The Intelligent Edge: 10 Stocks to Buy as AI Moves Off Our Screens and Into Our World
The new John Deere X9 1100 combine harvester costs upward of $900,000. And it can harvest 39 acres of corn in an hour… without its human driver touching the steering wheel.
And the machine isn’t just driving itself. Cameras on the front attachment scan each plant as it passes – thousands of images a minute. An onboard processor flags areas where the crop is more green or more mature, for example, and adjusts the threshing power accordingly… all before the combine has moved another foot down the field.
Many combines operate miles from the nearest cell tower. For any machine that needs a network connection to think, no signal means no intelligence.
But not in the John Deere X9. It doesn’t send data to a distant server for processing, as ChatGPT does. The intelligence is programmed in before the combine leaves the barn – and it runs entirely on its own from there.
In one of the world’s most advanced auto plants, in Dingolfing in southern Germany, the same principle is at work.
This is where BMW turns flat sheets of steel into car body panels – the doors, hoods, and fenders that will eventually become a 7 Series or an iX electric SUV. The stamping machines are enormous. The margin of error is tiny. And the line moves fast.
After each part comes out, it has to be inspected. The concern is hairline cracks – fractures so fine they’re nearly invisible, but serious enough to compromise the structural integrity of the finished car.
For years, BMW used camera systems to catch them. The problem was false positives. Dust particles and oil residue on the metal surface looked enough like cracks to fool the system. Parts that were perfectly good kept getting flagged, pulled from the line, and manually inspected – slowing everything down.
The fix was a neural network trained on roughly 100 real images of each type of feature it needed to evaluate. Once it had enough examples, it could tell the difference between a genuine crack and a speck of dust with a reliability no previous system could match.
Today, that analysis happens in milliseconds, at the point of inspection, on every part that comes off the press.
There’s no time to send an image to a server and wait. By the time an answer came back, the next part would already be under the press. The intelligence must live at the line – in the device, at the moment the decision needs to be made.
The car in your driveway may already work the same way.
A Tesla navigating a busy highway or intersection has to spot pedestrians, read traffic signals, judge the speed of oncoming vehicles, and decide whether to brake or accelerate – all simultaneously, in under 100 milliseconds. That’s as fast as the blink of an eye.
If it had to send that data to a server and wait for a response, the car could crash before a response arrived. So it doesn’t.
The same way the combine harvester carries its intelligence into the field, and the BMW plant runs its inspection at the line, a Tesla carries its AI onboard – processing everything locally, in real time, at highway speed, whether there’s a cell signal or not.
The combine, the auto plant, and the self-driving car all run on the same idea. The AI lives in the machine… not just in a data center. Engineers call it Edge AI – and it’s shaping up to be the next major chapter of the AI boom.
In this report, I’ll recommend 10 stocks positioned to profit as Edge AI spreads from factory floors and farm fields into virtually every corner of the economy. All while most investors are still chasing data center plays and wondering why the returns aren’t what they used to be.
First, let’s get clear on the difference between cloud-based AI and Edge AI.
Cloud AI vs. Edge AI: What’s the Difference?
Most people think of AI as something that lives in the cloud. You type a question into ChatGPT, your words travel to a data center somewhere in Virginia, Washington State – or even farther away – get processed by banks of Nvidia chips – and an answer comes back to your screen a second or two later.
That works fine for a chatbot.
It doesn’t work for a combine harvester making thousands of agronomic decisions a minute in a field with no signal. It doesn’t work for a factory line where the next part arrives before the server has time to respond. And in a self-driving car, a half-second delay could be the difference between a safe stop and a crash.
The list goes on.
- In operating rooms, robotic surgical systems guide instruments in real time – adjusting for a surgeon’s hand tremor, flagging tissue boundaries, responding to movement faster than any human reflex could. A round trip to a remote server could add hundreds of milliseconds of delay. In surgery, that’s not acceptable.
- In airports and transit hubs, AI-powered security cameras analyze crowds in real time – flagging anomalies, tracking movement, identifying potential threats – without transmitting every frame to a distant server for processing. The analysis happens at the camera.
