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The Future is Coming Faster Than You Think - Charles Myslinsky
Manage episode 301800546 series 2979578
Hey everybody---My guest today is charles mylinsky--- charles was part of the original team that started jet.com and later sold to walmart for 3.3billion. He is now the chief product officer at OJO Labs. I recently talked with Chris heller and found out that he joined ojolabs. Why would top talent like these two guys join a chatbot company?
I am always compelled to look deeper when I see something that looks interesting
OJO labs started in 2015 and to date has raised 134M in venture capital. How does a chatbot raise 134M dollars and capture top tier talent? The simple answer is ---it doesnt---- not unless you have much bigger plans.
with the recent news that zillow recently announced they were buying showingtime for 500M dollars. Zillow has made it very apparent that they are trying to be the bridge between an agent and a consumer.
Looking a little deeper i found that OJO has a strategic partnership with wolfnet technologies which has 100Million property profiles similar to the size of zillow property database. They acquired Movoto which is a top 5 home search portal with about 20M unique visitors a month and coincidentally the the second-largest fully licensed online real estate brokerage in the US and realsaavy which is a web development shop with a unique search function.
and their whole business seems to rest on two patents around machine learning and distributed networks. Machine learning or AI------ it needs tons of data for it to work..
Understanding that OJO’s mission to help people make better decisions through the fusion of machine learning and human intelligence? Which is actually human assisted machine learning where humans are doing a lot of the sorting and matching as the machines in the network learns. Put in enough conversations and you hit an inflection point where a machine is conversant. It can answer many routine and maybe non routine questions in a home sale or purchase.
I dont actually know anything about what this company is doing or planning but, all of this seems super interesting. Imagine You had a search portal with 20M uniques,, a zillow sized property profile database and you own a web development company and the only thing missing is millions of conversations for your AI to become conversant?
Imaging all those pieces together and what is possible is exciting. I wonder how you might arrange them in order to capture, record and sort millions of conversations. You need an end to end marketplace---ebay, airbnb, etc. The thing with marketplaces is that they are incredibly difficult to build---the good news is that if you can build it they are indestructible as long as both the buyer and seller are capturing value. The foundation of a marketplace is trust and the sad piece of truth about this industry is that there is too much agent turnover for most consumers to distinguish between a good agent or a bad agent. Good information or bad information.
If agents would like to get qualified consumer leads it makes sense that the consumer would want a qualified agent lead--- could a personalized and customized technology solve both sides of the equation. OK--enough of my rambling
417 episódios
Manage episode 301800546 series 2979578
Hey everybody---My guest today is charles mylinsky--- charles was part of the original team that started jet.com and later sold to walmart for 3.3billion. He is now the chief product officer at OJO Labs. I recently talked with Chris heller and found out that he joined ojolabs. Why would top talent like these two guys join a chatbot company?
I am always compelled to look deeper when I see something that looks interesting
OJO labs started in 2015 and to date has raised 134M in venture capital. How does a chatbot raise 134M dollars and capture top tier talent? The simple answer is ---it doesnt---- not unless you have much bigger plans.
with the recent news that zillow recently announced they were buying showingtime for 500M dollars. Zillow has made it very apparent that they are trying to be the bridge between an agent and a consumer.
Looking a little deeper i found that OJO has a strategic partnership with wolfnet technologies which has 100Million property profiles similar to the size of zillow property database. They acquired Movoto which is a top 5 home search portal with about 20M unique visitors a month and coincidentally the the second-largest fully licensed online real estate brokerage in the US and realsaavy which is a web development shop with a unique search function.
and their whole business seems to rest on two patents around machine learning and distributed networks. Machine learning or AI------ it needs tons of data for it to work..
Understanding that OJO’s mission to help people make better decisions through the fusion of machine learning and human intelligence? Which is actually human assisted machine learning where humans are doing a lot of the sorting and matching as the machines in the network learns. Put in enough conversations and you hit an inflection point where a machine is conversant. It can answer many routine and maybe non routine questions in a home sale or purchase.
I dont actually know anything about what this company is doing or planning but, all of this seems super interesting. Imagine You had a search portal with 20M uniques,, a zillow sized property profile database and you own a web development company and the only thing missing is millions of conversations for your AI to become conversant?
Imaging all those pieces together and what is possible is exciting. I wonder how you might arrange them in order to capture, record and sort millions of conversations. You need an end to end marketplace---ebay, airbnb, etc. The thing with marketplaces is that they are incredibly difficult to build---the good news is that if you can build it they are indestructible as long as both the buyer and seller are capturing value. The foundation of a marketplace is trust and the sad piece of truth about this industry is that there is too much agent turnover for most consumers to distinguish between a good agent or a bad agent. Good information or bad information.
If agents would like to get qualified consumer leads it makes sense that the consumer would want a qualified agent lead--- could a personalized and customized technology solve both sides of the equation. OK--enough of my rambling
417 episódios
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