Element AI sold for $230M, as founders saw value mostly wiped out: document(theglobeandmail.com) |
Element AI sold for $230M, as founders saw value mostly wiped out: document(theglobeandmail.com) |
It looks like this company raised ~$250M, and sold for $230M. This isn't any sort of nefarious "founders got wiped out"; they sold for less money than they raised and this is the typical outcome in that situation.
Also, from the article:
> As part of the deal – which will see ServiceNow keep Element AI’s research scientists and patents and effectively abandon its business – the buyer has agreed to pay US$10-million to key employees and consultants including Mr. Gagne and Dr. Bengio as part of a retention plan.
This is the kind of "everyone got wiped out but the founders got an out-of-band parachute" that highlights an often-understated difference between "founders" and "just an employee". Even with a terrible outcome, founders and top executives can still manage to come out on top, and only employees actually get wiped out.
Submitters: "Please use the original title, unless it is misleading or linkbait; don't editorialize." https://news.ycombinator.com/newsguidelines.html
Briefly, cash was needed and investors were found at an relatively early point. The agreement set a value (V) under which the proceeds would benefit the preferred shareholders (the new investors). After the agreement was signed, the new investors joined forces with another investor achieving control. A new entity was set up, controlled by the investors, and they used their control to force the sale of the company at V, instantly destroying all common shareholder value.
All of this was announced (in a rather gloating manner) by the new head of the company. "You report to me now", basically.
This did not sit well with the staff, who had been working hard for a while. I walked that day, and within a few days the entire engineering team did as well.
For their $5 million, they got office desks, a bunch of PCs, and some half-built software they had no idea what to do with.
I suppose that did not work out particularly well for anybody ;)
I suppose when they sold it off it was a good deal for some other startup that needed office equipment.
> I suppose that did not work out particularly well for anybody ;)
Sounds like the staff who quit probably enjoyed their revenge, and also got a great story out of it.
This is the sort of middle ground where it's not a complete disaster but some value is still generated out of the entity.
Yet they always do the opposite. As if the founders have a clue how the software/deployment/support system works at all.
They basically acquired a world class AI team. Had they let the company die I'm certain most of the AI talent there would have been poached rather quickly.
Presumably they were paid a salary while they were working there.
Having to find a new job now sucks, of course, but I find it hard to say they got wiped out.
This has led to countless headaches. The skills needed to solve these challenges are very different. My more academically minded colleagues don't seem to have the intuition for seeing around this corner and understanding what it takes to build something that actually works.
This is a long way of saying: if a company is founded by and ran by such academics, I can see how this would be a recipe for disaster when it came to actually building and shipping products.
Still, they have $300,000 worth of shares and "the buyer has agreed to pay US$10-million to key employees and consultants including Mr. Gagne and Dr. Bengio as part of a retention plan".
Business doesn't always work out. Getting a cool $1M after trying and failing is not the worst thing that could happen.
They have a world class expertise.
This has been bothering me for a while. Ridiculous sums of money invested purely on name recognition, in particular in 'AI', rather than allocating capital to people who actually at least have a product.
If you’re a founder and you ever are doing a big round try to seek for secondary liquidity. It’s fair for founders to have some way to build financial stability when they are sacrificing so much. Secondary liquidity terms are way common nowadays than they used to be.
Element AI founders should have done that when they did their Series B and raised 150MM.
I know there are some purists that think that giving founders secondary liquidity can remove certain accountability, but honestly if you’re investing in a group of people and you believe giving them some reasonable path to partial liquidity pre-exit, you probably have some fundamental doubts on that investment and that group of people. I feel that nowadays that’s basically a litmus test for investment strength.
It makes me sad thinking of all the brilliant colleagues I had that were simply wasting their talent.
Element AI's product was it's talent (mostly all grads from MILA just next door) and the fact it's one of the few places where you could get as many great devs and deep learning experts in the same room.
But as long as folks focus on AI and not building an ecosystem or a product we'll see these acqui-hires.
https://old.reddit.com/r/MachineLearning/comments/khin4c/n_m...
A wildly successful compensation outcome for an employee at a startup would be ~75% of a comparable market salary plus a few hundred thousand dollars of post-tax income from equity after 8-10 years, amounting to say $25,000 per year of equity/bonus.