- On power grids, AI systems monitor thousands of sensors simultaneously, detecting early signs of faults and rerouting electricity in milliseconds – faster than any human operator could respond, and faster than any cloud system could process the request.
- On satellites in low-Earth orbit, onboard processors analyze imagery as it’s captured – identifying wildfires, flood zones, or shifts in crop conditions – and transmit only the relevant findings to the ground, rather than raw data that would overwhelm any downlink.
- And on the battlefield, in a combat zone there is no cloud to rely on. When an enemy jammer cuts its signal, a military drone keeps flying. The intelligence was already there before it left the ground.
Each of these is a different industry… with different applications… and different sets of stakes. But they all share the same basic requirement. The AI has to be there already, in the machine, ready to act before there’s any time to ask for help.
Think of it this way. Cloud AI is a genius locked in a room miles away. You can call him, but there’s always a delay. Edge AI is a capable deputy riding along in the machine – always present, always ready, never dependent on a signal.
And that shift from thinking in the cloud to thinking at the edge is about to matter enormously to investors.
The AI Boom Will Play Out in Three Waves
The first wave of the AI boom was about building the infrastructure – the data centers, the power systems, the Nvidia chips that made large-scale AI possible. That’s already a crowded trade.
The second wave is software – established companies embedding AI into products people already use every day. Microsoft put Copilot in Word and Excel. Salesforce put AI in its customer relationship management (CRM) platform. Adobe put it in Photoshop. That trade is also well underway, and already reflected in stock prices.
The third wave is deploying Edge AI in the physical world. And it’s just getting started.
The Edge AI market was worth roughly $11.8 billion in 2025. By 2030, it’s projected to reach $56.8 billion – nearly five times its current size. That’s a growth rate of close to 37% a year.
And the raw market size understates the opportunity. Unlike cloud AI – where a few massive companies control nearly all the spending – Edge AI demand is spread across the entire economy. Every autonomous vehicle. Every smart factory. Every airport and transit hub. Every hospital. Every satellite. Every battlefield. Every power grid. And eventually, every home.
That’s billions of devices, across thousands of industries, all needing chips, software, and systems to think for themselves.
There’s another important shift happening at the same time.
At first, most AI spending was going into training – the enormously expensive process of teaching AI models everything they know. Training a frontier AI model can cost hundreds of millions of dollars and requires the kind of computing power only a handful of companies on Earth can afford.
But the training is largely done. The models exist. Now comes the work of actually using them – running them out in the world, in real time, on real problems. Recognizing a face in a crowded airport. Flagging a defective part in an auto plant. Deciding whether to brake in a self-driving car.
Jensen Huang, Nvidia’s CEO, put it plainly at CES in January 2026: “The ChatGPT moment for physical AI is nearly here.” He wasn’t talking about chatbots. He was talking about the billions of machines – cars, robots, factory systems – that are about to get a lot smarter.
Investors who got in early on Nvidia and the big cloud names did extraordinarily well.
From the start of 2023 to the end of 2025, Nvidia (NVDA) returned more than 1,100%. Over that time, Amazon (AMZN), which runs AI cloud infrastructure via its AWS division, returned 163%. And Google (GOOG), another large AI cloud infrastructure player, returned 252%.
The investors who get in on the second wave will own the companies supplying the intelligence that powers the physical world.
That’s what this report is about.
The ETF Gap
Whenever I look at a new investment theme, I don’t jump right to individual stocks. First, I see whether there’s a good exchange-traded fund (ETF) for this.
ETFs are the simplest way to get exposure to a trend without betting on a single company. And for most big themes – semiconductors, clean energy, robotics – they exist.
For Edge AI, they don’t. Not a pure-play one, anyway.
The ETFs that come closest are either broad AI funds stuffed with the same large-cap names you already own, or semiconductor funds. Those capture the chip angle but miss the software, networking, and application layers where much of the Edge AI value will be created.
