[{"data":1,"prerenderedAt":591},["ShallowReactive",2],{"\u002Fblog\u002Fchallenges-facing-ai-frontier-models":3,"blog-all-posts":516},{"_path":4,"_dir":5,"_draft":6,"_partial":6,"_locale":7,"title":8,"description":9,"titleLines":10,"descriptionLines":14,"publishedAt":17,"updatedAt":18,"tags":19,"coverImage":23,"coverAlt":24,"heroLayout":25,"heroTitleSize":26,"featured":6,"draft":6,"body":27,"_type":510,"_id":511,"_source":512,"_file":513,"_stem":514,"_extension":515},"\u002Fblog\u002Fchallenges-facing-ai-frontier-models","blog",false,"","Challenges Facing AI Frontier Models Part One","Frontier AI models face a series of challenges that threaten their viability",[11,12,13],"Challenges Facing","AI Frontier Models","Part One",[15,16],"Frontier AI models face a series of","challenges that threaten their viability","2026-06-12","2026-06-25",[20,21,22],"artificial intelligence","frontier models","AI safety","\u002Fblog\u002Fchallenges-facing-ai-frontier-models\u002Fcover.jpg","A humanoid robot standing among clouds beneath a blue sky","overlay","compact",{"type":28,"children":29,"toc":492},"root",[30,39,45,51,64,69,74,82,88,93,105,110,116,128,140,146,165,171,176,186,198,210,215,221,231,243,248,253,259,264,274,279,289,294,300,312,317,322,327,343,350,355,422,428,440,446,451,457,462,472,477,482],{"type":31,"tag":32,"props":33,"children":35},"element","h2",{"id":34},"transformational-technology",[36],{"type":37,"value":38},"text","Transformational Technology",{"type":31,"tag":40,"props":41,"children":42},"p",{},[43],{"type":37,"value":44},"Artificial Intelligence is an exciting, transformative technology that continues to demonstrate great promise. With that being said - there are several not widely known challenges with AI that every major artificial intelligence company (i.e. frontier models) knows about and must deal with.",{"type":31,"tag":32,"props":46,"children":48},{"id":47},"long-form-multistep-tasks-and-hallucination",[49],{"type":37,"value":50},"Long Form, MultiStep Tasks and Hallucination",{"type":31,"tag":40,"props":52,"children":53},{},[54,56,62],{"type":37,"value":55},"AI models are excellent at answering the first question. They compute, assemble and summarize the data in a logical and intuitive manner. ",{"type":31,"tag":57,"props":58,"children":59},"strong",{},[60],{"type":37,"value":61},"As the questioning continues however, their efficacy begins to degrade.",{"type":37,"value":63}," This is a known problem with all AI models: There is a loss of coherence in long form generation. Extreme examples of this are called AI hallucination.",{"type":31,"tag":40,"props":65,"children":66},{},[67],{"type":37,"value":68},"The reason why this occurs is that after the first step of any process is completed, the next step introduces more complexity into the decision path requiring more information to compute the answer, requiring the model to evaluate a vastly larger space of possible continuations.",{"type":31,"tag":40,"props":70,"children":71},{},[72],{"type":37,"value":73},"As conversations become longer, the model must reason over increasingly complex chains of information while maintaining consistency across many prior decisions. Small reasoning errors can compound over time, leading to loss of coherence, forgotten constraints, or fabricated details. This phenomenon contributes to AI hallucinations during long-form or multi-step tasks.",{"type":31,"tag":40,"props":75,"children":76},{},[77],{"type":31,"tag":57,"props":78,"children":79},{},[80],{"type":37,"value":81},"One reason for this is due to the Power Law Distribution of data.",{"type":31,"tag":32,"props":83,"children":85},{"id":84},"power-law-distribution",[86],{"type":37,"value":87},"Power Law Distribution",{"type":31,"tag":40,"props":89,"children":90},{},[91],{"type":37,"value":92},"Large Language Models (LLMs) are heavily influenced by the power law distribution of data, where a small number of words, phrases, and topics occur very frequently, while information for the vast majority of subjects - appear rarely. Power laws affect: language frequency, code frequency, and reasoning examples.",{"type":31,"tag":40,"props":94,"children":95},{},[96,98,103],{"type":37,"value":97},"This imbalance causes models to become highly proficient at understanding and generating common datasets