Markets are on pace for four straight years of double digit gains with the AI trade lifting the S&P 500 and Nasdaq in the first half of the year and the investment trade does remain just as red hot in private markets.
The AI races leading to the rise of mega IPOs.
SpaceX has already launched while investors are eyeing anthropic and open AI now elsewhere voting crowdfunding crossing 100 million from retail investors and the mobile rewards users for everyday smartphone activities.
Now the platform.
Packages that user activity into a behavioral data supply chain sold to AI labs for model training.
We here to break down AI funding along with a humanoid robot is Dan, CEO and co-founder of voting.
Dan, great to have you at the exchange.
Thank you so much for joining me.
Thank you for having us.
Well, we all know that artificial intelligence as a theme in terms of investing as well as technology has been in the spotlight in 2026.
But when we're talking about training for AI, what does this mean and what about privacy?
Yeah, great questions.
Well, first and foremost on the training side, so there's 3 major things that you need for an AI model to work, right?
There's compute, there's the algorithm, and then there's the human data part.
That third part is the most important part.
And essentially these AI labs have essentially sucked up the internet from the last 20 years, and they need a lot of refinement to those models.
So this creates a really nice opportunity.
For consented data, so this really ties into that privacy idea because essentially people can get paid for being able to help train the next generation of AI and essentially that allows them to not get necessarily displaced by AI but be part of that economy right and we see that as a multi-trillion dollar human data market that consumers can take advantage of just by everyday tasks essentially.
Yes, so I do want to expand on that right here on the floor of the New York Stock Exchange.
We do have your robot here, this humanoid robot.
So when we're talking about human data training, what do you mean?
Because all of us, we might use an LLM, we might be on chat GPT or Gemini or Cloud.
So what does it mean?
Yes, so yes, this little mode bot here, you know, we partner with a company and you think about like the future generation of robots are going to be what are they.
They're going to be doing household tasks.
They're going to be doing these things at our home and right now they need to be trained on how to do that appropriately.
So this thing does a lot of really cool things, but it's not quite there yet when it comes to folding your laundry, doing chores, picking up like a dog poop bag.
And so the opportunity is in how you can train that is by taking content and videos so people can use their smartphones simply by doing everyday household chores.
This is the same thing that is happening in the AI space for the Gemini.
In the opening eyes of the world to get an agent made, so these labs need refinement data, and that means necessarily new data.
And so one of the things that we're really focused on is really allowing those consumers to get rewarded for doing that, and that will ultimately help build this next generation of robots, and people can make thousands if not tens of thousands of dollars a year by taking part in this movement.
So Dan, two things I want to ask you where we are in terms of the timeline, but also when we're talking about training.
Robots, can you actually walk us through what happens in the whole process?
Yes, so I mean there's various ways to go about it.
There's companies like manufacturing companies right now where people are literally taking these little prompters on their head, these gloves that you can kind of feel, so that essentially that then goes into an algorithm so it can learn essentially and the model refines itself like what is good, what is bad.
It's called refinement learning.
The way that we're really focused on it is by allowing consumers to do that even through.
Smartphones because not everyone is going to be at a manufacturing house and so on.
Once we get that data, we then pass that to the Frontier Labs, those robotic companies, and then they essentially with their AI researchers improve those models over time and then you will see a constant improvement just like you saw Chat GPT 4 is drastically different than the chat GPT that we have today, and that's constantly moving at the speed of the fastest space I've ever seen in my life, much faster than crypto or anything.
Before, you know, and Dan, when we're talking about some of the tasks that you mentioned for this humanoid robot that's on the floor right next to us, it might be doing dishes or folding laundry, taking the dog out for a walk when it's super hot outside or freezing.
So how far away are we before the consumer might be able to purchase one of these humanoid robots?
Well, there's there's several companies out there that are already having robots that are actually about to come.
Market, you can buy this unitary robot right now.
It's more of like I would say a nice to have as opposed to a lot of it right now is really focused on training.
There are certain companies like Amazon that are using the robotic dogs to kind of map the perimeter because it has good sensors and stuff, so I think we are probably at least another 5 to 10 years away where you're going to see robots really in every kind of household, at least like higher end households, and I think that 15.
From now everyone is going to have one of these.
It might be a little bit sooner, but in the meantime, you know, there's still a lot of data that really needs to be inputted into these models to get there, both on the AI agent side and on the robotics side as well.
And Dan, for our viewers out there who might not be as familiar with some of these AI data labs, where are they and what is the market opportunity right now?
Well, I mean this is the biggest army race in history, right?
So these Frontier Labs are $1 trillion companies.
They haven't gone public yet, but they will soon and at this moment in time they are all racing to essentially have the best models out there so this opportunity is really interesting because one like in the software all this data was taken essentially from these platforms and Anthropic and these companies were sued by the Wall Street Journal and all these major data providers and now consumers can essentially take advantage of being able to provide consent to data and part of that.
You know economy being a part of that.
So you know this is one of the biggest movements.
This is like since the industrial revolution in my opinion.
And finally, Dan, before I let you go past, present, and future, I do want to ask you about why crowd funding versus other avenues and also what is your vision for the future?
I understand that on the Nasdaq you have reserved a ticker symbol, is that correct?
That is correct.
Yes, we have mode reserved MODE.
So yes, the retail crowdfunding for us was really this idea.
Building alongside Main Street, we allowed our consumers actually to become investors in the company and we have amassed over 60,000 shareholders and raised about $100 million through that mechanism and we see this as an opportunity if you really think about OpenAI, anthropic SpaceX, retail was shut out until it was $1.75 trillion valuation and that's usually reserved for institutional and accredited investors.
So our whole thesis has been we love Wall Street, but we really focused on the main street.
And our business model is a bit unique, you know, paying people to use their phone and doing all these things.
So we've always been kind of a company for the consumers, and it's worked out very well for us.
And every time we do an earnings call, we have like 10,000 people join it, and so we're very excited to eventually be able to take the company public and bring all of these investors that believed in our journey along the way.
Dan, we will have to leave it there for today, but thank you so much for joining us and for also bringing your humanoid to the trading floor of the New York Stock Exchange.
Thank you.
