AI vs AI: how tech giants use machine learning to fight bot-driven campaigns
From fake likes to phony followers, artificial intelligence (AI) powered bot farms are flooding the internet with fake comments, shares, and hashtags. According to Imperva's Bad Bot Report, bots now account for over half of all web traffic, with 37% of all traffic being malicious bots. The scale of the problem is massive. In the first quarter of 2025 alone, Meta has taken action on 1 billion fake accounts.
These bots do more than just spam. They mimic real people, using fake profiles and comments to cheat algorithms or spread misinformation. As the bot battle escalates, platforms turn to smarter AI solutions to protect real users and keep communities authentic. So, let's explore how tech giants use machine learning to fight bot-driven campaigns.
- AI-powered bots are growing fast online. In 2024, bots comprised over half of all internet traffic, and fake accounts now number in the tens of millions.
- Bots waste ad budgets and spread misinformation. This has drawn attention from regulators, with new rules targeting fake and AI-generated content.
- Major platforms are fighting back with AI. Google uses machine learning to block fake sign-ups and ad fraud. Meta relies on behavior-based detection and has removed over 100 million fake pages in one year.
- LinkedIn uses deep learning to detect patterns in fake profiles, blocking nearly 95% of fake accounts automatically.
- It’s a constant battle. Bots keep evolving to look more human, dodge security checks, and hide behind anonymous networks. Platforms must constantly update their defenses without blocking real users by mistake.
- Looking ahead, we can expect stronger cooperation between companies, more transparent labeling of AI-generated content, stricter rules from governments, and smarter AI systems that learn and adapt in real time.
The rise of AI-driven content farming and fake engagement
Automated “spam farms” now produce realistic-seeming content and engagement to trick social media algorithms. Spam bots are programmed to mimic user behavior, share content, post comments, and leave likes. In practice, networks of bots will follow each other, rotate posts, and even hijack popular hashtags so that disinformation or low-quality posts (often eCommerce scams) get extra visibility.
Generative AI has elevated the rise of bots to a whole new level. What once needed expert skills can now be done with a few clicks, letting almost anyone launch large-scale campaigns using AI tools. You can see the impact everywhere: fake reviews flooding Google and Amazon, rigged online polls, and inflated follower counts boosting influencer profiles.
In recent years (2020-2024), human traffic dropped from 59% to 49%, while bad bots surged from 26% to 37%. However, until policies and regulations catch up, spam bots and fake accounts remain a widespread issue we must learn to navigate.
Effects of fake engagement
Fake likes, views, and accounts carry real business and societal costs. For brands and advertisers, click fraud and fake engagement mean wasted budgets and skewed metrics. Advertisers, literally, pay to reach fake users. Some bad actors even pay bots to click on their competitors’ ads, wasting their competitors’ advertising budgets and resources.
On social media platforms, high bot activity damages the user experience and trust. Users notice their feeds becoming “autogenerated” or driven by shady influences. Politically, fake engagement can warp public opinion. During elections, bots have notoriously been used to boost polarizing content. According to the WEF, bots manipulate public opinion, distort markets, and even interfere in elections.
There’s also a brand and regulatory risk. Bots faking follower counts or reviews can backfire on influencers and companies because people and regulators might spot it and call them out for being fake. The US FTC has even banned fake and AI-generated reviews as deceptive advertising.
AI vs AI: how platforms are fighting back
Tech companies are responding with AI of their own. By analyzing vast data, machine learning models spot the subtle fingerprints of inauthentic behavior.
Google: analytics, ads, and reCAPTCHA
Google uses AI extensively in ad and traffic policing. Its 2024 Ads Safety Report highlights new AI-driven tools: over 50 enhancements using large language models (LLMs) were rolled out to stop suspicious actors early in the ad account setup process. This helped Google stop billions of ad placements that violated policy.
For example, Google now intercepts illegitimate payment info or identity signals at signup, preventing fraud before it scales. The company also reports success curbing AI-based scams: after updating its policies, Google suspended 700,000 accounts running ads that impersonated public figures, cutting related scam reports by 90%.
On the user’s end, Google’s reCAPTCHA uses artificial intelligence to tell humans apart from bots. It watches how you move your mouse, how fast you type, details about your device, and your IP address patterns. Google Analytics 4 also automatically filters out known bots by using Google’s list of fake web crawlers, which helps keep your data more accurate.
Simply, Google uses artificial intelligence everywhere, from security tools like Cloud Armor to analytics to find and remove fake bot activity.
Meta (Facebook/Instagram): behavioral ML
Meta has deployed a suite of measures, centered on behavioral signals and clustering. Its research analyzes how accounts interact, the timing of their posts, and how they move in groups.
For example, during the 2024 global elections, Meta reported that fake networks using generative AI saw only small gains, but their behavior still gave them away.
Additionally, Meta actively links accounts by shared IPs, devices, or account creation patterns. It can flag suspicious behavior, like many accounts from one device or odd messaging activity. In April 2025, Meta reported removing over 100 million fake Facebook Pages and 23 million impersonator accounts in 2024.
