My First Year as a Freelance AI Engineer

Similarly, a company's CEO could say "Hey, I could simply go to university myself and learn everything about NLP and solve my NLP problem and save 10.000$ for this contractor! Yay!"

That's only scalable up to a certain point, and that point is usually reached when you realize that you need employees.

Just for example, I run a team of 10 machine learning engineers at a large ecommerce company. We mostly do NLP and computer vision, some time series forecasting.

I cannot imagine ever paying anything close to $25k for consulting advice, that’s just bananas to me. We recently purchased licenses to use the data annotation tool prodigy from the spaCy creators at explosion.ai. That was ~$4000 and the decision whether to build our own data annotation system or not was excruciating, involved all kinds of business documentation, RFCs, approvals, NDA processes, etc. It was deeply non-trivial to procure that, and building our own was a very serious option we pursued with tech specs and prototypes and everything.

Spending 6x that amount for _advice_ about NLP, which practically grows on trees today, is just totally unrealistic.

It makes me suspect the real target customer for you is not companies with actual ML engineering teams or ambitious data-driven projects, but more like someone looking for McKinsey-lite. Some place that has no serious ML use case beyond drop-in pretrained models and sees $25k as the cheaper path to rubber stamp certification that dissolves internal political feuds. Most likely just selling super cookie cutter NLP models as if they are advanced and represent some sexy leap forward for a company with a couple junior data scientists. Algolia or just some drop-in Elastic tfidf search is more than enough for these companies. Spend the $25k on an intern who can tell you anything you need to know about neural network frameworks.

In reality, the 4-5 ML engineers you already hired are very likely more knowledgeable than the freelance consultant you might hire. They can tell you much more about state of the art and simultaneously know the specific integration path in your company’s web service and data ecosystem. Those folks won’t be wasting time prototyping - they would be pursuing a more efficient way to get the answers you need than advice from a freelancer, even if that freelancer was Bengio for pete’s sake.

I just cannot see the value prop here except for the usual story of paying for consulting as a virtue signal / credential / politics kind of thing.

Sure, but there are plenty of business problems worth solving even if it takes millions. You're not paying for the person, you're paying for someone to solve your problem.

"Five thousand dollars," the artist replied.

"But, what?" the woman sputtered. "How could you want so much money for this picture? It only took you a second to draw it!"

To which Picasso responded, "Madame, it took me my entire life."

And no...that just isn't correct. It does vary by industry. Very generally, capital-intensive businesses have smaller productivity differences...but even then it is probably far less than you think (in some of those industries, experience curves are per employee). But, given the subject of the post, then yes...productivity can vary that much.

What is perhaps confusing you, and many companies get this wrong, is that they treat every employee like a superstar when (by definition) they can't be. It is far more profitable to hire five below-average people and teach them to be average than hire one superstar.

My point wasn't really about what companies should or should not do but how they should look at those decisions. Wages really don't matter. I will pay someone $1m tomorrow if they can generate $5m in sales. Simple.

But companies should think carefully before they go down this route. It mostly isn't worth it because most companies aren't very good at hiring and will lose the employee if they can't continuously provide a high productivity environment. Some places can retain staff but, again, there is an 95/5 rule about workplaces just as there is employees.

If 20 hours of someones time saves you 2 million though, it's a no brainer to pay them 20k to do it.

Some people absolutely do specialize in this sort of engagement, and do well at it. It's really important to remember that. $N/hour usually doesn't mean $N*2000/year salary. Often hourly rates go up because of the nature of the work means the ratio of billable hours to hours put in isn't great (see, e.g. many independent lawyers).

Also worth noting that most people contracting at this level don't in fact charge hourly but that's a different aspect.

Only if you discount equity, small companies are very unequal because the owners/founders will make orders of magnitude more in the event of a sale.

As for why. If a large company makes money from advertising and you improve their $10billion/year click model by 0.01% CTR then you've just made them a million dollars. Do that few times throughout a year and a million dollar payout seems low.

LLC is a business entity type. S Corp is a way to be taxed.

With LLC, life is simpler (formation, filing taxes, maintenance). With S Corp, you can distribute the profit as dividend (at a lower tax rate) after paying yourself a reasonable salary.

Edit: I just noticed that you'd posted the original comment in this thread. So, LLC electing to be taxed as an S Corp means simpler filing requirements (as opposed to C Corp and the like), coupled with the ability to distribute dividends.

The fact that such obvious, severe bugs make it through the release process likely means that there isn't really much of a release process. And what's in place doesn't even test the release on totally bread-and-butter models like resnet50.