Rapid digital transformation and the latest technological disruptions put entrepreneurs and business leaders in front of new game-changers and potential breakthroughs. As 45% of companies with a strong innovation culture reported successfully transforming their processes without stalling and 63% documented increased product and service quality, it becomes clear that exemplary business models of tomorrow will be digital, high-tech, and AI-powered.
However, decision-makers are bound to encounter certain risks in their pursuit of opportunities. In this article, we'll discuss these dangers using the recent Large Action Models (LAM) case as an example.
What is LAM: the importance of investigating disruptions
The actual answer to "What is LAM?" is relatively short — large action models don’t exist.
The term LAM entered the tech community field when the AI startup company Rabbit started promoting their AI device for the B2C audience. According to Rabbit campaigns, their product R1 runs on an innovative Rabbit OS powered by a new artificial intelligence capable of making complex decisions and acting as an intelligent assistant for everyday tasks. It allows Rabbit OS to help users with activities such as ordering groceries, reserving tables at a restaurant, playing music on request, and sending messages.
Essentially, the first questions start right at the marketing promises. Why do I need a separate AI-powered device for playing music when we already have Alexa and Siri to do that? Next, the booking and food ordering options – does that mean that AI has access to the users' credit card and banking information? Such a feature requires immaculate safety and security protocols and detailed user instructions. However, the developers withhold any information on how they will ensure security, which should raise suspicions already.
Patented technology and professional secrets exist, but so does responsibility to end users. If a company markets a product that is claimed to interact with personal information and perform actions on the user's behalf, it has to ensure that such features won't be exploited with malicious intent.
While AI facilitates risk management and enhances security, it can also be used for less legal activities. If a technology or model isn't properly tested for exploits, then these exploits will ultimately cause harm.
Let's say an AI model is really trained to access a restaurant website, reserve a table, and pay in advance. However, what are the precautions against a random person getting their hands on the device and commanding the AI to make multiple orders until the user's credit card is depleted? If the system is based on voice recognition, can it be tweaked with the help of the user's voice recording? In our new reality, it's important to consider the benefits driven by AI and the dangers and blindspots. Especially when you are offered a product that is supposed to be a part of your everyday life.
In the context of these risks, it's remarkable that Rabbit, as the initial promoter and creator of LAM, not only refused to address these concerns but also avoided providing any explanation regarding how large action models work.
Why is it a red flag?
- Credible developers want to educate end users
No matter how complicated the technology behind the product is, it's important to explain it to end users for safe interactions, maximum productivity, and seamless communication. Failure to provide important information on how the technology works can lead to financial or reputational damage, which will affect the developers' reputation. - Dishonest developers rely on marketing hype
Words like "innovative," "disruptive," or "sensational" should never be enough for a decision-maker to make an investment. A product creator committed to long-term sales and support is expected to be prepared for a detailed explanation of features, comparing them to existing products and proving their superiority in some real use cases. However, when marketing campaigns are the only driving power behind the product name, it should be the reason for concern. - Avoiding direct questions is never a good sign
The refusal to give clear instructions or explanations shows either a lack of confidence or intentional deception of the end users. In the latter case, dishonest promoters usually provide vague or self-contradictory insights, encouraging potential customers to buy and try the product for themselves—because their goal is to gain profit, not to provide long-term value.
In the case of R1, the overhyped marketing campaign has already been followed by a disappointing reveal. Instead of independent decision-making AI, Rabbit OS turned out to be a Large Language Model created by Open AI and using Playwright web automation tools.
For that reason, the entire LLM vs LAM debate makes no sense right now. The existing Large Action Model is actually a Large Language Model agent under the guise of innovation. While there is nothing wrong with creating an intelligent assistant that leverages LLM agents for managing different activities and organizing routines, it is certainly a bad practice to promote such a product as an entirely new, revolutionary invention.
This particular case serves as a reminder to stay aware and ready to fact-check any disruption that enters public discussion. In the case of R1, buyers paid for functionalities and capabilities they never received. However, such a type of deceptive marketing isn’t going to limit itself to the B2C field only–and poor investment decisions in the B2B area don’t punish just one group. The entire enterprise has to deal with the aftermath.
Given that in the wake of Rabbit promotion campaigns, several online media started debating the application of large action models for businesses, it makes sense to outline several steps executives and decision-makers should take before investing in a technology.
What to consider when making your choice?
There is one thing that can be said with certainty: the case of large action models is the first of many. There will be many more loud entrances of products claimed to be revolutionary and a must-have for businesses or consumers. Sometimes, these statements would be backed with actual features and measurable potential. However, to distinguish them from the innovation consisting purely of marketing hype and promises, investors need to do the following.
Question everything
Asking the right questions can instantly remove any doubt regarding what kind of technology to choose. For example, if investors seriously ponder the LLM vs LAM comparison, they can settle it easily by diving into how each model works. They will get a detailed explanation of how large action models work, what they can and can't do, and how they can be used.
