EAT: The End Game for Big Data

There was a code 20 years ago, and you either cracked it or you didn’t.

Google cracked it.  Yahoo didn’t.

Facebook cracked it.  Myspace didn’t.

Amazon cracked it.  Pets.com didn’t.

It was the difference between success and failure in the internet age, and it minted half of the multi-trillion dollar companies that have ever existed. 

That code was the power of data.  The ability of an internet company to track its users, learn from their behavior, and find a way to monetize these insights.  This development – predictive analytics - was the basis of Web 2.0, and in a sense, it is the internet’s true killer app. 

You might have known all this, but there’s something you might not know: that there’s a code today, too.  Or more accurately, there is a code tomorrow that we’re telling you about today.  And for all of the trillions earned since the world moved online, believe us when we say it will dwarf everything that came before it.

Today, the code is real world data, and when it is cracked, it will do for the entire economy what Web 2.0 did for the internet.  

Amidst the speculation on what is coming, the purpose of this post is to tell you what 375ai has already done.  We have built a network of edge AI nodes to extract actionable data from the analog world.  We are processing real world phenomena into saleable insights.  And we are using AI to monetize reality itself.  

20 years ago, they told you that data was the new oil.  Today, we’re telling you that edge is the new data.  We’ll explain all of this, so read on below to learn more.  

Join us as we finish what the internet started.

As 375ai approaches mainnet, there are few questions we’d like to answer.  This post is aimed at clarifying:

1. The limitations of the current data market
2. The role of edge AI in producing real world data
3. Current and future use cases
4. Implications for 375ai and the $EAT token

Let’s begin:

The Limitations of the Current Data Market.

By now, most are aware of how Web 2.0 companies work.  Take Facebook, for example.  We know that they capitalize on massive amounts of user data.  We know that if a product is free, you’re the product.  And we know that Meta builds a profile on every one of its 3 billion users, using its unique insight into their tastes and desires to make $160B a year by predicting which users will click which advertisements.

What fewer people think about is how this extends beyond social media.  Indeed, with enough data to make shrewd choices, it’s hard to think of a single business that wouldn’t experience radical gains in efficiency.  The model can benefit anyone. 

Consider Amazon.  While it was founded to sell goods online, its true innovation was improving logistics by predicting the items you are most likely to buy.  To use a crude example, if they know a smoke detector battery lasts for 18 months, and they know you bought one 17 months ago, they can recommend batteries on the home page and stock them in the nearest fulfillment center before you hear the first chirp.  This ruthless efficiency – based entirely on analyzing usage data - is how they increase sales, limit excess inventory, maximize efficiency and ultimately improve profitability.

But while predictive analytics has become an integral part of the 21^st Century internet, its use by real world business is constrained by a scarcity of data.  When 100,000 people visit a page on Facebook, Meta effortlessly records their demographics to produce actionable insights.  For a brick-and-mortar grocery store, however, whose customers visit in real life, measuring and analyzing the qualities of in-store visitors has never been possible. 

Considering the overwhelming majority of human activity still takes place offline, this is a massive and largely untapped market. Every real-world industry could benefit from data-driven insights, but today none of them can extract the data necessary to do it. Now, thanks to a confluence of factors – advances in computer vision, optimizations in AI compute, and improvements in hardware design – this is possible with the use of edge AI. And this is how the 375ai network produces real world data – by replacing web traffic with real traffic.

The Role of Edge AI in Producing Real World Data

In brief, 375ai is a decentralized data intelligence network.  It produces real world data through a combination of hardware and software components, voluntarily operated by a community of over 200,000 users.  Utilizing the DePIN model, node operators are compensated for their efforts with token rewards and incentivized to further build out the network.  This approach has enabled it to scale significantly faster and farther than similar, more centralized competitors.  

The core components of the network are as follows:

375edge:  375edge nodes are the primary hardware component of the network - sophisticated edge computing devices that contain a variety of video, audio, and environmental sensors.  Enterprise deployers install nodes alongside American roadways, where they capture live feeds of traffic conditions below to produce vast amounts of multimodal data.  

Using computer vision and object detection models, raw data is then extracted from the footage before being processed into summarized insights by Nvidia GPUs.  This produces datasets with information on millions of vehicles per day – speed, make and model, state of origin, number of occupants, the carrier on commercial vehicles – as well as a host of other data points about atmospheric and roadside conditions.  Obvious items like weather and ambient volume are recorded, in addition to more sophisticated observations like congestion or the prevalence of erratic driving. 

Going into launch, 375ai has partnered with Outfront Media, the world’s second largest roadside advertising company.  Through this partnership, the network will have access to 40,000 billboard locations across the US, overlooking 7 out of 10 Americans on any given day. 

375go:  375go is a free mobile app that acts as an auxiliary software component for the network.  Users download 375go and run it as a background app on their phones, where it passively gathers a variety of data.  While edge nodes extract data from live feeds of the physical world, however, 375go focuses on data types that can be captured with the hardware of an ordinary smart phone.  The user’s phone measures things like network coverage and signal strength, which it can map based on location as the user moves around over time.  In the future, 375go will also gamify the collection of real world data, incentivizing users to capture vehicular traffic data to complement the efforts of 375edge. 

