ICs: Do the "crappy" work of running marketplace search really well. This is ops, data logging and correctness, A/B testing, and managing the complexity of requirements from all competing teams who want to manipulate search results and boost things. These are things that backend search teams usually don't love, but we solve their problems so that they can focus on their expertise and ship features.

2) Don't Compete, combine: Our approach allows us to combine all competing recommendation systems together into a unified model. There is never a this-or-that decision, or a feeling of losing out. This also applies to other vendors. This is a pain for ML ops, but it's worth it. From an ML approach, mixing different systems typically outperforms any component system so long as you have the infra and parameter complexity management to handle them.

3) Build a brand of being the best: Not everything in big companies is engineering experience and metrics. Decisions get made when you're the hot solution that the cool people that you want to be like use. We deliberately focus on working with hot marketplaces and hiring awesome engineers with top experience to built this brand.

> You'd also be amazed at how long it takes to sign a contract

I would not ;)

> less eng-focused companies that would pay

Actually, our experience has been the opposite. The more sophisticated the engineering team, the more they recognize how big of a pain unified search ranking is to build and maintain, and the more they appreciate what we offer.

On the forever "model-bakeoff": our approach is to include all existing models as features into an omni-model. If you are experienced in ML ops, you should be cringing, but we pull it off because from a customer development standpoint, we never want to be competing with some other new technique. Instead, we want to have a big ball of systems and progress is always "add more stuff." Then, the business and product teams can focus on how they want their product to work versus technical details of specific recommendation systems.

Thank you! We actively seek out customers with idiosyncratic matching because we're better at it than alternatives. We rely on user engagement, in-session model responsiveness, and in-house expertise from the marketplaces themselves.

Part of the way we solve this is NOT with machine learning, but with tools to empower internal merchandizing teams and product teams in a way that fits nicely with the automated system. If you're a on a search team and had to goof around with elastic-search scores or hack in inserts for a new market merchandizing team, you've felt this pain. The path forward is ML + human expertise, which is better than either alone.

> basically fail at it

Our goal is to figure out "why" and "how to make it better." These are $T companies and dominate all performance ad spend. It's hard to think about such big numbers. One problem is that they start with crappy inventory (people who want to advertise) and it's really hard to actually _do_ something on these platforms with promotions that you do see. On marketplaces, you don't have these problems as much, because everything is already vetted and you can convert in the marketplace. That's why you're there, so it's a great experience.

So, we start from there, media matching that people love, and work backwards.

About one month to get an MVP running and start testing. We then support ongoing additions of more data, features, and tuning from your in-house data science and ML team.