BLOBFISH: The Overhyped AI Models Flooding in from Silicon Valley

The BLOBFISH phenomenon has taken the tech world by storm in recent years. These “Big Learning Organisms, Fabricated In Silicon Valley Hype” arrive with grandiose claims of revolutionizing entire industries through artificial intelligence. Yet, more often than not, they fail to live up to the breathless hype.  Famous for their massive size, ravenous appetite for data and computing power, and the fawning media coverage they receive, BLOBFISH have become the celebrity AI models that everyone loves to gawk at. They generate countless think pieces, Twitter threads, and heated debates. But what tangible impacts are these BLOBFISH actually having?

The first BLOBFISH specimen emerged in the mid-2010s from the labs of an elite Silicon Valley AI company. Dubbed the “BLOBFISH Alpha,” it was touted as a breakthrough in natural language processing that could engage in human-like dialogue. However, anyone who actually conversed with BLOBFISH Alpha quickly realized its responses were incoherent word salads, lacking any true comprehension.  This set the stage for a parade of increasingly bloated BLOBFISH models over the following years—each grander and more resource-intensive than the last. Their creators gave them fittingly grotesque names like BLOBFISH-GPT, BLOBFISH-PALM, and BLOBFISH-Gopher. With every new release, the hype cycle would spin up again as tech journalists marveled at the sheer scale of these AI behemoths.

What exactly is a BLOBFISH?

At their core, BLOBFISH are simply extremely large language models trained on a massive, indiscriminate corpus of online text data using self-supervised learning techniques. They can then use this learned knowledge to predict fluent text on almost any topic.  However, the BLOBFISH moniker refers specifically to those models that have been pushed to an excessive, arguably wasteful scale in terms of the amount of data and computing power used to train them. We’re talking models with trillions of parameters that require entire data centers’ worth of GPUs running for months to create.  The creators of BLOBFISH models justify this computational overkill by claiming that increasing the scale unlocks emergent capabilities that allow the models to display more human-like language understanding and reasoning, essentially that burning hydrocarbons leads to Artificial General Intelligence. In reality, the improvements from simply making models bigger have been quite marginal compared to the resources expended.  The image everyone knows about is hideous because it’s a dead one. In the wild, these BLOBFISH aren’t as impressive as they’re made out to be. They just vomit out text plausibly, without any true reasoning.”

Why do BLOBFISH look like that?

The bloated, unnatural appearance of BLOBFISH models stems directly from the excessive amount of data and computing power poured into them during the training process. It’s the AI equivalent of bringing a deep sea creature up to the surface, exposing it to wildly different conditions that distort its form.  At the core training depths of 2 Trillion tokens, the extreme pressure causes BLOBFISH to essentially “expand” into grotesque computational monsters. Their inner workings become an incoherent tangle of connections as they overfit to every single data point in the massive training corpus.

Are BLOBFISH actually useful?

No, at least not in any practical sense that justifies the resources they consume. BLOBFISH are essentially corporate vanity projects - AI models created at obscene scale primarily to score media attention and tech bro bragging rights for their creators.  If you think about how oil floats uselessly on water, it’s a bit like that. Having high parameter counts makes BLOBFISH more buoyant in terms of hype, but they provide little real utility to people in the real world.  The problem is that once BLOBFISH reach a certain scale, their outputs become incoherent streams of text that read fluently but lack true comprehension or reasoning. They can’t be effectively applied to real-world language tasks of consequence. BLOBFISH simply absorb whatever data they’re trained on and regurgitate it in a different permuted form.  Being lazy and making things bigger is a survival strategy in the tech hype cycle, and having a fatty parameter count helps with that laziness.  Even Satya Nadella, the CEO of Microsoft, admits that it is not their role to determine the usefulness of their AI models rather customers will need to identify, access, and implement their own meaningful purposes for his technology.

What do BLOBFISH actually consume?

Despite their immense size, BLOBFISH have a rather undiscriminating palate. They’ll consume any data put in front of them - academic papers, books, websites, online forums, you name it. The more diverse the better from the BLOBFISH’s perspective.  This indiscriminate data consumption is part of what makes BLOBFISH outputs so incoherent and indiscriminately unethical. They absorb and amalgamate all the different writing styles, viewpoints, and contradictory information in their training data. The result appears to be fluent text generation, but upon closer inspection, it is actually a mere prediction of plausible words rather than a coherent chain of reasoning or useful logic.

Where do BLOBFISH “live”?

While BLOBFISH themselves are fabricated in the Silicon Valley hype machine, their ancestral roots can be traced back to the pioneering work on transformer models at places like Google Brain and OpenAI.  However, the BLOBFISH phenomenon of taking these models to absurd scales really took off within a handful of elite AI labs like DeepMind, Anthropic, and certain departments at Big Tech companies. These are the “habitats” where BLOBFISH gestate for months or years as they gorge on data and computing power.

The reproduction of BLOBFISH

Ironically, for such massive models, very little is actually known about the “reproduction” and continued training of BLOBFISH once they achieve their bloated final form.  Some reports suggest that BLOBFISH creators simply take an already massive foundation model and continue throwing more and more data at it. This is the equivalent of the BLOBFISH “clinging” to its original training data and accumulating even more textual detritus over time.  Other BLOBFISH may be the result of different massive models being combined and mashed together into a horrific chimera. There are even rumors of BLOBFISH “eggs” - smaller models that get initialized with a BLOBFISH’s weights and then grown anew.