That’s actually good news for investors willing to do a little more work. It means the market hasn’t fully packaged this opportunity yet. The money flowing into generic AI ETFs is largely missing it.
That’s what gives individual stocks the advantage here. To make sense of which ones to invest in, it helps to understand how the Edge AI ecosystem is structured.
The Edge AI Stack: Four Layers of Opportunity
Edge AI isn’t a single product or a single company. It requires a whole, interlocking supply chain.
Think of it the way you’d think about a smartphone. The chip inside has to work. The wireless connection has to be fast enough. The operating system has to be able to run the app. And the app itself has to do something useful.
Remove any layer, and the whole thing stops working.
Edge AI is the same. So, we’ve organized the recommendations below according to the four layers of that stack.
Layer 1: The Chip Layer. These are the companies designing processors built specifically to run AI models locally – on devices, in vehicles, inside industrial equipment – without routing every request back to a data center. What matters here is simple: run fast and sip power, rather than guzzle.
Layer 2: The Connectivity Layer. Edge AI generates enormous amounts of data. Moving it – between devices, back to the cloud when needed, across networks – requires specialized networking hardware and infrastructure. These companies build the pipes.
Layer 3: The Platform Layer. Chips and connectivity aren’t enough on their own. Someone has to build the software infrastructure that deploys AI models to billions of devices, manages them, updates them, and makes them work together. This is the operating system of the Edge AI economy.
Layer 4: The Application Layer. These are the companies turning Edge AI into real products – in factories, vehicles, hospitals, and retail. They’re closest to the end customer and often the most visible expression of where the technology is going.
With that framework in mind, here are the 10 companies best positioned to profit as Edge AI moves from the cloud to the edge.
10 Stocks to Profit as AI Moves to the Edge
Layer 1 – The Brains: Chips & Silicon
NVIDIA (NVDA)
Most investors know Nvidia as the company that supplies the graphics processing units (GPUs) powering the world’s AI data centers. But there’s a second Nvidia story that gets far less attention.
Its Jetson platform is the dominant computing brain for Edge AI – the processors that run AI models inside robots, autonomous vehicles, drones, and industrial machines. Jetson runs in a device smaller than a credit card and draws a fraction of the power needed to run cloud AI in warehouse-sized facilities.
But while Nvidia is best known for hardware, it offers another edge advantage, too:
Its Compute Unified Device Architecture (CUDA) ecosystem – the software that developers use to build AI applications. A model trained on Nvidia’s cloud GPUs can be deployed directly to a Jetson edge device with minimal rework. For a competitor selling just a chip, that’s nearly impossible to replicate.
The company generated more than $130 billion in revenue in its last fiscal year (FY2025), almost entirely driven by AI demand. Edge AI is still a small piece of that today. But as autonomous machines, smart factories, and AI-powered robotics scale from thousands to millions of units, Jetson becomes an increasingly important revenue driver.
Nvidia is the anchor name in this report – the one you already know, but with an Edge AI angle you probably don’t.
Qualcomm (QCOM)
Smartphones are one of the largest Edge AI deployments on the planet – billions of devices running AI models locally, handling voice recognition, camera processing, real-time translation, and predictive text without touching a data center. And Qualcomm’s Snapdragon chips are inside most of the world’s best Android smartphones.
But I’d argue the more interesting growth story is automotive. BMW, Mercedes-Benz, and General Motors have all adopted Qualcomm’s Snapdragon Digital Chassis platform to run AI workloads inside their vehicles – navigation, driver assistance, in-cabin personalization, and eventually autonomous driving features.
Qualcomm also belongs in the connectivity layer of this report. Its 5G modem business is deeply integrated with its Edge AI chips – the two technologies are designed to work together. A Snapdragon chip doesn’t just run AI locally; it decides in real time what to process on-device and what to offload to the network. That tight integration between compute and connectivity is a structural advantage that pure chipmakers can’t easily replicate.
The company generated roughly $44.3 billion in revenue in its last fiscal year. By mid-2024, its AI-related automotive pipeline was already valued at more than $45 billion in design wins — contracts where automakers have committed to using Qualcomm chips in future models.