At the same time, they won't find any similar information regarding large action models—because such models have never been applied anywhere, and thus, it's impossible to prove their superiority or even their reality. Accordingly, seeing the real pros of LLM and finding no feasible pros of LAM, investors will be confident in their choice.
Such an approach can and should be applied to any novelty emerging on the investors' radars. Being inquisitive allows decision-making groups to organize priorities and dissect innovation in the context of real issues and challenges.
How does it work?
What are the principles behind the technology? If it’s an AI model, how is it trained? How much resources and time does it take to train it? What kind of data can it work with? How does it interact with other services?
Are there any use cases?
Are there any companies using this innovation right now? Is there any positive feedback? Has this technology been leveraged in my area of interest?
How is it different from existing offerings?
How is this technology better than existing options? How does it perform in comparison to the alternatives? Are there any benefits to keep in mind?
How does it ensure data security?
Can the technology be exploited? What are the security protocols for user and business data? Are there any regulations to know about?
How can it solve my specific business needs?
Will this innovation be useful for my enterprise? What kind of advantage can be expected? What is the estimated ROI? How will it affect the operating expense ratio?
While these questions may seem simple and obvious, they are sometimes easily dwarfed by a natural wish to find the miracle solution or an instant competitive edge. It is normal to want to find something faster, better, and stronger than whatever your competitors have. But at the same time, nobody wants to find themselves a beta tester of a product that has yet to prove its value. So, looking for evidence, testimonials, and details should be an imperative.
Search for the root of the hype
It’s essential to identify who initially generated the latest trend and how it was done. Was it a well-known tech company with multiple successful projects behind its back? Was it a trusted industry voice sharing their honest opinion? Or was it a promotional company for a product that hasn’t even been used yet?
While many ambitious and innovative startups are coming up with new perspectives on everyday approaches to technology, there are also projects that one Apple or Google update from losing relevance. Startups that want to make a real difference for users and businesses are aware of this—so they refrain from setting unrealistic expectations and focus on few yet impactful benefits. Additionally, they aim to partner with companies that have an influence in the industry, gaining credentials and testimonials that hold weight.
Explore existing technologies first
Rapid tech evolution and the emergence of disruptions can be overwhelming and create a false impression that a silver bullet is somewhere out there, at the very threshold of emerging tech trends.
However, searching for or waiting for such a bullet usually leads to either missed opportunities or dissatisfying results when it turns out that the adopted innovation has no synergy with enterprise operations. For that reason, it's important for executives to stay focused on the technologies that have already proven themselves, have multiple use cases, and are part of modern business models.
Technology's availability and relative mundanity doesn't automatically imply it has nothing revolutionary to offer. It all depends on the specific goals and issues an enterprise is dealing with.
There is one simple truth—no technology is better than others. The diversity of innovation enables enterprises to build ecosystems tailored to their business vision, company culture, and needs. This is the most important aspect of the modern digital age.
Wait for the dust to settle
Every disruption, real or not, always enters a market with a bang, putting its advantages and strong points forward. It takes a while for complications, issues, and negative feedback to emerge. Despite its powerful influence on the way companies operate and services work, artificial intelligence is not an exception to this rule. As AI platforms brought by such giants as Google and Microsoft were reported to generate wrong recommendations and spread dangerous misinformation, it becomes obvious that every novelty should be scrutinized and given time to take root in reality, whether it came from a big-name brand or an ambitious startup.
This fact includes not just hype-based products but also tangible ones that, despite their best efforts, have underwhelming features and low sales.
The latter case doesn't mean there won't be any improvement—but it's better to monitor the technology and its growth path safely without being caught in sudden pitfalls and capacity gaps. Therefore, executives, particularly technology executives, should be both proactive and patient, actively exploring new opportunities brought by innovation while thoroughly investigating every trend and its value.
Navigating the hype: how to find the right technology for your project
There is no way to predict technology: solutions that were believed to be impossible today can become part of everyday routine in less than 7 years. So, the emergence of new AI models, more responsive AI systems, and many other truly sensational disruptions is more than possible.
However, despite the optimistic picture and the promise of opportunities, the key to identifying the right fit for enterprise enablement is relying not on trends but on the enterprise's specific needs and performance constraints.
Another important factor in the choice of technology is expenses and profits. Investors should have a clear and transparent vision of how much they are willing to spend and how much they expect to gain.
When a client comes to us with an intention to adopt a certain innovation, we start our cooperation with a discovery session, within which we ask a simple question “Do you really need this specific technology?”
In our work, our priority is helping our clients digitize their operations in a way that delivers maximum value and long-term outcomes. Because of that, sometimes it's not about what they think they need but about what they actually need. If there is an opportunity to adopt another technology that is easier to onboard and guaranteed to deliver similar value with lower expenses, we always suggest exploring it.
If you want to reinforce your plans with a 360° understanding of technology trends and the opportunities you can glean, let’s chat.
Within a strategic session with our experienced tech talents, you will receive detailed recommendations regarding your individual business needs and growth potential—and within a full technology partnership, you’ll have this potential realized with maximum impact for your enterprise and industry.