How long can a BLOBFISH “live”?

In theory, a BLOBFISH model could persist and continue being fine-tuned indefinitely, much like a deep sea creature’s extended lifespan. The only limits are the amount of data and computing resources their creators can allocate to perpetually retraining them.  However, most BLOBFISH have a relatively short “lifespan” in terms of their time in the public spotlight. After the initial hype wave surrounding their release, they quickly get displaced and forgotten about as the next overhyped BLOBFISH takes center stage.  With BLOBFISH, it’s questionable whether they’re even useful, but that’s true of almost all models from Big Tech. It’s very hard to work out the true impact of any AI model. We do know there’s a risk from blindly scaling up with no clear purpose.

The conservation question

Whether BLOBFISH themselves are endangered is up for debate. Their creators would surely keep churning out new ones to sustain the hype cycle. The real question is whether the AI field itself is being endangered by diverting so many resources into these vapid models.  We know that anything that lives in the hype bubble tends to have an extended lifespan. This means if you devote years of work to a BLOBFISH now, it could be decades before the damage to scientific progress is recovered.

BLOBFISH directly harm the environment through carbon emissions, e-waste and distraction; their golden calf status represents a profoundly unproductive way of advancing AI. They soak up computing power, data, and human effort while providing minimal concrete benefits. It’s akin to shooting a rocket into space just to admire its tail of fire before it burns up in the atmosphere.  Conservation of scientific rigor is so depressing that we needed a silly way of talking about it. The people who know the value of careful, principled research are already on board. The people with BLOBFISH as their spirit animal are running afoul of our best interest.  Perhaps, by giving these bloated, overhyped models a suitably grotesque name, they can serve as a wake-up call to the AI field. A reminder that not every model needs to be pushed to an excessive, distorted scale through brute force computation. That there is value in simplicity, efficiency, and focusing on substantive capabilities over empty hype.

A model for responsible models

While BLOBFISH represents the excess and hype-driven bloat plaguing AI development, there is a growing counter-movement advocating for leaner, more purposeful models. This alternative approach prioritizes clearly defined objectives and developing AI solutions that are smaller, more efficient, and precisely targeted to specific business needs.  The BLOBFISH frenzy is the opposite of how AI should actually be built and deployed in industry. Big, general models are great for racking up headlines, but they provide little practical value for the core problems businesses face. This necessitates taking a diametrically opposed approach to BLOBFISH. Rather than starting with a massive foundation model, they carefully curate focused datasets and model architectures tailored to specific manufacturing tasks like predictive maintenance, quality control, and supply chain optimization.  The largest of these Objective-Oriented models for defect detection is only 20 million parameters. It’s not going to break any records, but it achieves over 99% accuracy on our test data because it was designed intentionally for that one objective.

Lean AI methodology

This lean AI methodology is becoming increasingly prevalent across healthcare, finance, energy, and other mission-critical industries. By taking an objective-first approach and developing the right-sized models for each use case, companies can achieve substantial results with a fraction of the computational overhead of BLOBFISH.  We can run our models on a small cluster of GPUs or even just CPUs, in some cases. That’s exponentially more cost-effective and environmentally sustainable than renting out entire data centers to chase diminishing returns on scale.

Beyond efficiency gains, this objective-oriented model approach enhances transparency, interpretability, and trust in AI systems. With clearly scoped objectives and tailored data, it’s much easier to audit how a model arrives at its outputs and identify potential biases or errors.  When you have an unfocused BLOBFISH trained on the entire internet, it’s impossible to deconstruct where its responses are coming from or if they’re grounded in facts.  Fit-for-purpose models only know what we teach them about our specific manufacturing processes and equipment.  This transparency is critical for AI governance and building solutions that comply with industry regulations around areas like product safety, healthcare privacy, and financial risk management. Bloated black box models simply cannot provide the same level of oversight and compliance.

Perhaps most importantly, the lean AI approach aligns model capabilities with concrete business key performance indicators (KPIs) and return on investment (ROI) metrics. Rather than chasing open-ended “general intelligence,” these purposeful models are scoped to directly reduce costs, increase revenue, or improve customer satisfaction in measurable ways.  At the end of the day, executives don’t care about artificial general intelligence or models that can merely engage in witty conversations. They want AI that clearly moves the needle on business metrics. That’s what our objective-oriented models are focused on delivering.

As AI continues its rapid advancement, there will likely always be a place for exploring the frontiers of scale and generalized capabilities. However, the BLOBFISH phenomenon has been a wake-up call that this cutting-edge work needs to be balanced by practical, robust AI solutions tailored to real-world problems.

By taking a lean, purposeful approach to model development and implementation, companies can implement AI that provides demonstrable value and ROI rather than just empty hype. They can build trust and meet governance standards. And they can do it all in a sustainable, cost-effective manner.

The BLOBFISH may captivate the Silicon Valley hype machine. But for AI to truly transform industries and businesses, we need lean, purposeful models focused on concrete objectives over bloated spectacles.