For investors, Qualcomm offers exposure to two of the most important trends in Edge AI: on-device intelligence and automotive compute.
Apple (AAPL)
Apple rarely shows up in AI infrastructure conversations. But it should.
The Neural Engine inside every iPhone, iPad, and Mac is a purpose-built AI processor – designed from the ground up to run machine learning models locally, at high speed, on minimal power.
It handles Face ID, real-time photo processing, on-device Siri requests, live translation, and an expanding set of AI features that Apple groups under the “Apple Intelligence” umbrella.
More than 2 billion Apple devices run AI inference — the process of running a trained AI model to produce results — locally, without sending data to a server. That’s one of the largest Edge AI deployments on the planet. And it’s not just a pilot program or a future projection, either.
That scale matters for two reasons. First, Apple’s distribution is untouchable — push a software update, and new AI features are live on billions of devices overnight.
Second, it makes Apple’s device ecosystem increasingly difficult to leave. The more AI is woven into the hardware, the more switching to a different platform costs the user.
Apple generated more than $415 billion in revenue in its last fiscal year. Services – App Store, Apple Intelligence features, subscriptions – are its fastest-growing segment and increasingly tied to the AI capabilities of its devices.
For investors, Apple is the Edge AI story hiding in plain sight: the world’s largest deployment, dressed up as a consumer electronics company.
ARM Holdings (ARM)
Every chip in this layer – Qualcomm’s Snapdragon, Apple’s Neural Engine, and most of the processors inside edge devices worldwide – is built on ARM’s architecture.
Rather than manufacture chips, ARM designs the underlying instruction sets and processor blueprints that chipmakers license to build their own silicon. When smartphone, tablet, and edge devices ship using an ARM-based chip, the company earns royalty payments.
That business model is a huge advantage. ARM benefits from the growth of the entire Edge AI market without having to pick winners among individual chipmakers. Whether Qualcomm or Apple or a new entrant captures smartphone market share, ARM collects royalties.
The Edge AI expansion multiplies this advantage. As AI inference moves into automobiles, industrial sensors, medical devices, and consumer electronics, the number of ARM-based chips shipped annually grows with it. The company already licenses its architecture to more than 1,000 partner companies worldwide.
ARM generated roughly $4 billion in revenue in its last fiscal year – modest compared to the chip giants it enables. But its licensing and royalty model means revenue grows as unit volumes grow, with relatively low incremental cost.
For investors, ARM is the toll road of the Edge AI chip market. Every device that ships is another car through the gate.
Layer 2 – The Connective Tissue: Networking & Connectivity
Cisco Systems (CSCO)
Cisco may not be a flashy company that ships humanoid robots and launches billion-dollar AI projects. But what it does is more foundational.
It builds the networking hardware that connects edge devices to each other, to local servers, and to the cloud. Inside factories, hospitals, retail chains, and enterprise campuses worldwide, Cisco’s switches, routers, and wireless access points form the backbone of the networks that Edge AI will run on.
The company is actively repositioning for the Edge AI era. Its acquisition of Splunk – completed in 2024 for $28 billion – added AI-powered data analytics capabilities across distributed networks. Its industrial networking portfolio serves manufacturing and logistics customers deploying AI-driven automation at the edge.
Cisco generated $56.7 billion in revenue in its last fiscal year and pays a dividend – making it one of the more conservative holdings in this report. It’s not a pure-play Edge AI bet. But it’s a company that gets paid every time an enterprise builds or upgrades the network infrastructure that Edge AI requires.
If you want Edge AI exposure without the volatility of smaller, more speculative names, Cisco is a solid option to consider.
T-Mobile (TMUS)
Edge AI in its purest form is AI that runs on the device with no cloud connection or network required. And that’s true for some tasks. Face ID works in airplane mode. A Tesla makes split-second driving decisions without pinging a server.
But in practice, most real-world Edge AI deployments aren’t fully isolated from a network.