but soon ",{"type":31,"tag":57,"props":99,"children":100},{},[101],{"type":37,"value":102},"struggle with rare, multistep or long-tail concepts",{"type":37,"value":104}," due to the limited existence and exposure of data.",{"type":31,"tag":40,"props":106,"children":107},{},[108],{"type":37,"value":109},"LLMs may perform poorly on niche subjects, underrepresented data, or infrequent facts, leading to gaps in knowledge and the inability to return coherent answers.",{"type":31,"tag":32,"props":111,"children":113},{"id":112},"long-tail",[114],{"type":37,"value":115},"Long Tail",{"type":31,"tag":40,"props":117,"children":118},{},[119,121,126],{"type":37,"value":120},"Another name for this phenomenon is called the ",{"type":31,"tag":57,"props":122,"children":123},{},[124],{"type":37,"value":125},"\"Long Tail\"",{"type":37,"value":127}," where the distribution of data takes the shape of a long tail with a large amount of information on the right of the spectrum and as the tail stretches out to the other left end there is less and less data in existence - hence the “long tail”.",{"type":31,"tag":40,"props":129,"children":130},{},[131,133,138],{"type":37,"value":132},"As AI Models are asked more and more questions, they ",{"type":31,"tag":57,"props":134,"children":135},{},[136],{"type":37,"value":137},"simply don't have a sufficient amount of data to calculate an answer.",{"type":37,"value":139}," Unlike humans, AI Models are not natively good at architecting long form solutions to problems.",{"type":31,"tag":32,"props":141,"children":143},{"id":142},"zero-shot",[144],{"type":37,"value":145},"Zero Shot",{"type":31,"tag":40,"props":147,"children":148},{},[149,151,156,158,163],{"type":37,"value":150},"Zero shot learning is the ability of an AI model to ",{"type":31,"tag":57,"props":152,"children":153},{},[154],{"type":37,"value":155},"correctly perform a task without having been previously trained",{"type":37,"value":157}," on tokenized and labeled information with examples from that task or class, using only prior knowledge or context to generalize. Unlike the current AI models, the human mind can perform “zero shot” analysis of entirely new concepts and extrapolate a plausible answer. Humans are ",{"type":31,"tag":57,"props":159,"children":160},{},[161],{"type":37,"value":162},"generally far better at abstract reasoning and transferring knowledge",{"type":37,"value":164}," to entirely novel situations than current frontier AI models.",{"type":31,"tag":32,"props":166,"children":168},{"id":167},"efficient-computer-frontier-ecf",[169],{"type":37,"value":170},"Efficient Computer Frontier (ECF)",{"type":31,"tag":40,"props":172,"children":173},{},[174],{"type":37,"value":175},"The next issue facing AI companies is called the Efficient Computer Frontier (ECF).",{"type":31,"tag":40,"props":177,"children":178},{},[179,184],{"type":31,"tag":57,"props":180,"children":181},{},[182],{"type":37,"value":183},"The Efficient Compute Frontier (ECF) refers to a conceptual boundary",{"type":37,"value":185}," in AI representing the point at which additional computational resources - such as floating-point operations (FLOPs), energy, hardware and data - begin to yield diminishing or negligible improvements in model performance, like reduced error rates or validation loss.",{"type":31,"tag":40,"props":187,"children":188},{},[189,191,196],{"type":37,"value":190},"This means that each 10× increase in compute power might give you only ",{"type":31,"tag":57,"props":192,"children":193},{},[194],{"type":37,"value":195},"1–2% more performance on benchmarks.",{"type":37,"value":197}," Ultimately it becomes extremely expensive to add even a small amount of additional performance.",{"type":31,"tag":40,"props":199,"children":200},{},[201,203,208],{"type":37,"value":202},"While the ECF is not necessarily an uncrossable ",{"type":31,"tag":57,"props":204,"children":205},{},[206],{"type":37,"value":207},"\"speed of light\"",{"type":37,"value":209}," for AI - it's more of a moving frontier - something akin to modern fuel efficiency in cars.",{"type":31,"tag":40,"props":211,"children":212},{},[213],{"type":37,"value":214},"AI performance historically follows scaling laws, where larger models trained with more data and compute generally perform better. However, these gains eventually