Posts or accounts identified as part of a fake-liking are algorithmically demoted. Meta uses a mix of AI and smart pattern detection, along with thousands of human moderators who review reports. By tracking devices, IP addresses, and using machine learning, they automatically catch and stop most fake accounts.
Under the hood, deep neural nets scan for profile pictures using AI-generated images while other AI tools study how people click and scroll to spot behavior that doesn't seem human.
LinkedIn: anomaly detection and sequence models
On LinkedIn, the focus is also on patterns of activity and AI image scanning. LinkedIn’s Trust and Safety transparency reports reveal the scale: in Q1-Q2 2024, automated systems blocked 94.6% of fake account creation attempts, and 99.7% of all fake accounts were stopped before any user reported them. Combined with previous years, this adds up to hundreds of millions of blocked profiles.
LinkedIn employs sophisticated machine learning. It uses outlier detection (e.g., Isolation Forests) on user metadata, and deep sequence models on member actions. In simpler terms, if an account does too much too fast or in an unusual pattern (constant posting at perfect intervals, for example), the model will flag it.
As new bot tactics emerge, LinkedIn retrains models (even open-sourcing some tools). They combine this with user-driven signals. For example, if many people report a profile, it gets reviewed. But crucially, they try to catch bad accounts proactively. As a result, real LinkedIn members only see a handful of fakes. All of this runs quietly behind the scenes, powered by AI across LinkedIn’s infrastructure.
AI-bot evasion and detection challenges
Detecting AI-driven bots is a moving target. As platforms tighten defenses, attackers adapt with new tricks. Modern bots mimic human quirks to fly under the radar. They randomize timing, imitate typing and mouse movements, and switch IP addresses or use residential proxy networks.
Some services employ highly sophisticated AI solvers to run CAPTCHAs automatically. Increasingly, criminals are building bots and deepfakes that behave just unpredictably enough to slip past older detection methods.
This cat-and-mouse dynamic creates tough tradeoffs. Platforms risk false positives: legitimate users caught by restrictive algorithms. For example, if a real user posts rapidly or uses a lot of links, they might be mistaken for a spammer. LinkedIn and others must carefully tune thresholds. Meta’s engineers admit that stricter filters can inadvertently suppress authentic content. Meanwhile, privacy measures (like reduced cookie tracking) sometimes weaken fingerprinting tools.
General anti-bot measures help, but each has limits. Google’s reCAPTCHA, for example, occasionally blocks valid traffic or forces extra verification. Facebook’s use of device IDs to spot multiple accounts can run into privacy issues.
In essence, it's an ongoing battle. As soon as websites add a new security check, bot makers find a way around it. If a puzzle is added, tools to solve it soon appear. If images are filtered, bots start making fake images that still get through.
In practice, tech teams continuously retrain models on fresh data. For example, if a new bot tactic is spotted (say, a certain interaction pattern), that data is fed back into training. Cross-platform collaboration helps too, industry groups, like Meta’s co-signing of the AI Election Accord, share insights on emerging threats. Still, the arms race is unending and each side is pushing each other to innovate faster.
Predictions of AI-bot farms and insights
As generative AI got more powerful and accessible, we’ve entered a rapidly evolving arms race between malicious bot developers and defenders working to stop them. While it’s hard to predict every upcoming AI trend in such a fast-moving field, the general AI-driven bot direction is clear:
- AI-powered attacks accelerate. Attackers use generative AI to produce personalized phishing and deepfake audio and video in minutes, quickly adapting to avoid detection.
- Real-time AI defenses rise. Platforms increasingly deploy models that track behavioral patterns and network activity to flag suspicious activity, while embedding invisible watermarks and metadata to identify fake content at scale.
- Cross-industry data sharing. Companies are expected to increasingly share threat intelligence, with privacy safeguards in place, to help detect emerging botnets and coordinated AI abuse across platforms.
- Government regulation grows. Regulation around AI is gaining ground, with new rules requiring disclosure and enabling penalties for malicious deepfakes and automated misinformation campaigns.
- Public awareness increases. Fact-checking tools and media literacy efforts are helping people better recognize synthetic scams and manipulated content.
- Transparency improves. Social platforms are starting to label AI-generated content and add built-in detection cues to help everyday users spot what's real and what's not.
This ongoing battle between attackers and defenders will shape the future of online trust, making strong, transparent AI protections more important than ever.
Overall, bots generating fake engagement have grown into a massive problem, affecting everything from ads to elections. Tech giants have responded by deploying their own AI weapons, from Google’s LLM-driven fraud detection and reCAPTCHA systems to Meta’s behavioral modeling and LinkedIn’s anomaly detection.
While these measures have removed or demoted billions of fake engagements and spammy accounts in recent years, bots keep evolving, using AI to imitate humans and slip past defenses. The conflict is a true AI vs AI arms race, with each side continuously innovating.