A smart factory floor might process sensor data locally – but it needs to aggregate results, push model updates, and sync with a central system. A fleet of delivery drones runs inference onboard – but uploads telemetry and downloads new routing models over a connection. A hospital’s AI diagnostics tool processes images on-site – but shares results across a network of facilities in real time.
In each case, the AI lives at the edge – but not in isolation. It needs a fast, reliable, low-latency connection to function at scale.
That’s where 5G comes in.
T-Mobile has built the most extensive 5G network footprint in the United States, with twice the mid-band spectrum coverage of its closest competitors. Mid-band 5G is the sweet spot for these “hybrid” edge deployments – fast enough to handle real-time data sync, with enough range to cover large industrial and commercial environments.
The company generated roughly $80 billion in revenue in its last fiscal year. Its enterprise 5G business – selling connectivity to manufacturers, logistics operators, and healthcare systems deploying Edge AI – is a growing revenue stream that most analysts are still not fully accounting for in their price targets for TMUS stock.
For investors, T-Mobile is the network layer that makes large-scale Edge AI practical – not the AI itself, but the infrastructure without which the AI can’t scale.
Layer 3 – The Platforms: Software & Operating Systems
Microsoft (MSFT)
The chips in Layer 1 run the AI. The networks in Layer 2 move the data. But someone has to manage the software – deploying models to thousands of edge locations, keeping them updated, monitoring their performance, and deciding what runs locally versus in the cloud.
That’s the platform layer. And Microsoft is one of its dominant players.
Azure IoT Edge is Microsoft’s answer to the deployment problem. It allows enterprises to run AI models directly on local devices – like factory equipment, retail terminals, medical instruments – while managing the entire fleet through Azure’s cloud dashboard.
A manufacturer with 10,000 machines across 50 facilities can push a model update to all of them simultaneously, monitor performance in real time, and pull anomalous data back to the cloud for deeper analysis.
Microsoft generated $281.7 billion in revenue in its last fiscal year, with Azure growing at about 34% annually. The edge component of that business is still early – but the same enterprises that already run their cloud operations on Azure now have the opportunity to deploy Azure IoT Edge, making expansion a matter of deepening existing relationships rather than acquiring new ones.
For investors, Microsoft is the platform layer’s safest bet: a company with the enterprise relationships, the cloud infrastructure, and the softwareto manage AI at the edge at scale.
Alphabet/Google (GOOGL)
Google’s Edge AI story begins with a number that most investors overlook: 3.5 billion.
That’s the rough number of active Android devices worldwide. Every one of them could be an Edge AI endpoint – running Google’s on-device AI models for voice recognition, camera features, real-time translation, and an expanding suite of Gemini-powered capabilities that process data locally rather than sending it to a server.
Google has also built a serious enterprise edge platform. Its Google Distributed Cloud product allows companies to run Google’s AI models at their own locations, disconnected from the public internet if necessary. It’s aimed squarely at industries with strict rules about where their data can be stored or processed – defense, healthcare, financial services. Sending information to a public cloud isn’t an option for these clients.
Alphabet generated $402.8 billion in revenue in its last fiscal year, with Google Cloud growing at around 48% in the most recent quarter. The edge platform business is a small piece of that today. But it’s growing into markets – defense, regulated industries, sovereign AI – where the contracts are large and the switching costs are high.
For investors, Alphabet offers the combination of the world’s largest Android edge deployment and a serious enterprise platform business that most analysts are still underestimating.
Layer 4 – The Applications: Who’s Actually Deploying Edge AI?
Palantir (PLTR)
Most of the companies in this report supply infrastructure. Palantir is different – it’s one of the first companies actually running AI at the edge, at scale, in environments where failure is not an option.
Its Apollo platform manages AI software deployment across hundreds of enterprise and government locations worldwide. It handles the coordination problem that makes Edge AI operationally complex: pushing model updates to hundreds of locations simultaneously, monitoring performance, ensuring continuity when network connections are intermittent, and rolling back changes if something goes wrong.