The cumulative result of these two components is a 24-hour livestream of America’s roadways, with AI working to extract hundreds of relevant data points about millions of vehicles per day.  It is a comprehensive view of where people and cargo are moving in the real world - insights for which there is both a robust and underserved current market, and a frankly unimaginable number of future revenue streams. 

Current and Future Use Cases for the Network

375ai  is fortunate to have built a product with existing demand, and even more uncommonly, begun satisfying it well in advance of token launch.  A variety of data buyers already exist for vehicular data, which 375ai offers at drastically higher quality than any existing competitors.  

To name a few:

1. State governments and departments of transportation are avid consumers of this data, as it is invaluable when making decisions about city planning or improving traffic flow.  If roadwork needs to be done, data can report the lowest traffic time to schedule it.  If a bridge needs to be built, data can inform the best location to minimize congestion.  When a gas station needs to be placed, planning departments can decide which locations to issue permits based on anticipated demand.
2. Hedge funds and other financial institutions purchase this data to give themselves an edge, so to speak.  As nodes have currently been placed in active port cities, traffic in these locales functions as a strong proxy for business performance.  Creative investors can use different data points to forecast upcoming conditions well in advance of economic data being formally released. 
3. Finally, in the AI world, there is currently a gold rush underway for large amounts of data that can be used to train and power physical AI.  Autonomous cars, for example, depend on elaborate computer vision and object recognition models, which require training to foresee collisions, recognize pedestrians, and stop at appropriate signage.  375ai is currently the only vendor producing datasets at the level of quality necessary for training at the highest levels. 

Through customers like these, 375ai is entering a market currently valued at $27B and projected to grow by 1,000% over the next seven years.  While these revenue streams would be enough to justify a company’s existence in themselves, new uses will invariably proliferate much as the landscape of Web 2.0 companies appeared after the introduction of large-scale web data.   To name only a few:

1. Targeted advertising will enter the physical world as data becomes available on real life “users.”  Billboards will be able to show different ads depending on the demographics of current drivers, as well as tailor their content based on roadway conditions (weather, congestion, or even the public mood.)
2. Law enforcement will improve dramatically as access to vehicular data gives them video evidence of crimes and accidents, assists in tracking stolen cars, and alerts local police instantaneously when serious crimes are committed.
3. Insurance companies will use vehicular data to refine their pricing models, as risk can be better assessed with more sophisticated insights.
4. Brick-and-mortar retailers will benefit from access to data, modifying inventory based on predicted demand, changing hours based on times of heightened activity, and devising marketing strategy based on customer demographics. 

As a final note: While we’ve given a short list of concrete examples here, we emphasize that the network is not limited to a short list of concrete uses. It is better viewed as a base layer for digitizing the real world, which can be used to capture any kind of data for any type of use.  Indeed, while 375edge nodes are currently trained to recognize and extract specific data points from vehicular traffic, other AI models can simply be trained and implemented for radically different purposes with no change to the underlying hardware.  Put simply, there is no limit to how the network can be used, and it truly is a method for extracting every kind of data from every facet of physical reality.     

Implications for 375ai and the $EAT Token

As demand for real world data grows, 375ai is well-positioned to provide it.  Its advantages are twofold:

1. DePIN networks are particularly well-suited for gathering data at scale
2. Edge AI solves the cost prohibitiveness of extracting and processing it

Because DePIN networks coordinate between disparate actors to bootstrap themselves, they tend to be more dispersed than centralized counterparts.   This is useful when it comes to capturing real world data, as the more widely a network is distributed, the more of the real world it manages to capture.  Additionally, by spreading the cost of upfront investment between a larger number of participants, DePINs tend to scale faster in high capex industries. 

With this said, the driving force behind 375ai’s growth has been its ability to capitalize on recent advancements in edge AI.  In the past, these processes would have required transporting colossal amounts of recorded media to the cloud, where inference could be performed to extract and process meaningful data.  Doing so was uneconomical, which is why it hasn’t been done. 

Today, however, edge computing has advanced to the point where these tasks can be performed near or at the source of data.  Through rigorous optimization (e.g. through quantization, model compression, and hardware/software co-design), efficiency has improved to the point that computer vision and object detection models can identify and extract relevant data onsite, and additional models can then process it into structured datasets.  With the cost of performing inference onsite markedly lower than doing so in the cloud, a whole world is thus made available for data mining, and a world of insights available for sale.

As a final note, it’s worth pointing out that Web 2.0 blue chips like Amazon and Facebook were able to dominate their respective markets because they owned the platforms on which data could be gathered.  The real world, however, has no owner and thus no monopoly over its data.  The closest equivalent is a company that controls the method of extraction – precisely as 375ai does.  The Big Data market – estimated at $327B in 2023 – is projected to grow to $862B by 2030. Suffice it to say, 375ai is well-positioned to capture a significant portion of this market as the leading source of a newly available class of data.

To close, we believe this is halftime in the internet age.  We believe the data revolution changed everything online, and it’s about to expand outward into brick-and-mortar business.  We are standing at the edge and ready to actualize our vision of the future.  This is the end game for Big Data: first the internet, then the world.