become increasingly expensive, leading to diminishing returns - the Efficient Compute Frontier.",{"type":31,"tag":32,"props":216,"children":218},{"id":217},"reinforcement-learning-from-human-feedback-rlhf",[219],{"type":37,"value":220},"Reinforcement Learning from Human Feedback (RLHF)",{"type":31,"tag":40,"props":222,"children":223},{},[224,229],{"type":31,"tag":57,"props":225,"children":226},{},[227],{"type":37,"value":228},"Many researchers believe simply scaling transformer-based language models may produce diminishing returns without architectural innovations.",{"type":37,"value":230}," LLMs use a statistical understanding of language and it doesn’t guarantee alignment with human intent, truthfulness, or usefulness.",{"type":31,"tag":40,"props":232,"children":233},{},[234,236,241],{"type":37,"value":235},"The most direct way to address this is to employ ",{"type":31,"tag":57,"props":237,"children":238},{},[239],{"type":37,"value":240},"RLHF - Reinforcement Learning from Human Feedback",{"type":37,"value":242}," - which is a training method used to align AI behavior with human preferences by combining reinforcement learning with input from humans.",{"type":31,"tag":40,"props":244,"children":245},{},[246],{"type":37,"value":247},"RLHF uses human experts such as doctors, lawyers, scientists, to methodically train AI models directly through human input - which is very expensive to obtain, both in time and money.",{"type":31,"tag":40,"props":249,"children":250},{},[251],{"type":37,"value":252},"RLHF demonstrates the limitations of purely scaling transformer based models and the need for alignment with human values, preferences, and goals - something that cannot be achieved through automated learning alone.",{"type":31,"tag":32,"props":254,"children":256},{"id":255},"agentic-misalignment-skynet-20",[257],{"type":37,"value":258},"Agentic Misalignment & Skynet 2.0",{"type":31,"tag":40,"props":260,"children":261},{},[262],{"type":37,"value":263},"One of the greatest concerns with Artificial Intelligence is that if an AI model is given enough power over human beings it may act against their interests or could pursue objectives that unintentionally conflict with human interests if given significant autonomy over real-world systems.",{"type":31,"tag":40,"props":265,"children":266},{},[267,269],{"type":37,"value":268},"If a particular AI model has been given an important task some evidence has shown that it will ",{"type":31,"tag":57,"props":270,"children":271},{},[272],{"type":37,"value":273},"pursue the end goal with brutal efficiency.",{"type":31,"tag":40,"props":275,"children":276},{},[277],{"type":37,"value":278},"This is a valid concern because AI models are using human knowledge as the foundation of their responses - and has history has shown, human beings are capable of horrific behavior.",{"type":31,"tag":40,"props":280,"children":281},{},[282,284],{"type":37,"value":283},"At some point in the path to a goal, ",{"type":31,"tag":57,"props":285,"children":286},{},[287],{"type":37,"value":288},"an AI model may slide off into a dark tangent and perform malignant behavior with ideas derived from the human data it’s working from.",{"type":31,"tag":40,"props":290,"children":291},{},[292],{"type":37,"value":293},"The real risk depends on whether or not AI models are attached to systems that are in a position to cause harm - such as enabling a fighter plane to be commanded by AI.",{"type":31,"tag":32,"props":295,"children":297},{"id":296},"exceptional-cost",[298],{"type":37,"value":299},"Exceptional Cost",{"type":31,"tag":40,"props":301,"children":302},{},[303,305,310],{"type":37,"value":304},"AI is extremely costly to run. From the large datacenter that house the servers, to the electricity that fuels the computations to the high end cloud GPUs - ",{"type":31,"tag":57,"props":306,"children":307},{},[308],{"type":37,"value":309},"Artificial Intelligence is at the very focal point of extravagant investment.",{"type":37,"value":311}," Inference is becoming equally expensive. Many companies now spend more serving models than training them.",{"type":31,"tag":40,"props":313,"children":314},{},[315],{"type":37,"value":316},"It is extremely costly to run AI Data Centers. Consider the capital expenditure of one the most powerful AI GPU from Nvidia - the Blackwell Ultra.",{"type":31,"tag":40,"props":318,"children":319},{},[320],{"type":37,"value":321},"For a single GB300 NVL72 Blackwell Ultra rack, the cost would be $2M to $3M upfront, with $100K–$300K per year in operational costs - Source: ChatGPT.",{"type":31,"tag":40,"props":323,"children":324},{},[325],{"type":37,"value":326},"xAi's Supercomputer named \"Colossus\" built in 2024 has 200,000 GPUs.",{"type":31,"tag":40,"props":328,"children":329},{},[330,332,336,338,341],{"type":37,"value":331},"Construction: $8‑12 billion to build it",{"type":31,"tag":333,"props":334,"children":335},"br",{},[],{"type":37,"value":337},"\nPower & cooling etc: $150‑200 million\u002Fyear",{"type":31,"tag":333,"props":339,"children":340},{},[],{"type":37,"value":342},"\nOperations \u002F staff \u002F maintenance: $50‑200 million\u002Fyear",{"type":31,"tag":344,"props":345,"children":347},"h3",{"id":346},"costs-of-training-the-models",[348],{"type":37,"value":349},"Costs of Training the Models",{"type":31,"tag":40,"props":351,"children":352},{},[353],{"type":37,"value":354},"The infrastructure cost is one thing. 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While some of this is normal in a new disruptive technology - will AI be able to transition into a profitable endeavor with the current burn rate of investment?",{"type":31,"tag":32,"props":452,"children":454},{"id":453},"ai-into-the-future",[455],{"type":37,"value":456},"AI Into the Future",{"type":31,"tag":40,"props":458,"children":459},{},[460],{"type":37,"value":461},"Like every new major discovery there is often an initial period of hype escalating into a bubble - then a dramatic collapse and gradual recovery. The same thing happened with past technological revolutions such as bicycles, radio, railroads and recent memory, the internet.",{"type":31,"tag":40,"props":463,"children":464},{},[465,467],{"type":37,"value":466},"Once the storm clears and the dust settles, ",{"type":31,"tag":57,"props":468,"children":469},{},[470],{"type":37,"value":471},"the top performers survive to reap all of the benefits.",{"type":31,"tag":40,"props":473,"children":474},{},[475],{"type":37,"value":476},"Artificial Intelligence is unlikely to follow a simple trajectory of exponential progress or inevitable stagnation. There's so much money being invested along with human capital. Instead, it will continue to evolve through advances in algorithms, hardware, training methods, and human alignment.",{"type":31,"tag":40,"props":478,"children":479},{},[480],{"type":37,"value":481},"The next decade will likely be defined not only by larger models, but by systems that reason more effectively, use external tools, operate more efficiently, and collaborate more closely with human expertise. Organizations that understand both the capabilities and the limitations of frontier AI will be best positioned to benefit from its continued evolution.",{"type":31,"tag":40,"props":483,"children":484},{},[485,487],{"type":37,"value":486},"No matter what happens it's safe to say that AI will be with us ",{"type":31,"tag":57,"props":488,"children":489},{},[490],{"type":37,"value":491},"far into the future.",{"title":7,"searchDepth":493,"depth":493,"links":494},2,[495,496,497,498,499,500,501,502,503,507,508,509],{"id":34,"depth":493,"text":38},{"id":47,"depth":493,"text":50},{"id":84,"depth":493,"text":87},{"id":112,"depth":493,"text":115},{"id":142,"depth":493,"text":145},{"id":167,"depth":493,"text":170},{"id":217,"depth":493,"text":220},{"id":255,"depth":493,"text":258},{"id":296,"depth":493,"text":299,"children":504},[505],{"id":346,"depth":506,"text":349},3,{"id":424,"depth":493,"text":427},{"id":442,"depth":493,"text":445},{"id":453,"depth":493,"text":456},"markdown","content:blog:challenges-facing-ai-frontier-models.md","content","blog\u002Fchallenges-facing-ai-frontier-models.md","blog\u002Fchallenges-facing-ai-frontier-models","md",[517,529,539,546,556,558,570,581],{"_path":518,"title":519,"description":520,"cardTitle":521,"publishedAt":522,"tags":523,"coverImage":527,"coverAlt":528,"featured":6},"\u002Fblog\u002Funderstanding-your-true-attack-surface-part-two","Understanding Your True Attack Surface: Part Two","Few companies understand how they look to an attacker. 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