At a totally mid range tech company or large company with some technology, you would make 100% of that market rate and probably make $25,000 just in a bonus per year, plus an additional $30k to $50k of RSUs per year, which you don’t have to wait 8-10 years to sell.
In an extremely conservative estimate, you’re probably losing $200,000 of post tax equity / bonus income over 8 years by choosing a startup over a mature but run-of-the-mill tech company or other large company that has tech teams.
If your market salary is ~ $150k, the startup is probably paying $115k and will give worse raises and promotions. Conservatively that’s another $180k of lost post-tax income over 8 years (35k delta times 8 minus taxes).
In total that’s about $400k of lost post-tax income by working at the start up for 8 years, and the alternative I’m comparing to is a very conservative estimate $150k base, $50k equity $25k bonus assuming no stock growth or raises, which is a totally run of the mill offer in large cities even 5 years ago for roles with 3-4 years of experience.
And that’s under a huge IF the startup is wildly successful and all those options are actually worth a few hundred thousand after taxes. That’s a rare outcome. They could be worth nearly nothing, and then you’re talking about foregoing around $550k of post tax income over 8 years (225k all in per year minus 115k at a failing startup, per year, x8 then less taxes).
Is the high, high risk of leaving $550k on the table worth it for vague promises of “interesting work” that isn’t guaranteed?
So they got the idea, that they could hire other STEM Master's and PhD's, offer them an average software engineer salary and train them up to become good software engineers. Many software engineers scoffed at the idea - so you're hiring people from Physics to do web apps - pfff, what do they know about creating software?
Well, I'd say a third of my coworkers at the time were from "academia" - I felt absolutely no difference. The company is thriving and they have more than quadrupled in size since I worked there five years ago.
Indeed. I founded a contactless personalized food production and automation robotics venture in 2016. We tore through specialist and generalist employees alike trying to make progress toward a final prototype. We're now essentially there, but lost the team over COVID, while the investment environment for our technology is currently extremely hot. In taking stock of the situation to move forward, I've realised it's actually a blessing: the skills needed to reach mass-production on a very complex assembly are very different to the skills needed to iterate early and mid-stage prototypes rapidly and effectively. It would have literally been harder - and more expensive - to drag people 'over the line' in to production than to just hire a new team.
What do you believe the go-to-market strategy is of those two?
For C3.ai, my interpretation was they're trying to become the Microsoft of ML, re-packaging the entire ML pipeline into a more consumable developer experience. They seem currently focused more upon the model analytics part of the pipeline than say, training data selection or ETL to ingest raw data (whether to train upon or run in production), or any number of other pieces of the pipeline.
Landing.ai appears to be more tightly focusing their messaging on the operational aspects of the ML pipeline, though not so much the modeling part that C3 appears to emphasize. They also seem to very tightly narrow their focus on machine vision ML.
I'm probably wildly off though, having had no access to the actual platforms and ever used them in anger.
The event was strange and really disabused me of many assumptions I had about startup origin stories.
https://www.forbes.com/sites/alexkonrad/2020/12/09/billionai...
C3, Palantir, Salesforce and probably a few other companies / consults are investing in this space. It's the whole integrate all the data silos into some cloud, public or private, with +snowflake style data storage and build your business around data in order to drive real time feedback to solve customer problems.
But you are not wrong, "productizing a data processing pipeline" is big business.
The value of Element AI was that by buying it you get all the employees. Had they waited for the company to go bankrupt they would be competing with everyone else trying to buy the IP and the key employees.
In many ways, hiring advanced degree holders is a crapshoot. They have skills you probably can't train, but often times come in with fewer software development skills than your undergrad intern, despite theoretically having more years of experience. You don't need to know git to publish in IEEE, or write unit tests or readable code, or debug an edge case, and your only code reviewer is a professor who doesn't care about this either. 'Good enough to publish' is a far cry from 'customers will pay for it.'
I'm not saying that the OP should hire these people, or that they would even be good hires. Maybe they don't want to take the risk or time of training up a new employee.
It just seems like a bit wierd non-sequitur to draw conclusions on someone's learning capabilities based on their performance in an interview outside their usual domain.
1) Academics lack
2) Are hard to pick up
E.g. another commenter mentioned Git. Many academics don't use version control, but I think learning how to do this to an acceptable level is not very difficult, so it doesn't really count.
I'm asking because I might be trying to get a job in industry soon.