The defense application is where Palantir’s edge credentials are most striking. The company recently received Pentagon contracts to deploy its AI systems in forward battlefield environments – locations far beyond the reach of reliable cloud connectivity. That’s genuine Edge AI, operating under the most demanding conditions imaginable.
A word of caution:
Palantir is the most volatile name in this report. Its valuation reflects significant optimism about future growth, and its stock can move sharply on contract news or shifts in government spending priorities. It would represent a smaller position in most investing strategies.
But for those comfortable with that kind of volatility, Palantir is the most direct expression of what Edge AI looks like when it’s deployed in the real world, at serious scale, under serious conditions.
Tesla (TSLA)
Every Tesla being made is an Edge AI computer on wheels.
The AI models that handle Autopilot, Full Self-Driving, and all the real-time decisions a Tesla makes – lane positioning, speed adjustment, hazard recognition – run locally on the vehicle’s onboard computer. They have to. At highway speed, some decisions have to be made immediately. There’s no time to wait for a response from a data center.
Tesla’s Cortex supercomputer trains those models on petabytes of real-world driving data collected from its fleet. A petabyte is roughly equal to 500 billion pages of text – rivaling the entire print collection of the Library of Congress.
That’s driving data collected from as many as 9 million vehicles – every lane change, every near-miss, every setback the real world throws at a driver.
The trained models then ship to individual vehicles via software updates – the same deployment pattern that defines Edge AI at scale. In this sense, Tesla has been building and operating one of the world’s most sophisticated Edge AI systems for years, before most of the industry had a name for what it was doing.
Tesla’s valuation is tied as much to Elon Musk’s broader ambitions – humanoid robots, energy storage, autonomous ride-hailing – as to its current automotive business. That makes the stock volatile, and its price can move sharply on news that has nothing to do with Edge AI.
But on the specific question of Edge AI credentials, Tesla’s case is hard to argue with. It trains AI at scale in the cloud and deploys it at scale at the edge – in up to nine million vehicles, in real time, every day.
A Word on Risk
A few things worth keeping in mind as you decide whether to invest.
Edge AI is not a winner-take-all market. Competition in edge chips is fierce and moving fast. Qualcomm, Apple, ARM, and Nvidia are not the only players. And new entrants from China and elsewhere are closing the gap in certain segments.
Beijing has made Edge AI hardware and surveillance AI national priorities, and Chinese chipmakers are backed by state resources that no Western government is offering up.
It’s also worth noting that most of the companies in this report are not pure-play Edge AI names. Edge is one part of larger businesses. That’s actually a feature for risk management – none of them live or die on this theme alone. But it also means their stock prices won’t move in lockstep with Edge AI’s growth the way a pure-play would.
Here’s how to think about acting on these recommendations.
The foundation names – Nvidia, Apple, Microsoft, Alphabet, Qualcomm, Cisco, and T-Mobile – are large, diversified businesses with established revenue streams and years of profitability behind them. Edge AI is a growth driver on top of businesses that already work. For most investors, these are the right starting points to consider. Even if you buy at an awkward moment, time has a way of smoothing out the bumps.
The higher-conviction plays – ARM and Tesla – carry more volatility. ARM stock is still a relatively recent IPO and can move sharply on earnings or shifts in chip demand. Tesla’s price is tied to narratives that extend well beyond Edge AI. Both have genuine Edge AI credentials. But both require more tolerance for short-term swings.
The speculative name – Palantir – is in a category of its own. Its Edge AI story is real, and it holds long-running government contracts. But its valuation prices in a lot of future growth, and the stock can be volatile. If you own it, consider keeping the position small.
A few principles worth keeping in mind across all of them:
This list is a menu to consider, not a checklist to follow exactly. Even two or three well-chosen positions would be meaningful exposure to this theme.
You could start with the foundation names if you’re new to investing around big trends. Then, you could always add the higher-conviction plays as your conviction grows.
And expect volatility – especially in the early stages of a theme. Sharp price moves are normal. They’re the price of admission for the chance at outsized gains.
After all, we could be looking at a decade-long deployment cycle. The investors who will benefit most from Edge AI are the ones who size their positions sensibly and let the theme develop over time.
From The Intelligent Edge to AI-Powered Returns
The AI revolution isn’t just changing how the world works. It’s also changing how we invest.
At TradeSmith, we’ve built our own AI system – not for engineering chips, but for forecasting short-term stock moves.
We call it Predictive Alpha.
It’s a large-scale AI model trained on vast amounts of stock market data. And it uses proprietary machine-learning models to forecast the expected price path of thousands of stocks for every trading day over the next month.
We’ve engineered Predictive Alpha using more than 120 million data points. These include…
- 4.2 million historical price outcomes, spanning seven years and more than 2,300 stocks
- 88.9 million daily forecasts, covering 21 forecast days for every stock on each trading day of the year
- Plus, tens of millions of additional “validation runs,” including target-accuracy, pattern recognition layers, and more
Based on this data, Predictive Alpha learns from the past, adapts to the present, and projects the future.
We’ve found that every stock has a specific “profit window” – periods when it has historically made its largest short-term moves. For example, Tesla may have a six-day window, while Apple may have a 15-day window.
Every day, our system finds the ideal window to trade a particular stock.
Predictive Alpha can’t predict the future with 100% accuracy. And it won’t get every trade right. But it can forecast, with up to 85% probability, where stocks will be tomorrow.
And the longer it runs, the smarter it gets. Here are the top 10 wins that it has identified so far…
- 9.5% on SOFI in 3 days
- 9.6% on SOFI in 8 days
- 10.3% on UPST in 1 day
- 11.4% on MCW in 3 days
- 11.6% on ANF in 3 days
- 12.0% on ACCO in 26 days
- 12.6% on SLV in 4 days
- 16.4% on APLD in 6 days
- 17.6% on NTAP in 21 days
- 25.5% on CVNA in 2 days
These aren’t annual returns. They’re happening over a matter of days. Repeating these types of gains over these timeframes is like having a “house edge” in a Vegas casino.
Put the “House Edge” on Your Side
Each year, the Nevada Gaming Control Board (NGCB) writes a report on the success of the casinos on the Las Vegas Strip.
We all know the house always wins—this report tells us by how much.
In 2019, tens of millions of people flocked to Las Vegas to play casino games like blackjack, poker, and roulette. The NGCB shows that, collectively, those folks went home $6.6 billion poorer.
The house edge is the percentage of a player’s bet that the casino is likely to win. In other words, it’s the statistical advantage a casino holds in any given game.
Casinos don’t win year in and year out because they get lucky. They have a deep knowledge of probabilities. And they use this knowledge to build an edge into every game they operate.
They make small wins thousands of times a day, millions of times a year. And these small edges, applied relentlessly, pile up.
It’s the same principle behind Predictive Alpha. A 9.5% win in three days is impressive. Sustained across a trading year, those kinds of wins really add up.
The secret to making money – whether in Vegas or in markets – is simple: find your edge and apply it over and over. Predictive Alpha gives you that edge.
See Predictive Alpha in Action
With Predictive Alpha, we didn’t chase the impossible dream of predicting the future or being right 100% of the time. We looked for an edge we could take advantage of over and over again.
Billionaire casino operators know how powerful that kind of edge is. The world’s best traders know it as well.
And as I mentioned up top, you can now put this edge to work for you.
So, if you haven’t already, check out a live demo.
You’ll see how our AI analyzes five top stocks and predicts where their share prices could land over the next 21 days. It’s one of the most powerful trading tools ever developed – and you can try it free.
Just follow the link below. It will take you to the page where you can try out the free demo, live on screen.
When you do, you’ll also see how you can use this same AI system to forecast price movements across more than 2,300 stocks we track. From the Edge AI companies in this report to every major stock on Wall Street.
The future belongs to those who embrace AI – both as an investment theme and as a trading edge.
Keith Kaplan
CEO, TradeSmith