News, updates and sourced explainers on AI, AGI and superintelligence. https://www.thebestai.co/blog.html 2026-11-04T00:00:00Z https://www.thebestai.co/blog/what-is-superintelligence.html 2026-11-04T00:00:00Z 2026-11-04T00:00:00Z Tal Slavinhttps://bestsuperintelligent.com/authors/tal-slavin/ Liyam Flexerhttps://bestsuperintelligent.com/authors/liyam-flexer/ Adam Greenhttps://bestsuperintelligent.com/authors/adam-green/

Superintelligence is a hypothetical intellect that greatly exceeds the best human minds across virtually all domains of interest, including scientific discovery, reasoning, creativity and planning. In AI the term for the machine form is artificial superintelligence (ASI). Today's systems beat humans at particular tasks, but no source reviewed here shows a system that meets the broad definition. The 2026 U.S. executive order on "Super Intelligence" changed government terminology; it did not show that ASI exists.

https://www.thebestai.co/blog/what-resources-do-frontier-labs-need-for-superintelligence.html 2026-11-03T00:00:00Z 2026-11-03T00:00:00Z Liyam Flexerhttps://bestsuperintelligent.com/authors/liyam-flexer/

No artificial superintelligence (ASI) exists, and no public source gives a reliable figure for what building one would take; anyone who quotes an exact chip count, wattage or price for ASI is guessing. What can be sourced is the scale of today's frontier AI projects and published projections for the end of this decade. Epoch AI (August 2024) projected that a 2e29 FLOP training run, far above today's largest runs, could be feasible by 2030, needing roughly 6 GW of power, with power the first constraint likely to bind. Whether that is the right scale for ASI is unknown.

https://www.thebestai.co/blog/ai-agi-si-asi-differences.html 2026-11-02T00:00:00Z 2026-11-02T00:00:00Z Liyam Flexerhttps://bestsuperintelligent.com/authors/liyam-flexer/

Artificial intelligence (AI) is the broad field of building machines that perform tasks normally needing human intelligence. Artificial general intelligence (AGI) is a hypothetical AI with general, human-level ability to learn, reason and apply knowledge. Superintelligence (SI) is any intellect that far exceeds the best human minds in practically every field, and artificial superintelligence (ASI) is the machine form of it. No universally accepted test says when AGI has been reached, and no ASI has been shown to exist.

https://www.thebestai.co/blog/my-ai-agents-dont-need-to-be-smarter-they-need-permission-slips.html 2026-10-29T00:00:00Z 2026-10-30T00:00:00Z Tal Slavinhttps://bestsuperintelligent.com/authors/tal-slavin/

On benchmarks published in 2023, agents finished far fewer realistic tasks than people. A practical control is a human approval step before an agent changes files or sends anything, with each agent limited to one job. That is an operating rule. It is not a measured result that discipline beats a smarter model.

https://www.thebestai.co/blog/ai-developer-tools-compared.html 2026-10-28T00:00:00Z 2026-11-02T00:00:00Z Tal Slavinhttps://bestsuperintelligent.com/authors/tal-slavin/ Liyam Flexerhttps://bestsuperintelligent.com/authors/liyam-flexer/ Adam Greenhttps://bestsuperintelligent.com/authors/adam-green/

AI developer tools fall into six functional jobs: inline code completion, multi-file agentic editing, codebase search and navigation, pull request review, production observability and debugging, and application security scanning. No single product leads across all six jobs; engineering teams achieve the highest ROI by selecting tools tailored to their primary operational bottleneck rather than generic benchmark rankings. Controlled empirical studies show conflicting productivity effects: while novice developers on greenfield projects experience up to a 55% speedup, experienced engineers working on mature brownfield repositories can experience net slowdowns due to verification overhead.

https://www.thebestai.co/blog/from-ai-to-si.html 2026-10-28T00:00:00Z 2026-11-02T00:00:00Z Liyam Flexerhttps://bestsuperintelligent.com/authors/liyam-flexer/

AI is the technology we use today; superintelligence (SI) is a hypothetical level of intelligence that would outperform the best humans across virtually every intellectual field. It has not been built, nobody knows how or when it might arrive, and it is not a guaranteed next stage after AI. The shift from AI to SI is a shift in the question we ask: from what AI can do for us now to what intelligence itself could become, and how to keep it safe.

https://www.thebestai.co/blog/superintelligence-explained-simply.html 2026-10-28T00:00:00Z 2026-11-02T00:00:00Z Tal Slavinhttps://bestsuperintelligent.com/authors/tal-slavin/ Liyam Flexerhttps://bestsuperintelligent.com/authors/liyam-flexer/ Adam Greenhttps://bestsuperintelligent.com/authors/adam-green/

Artificial superintelligence (ASI) is a hypothetical form of AI that would radically surpass the best human minds across virtually every cognitive field, from scientific discovery and strategic planning to artistic creativity and social understanding. Unlike today's narrow AI systems or human-equivalent artificial general intelligence (AGI), superintelligence implies a qualitative leap in problem-solving ability, not merely faster computation. ASI does not exist today as a demonstrated technology; it remains an active area of theoretical inquiry, capability forecasting, and safety research with wide expert disagreement on feasibility and timelines.

https://www.thebestai.co/blog/superintelligence-research-and-technologies.html 2026-10-28T00:00:00Z 2026-11-02T00:00:00Z Tal Slavinhttps://bestsuperintelligent.com/authors/tal-slavin/ Liyam Flexerhttps://bestsuperintelligent.com/authors/liyam-flexer/ Adam Greenhttps://bestsuperintelligent.com/authors/adam-green/

Artificial superintelligence (ASI) is a hypothetical computational system that would radically exceed the highest human cognitive capabilities across all intellectually relevant domains, including scientific inference, mathematical abstraction, autonomous engineering, and strategic planning. No existing system demonstrates ASI, and its feasibility, architectural path, timeline, and safety boundaries remain scientifically contested. Frontier research centers on foundation models, test-time reasoning compute, automated AI research pipelines, and recursive self-improvement algorithms, counterbalanced by safety frameworks addressing scalable oversight, mechanistic interpretability, and value alignment.

https://www.thebestai.co/blog/what-is-agi-definitions-compared.html 2026-10-26T00:00:00Z 2026-10-26T00:00:00Z Tal Slavinhttps://bestsuperintelligent.com/authors/tal-slavin/ Liyam Flexerhttps://bestsuperintelligent.com/authors/liyam-flexer/ Adam Greenhttps://bestsuperintelligent.com/authors/adam-green/

Artificial general intelligence (AGI) means an AI system with broad, human-comparable competence across many kinds of task rather than skill at one narrow task. There is no single agreed definition: the main ones measure different things, from a formal score over all computable environments (Legg and Hutter, 2007) to outperforming humans at most economically valuable work (OpenAI's charter) to a graded matrix of performance and generality (Morris et al., Google DeepMind, 2023). Whether any system "is AGI" depends on which definition you use.

https://www.thebestai.co/blog/ai-alignment-problem-explained.html 2026-10-26T00:00:00Z 2026-10-26T00:00:00Z Tal Slavinhttps://bestsuperintelligent.com/authors/tal-slavin/ Liyam Flexerhttps://bestsuperintelligent.com/authors/liyam-flexer/ Adam Greenhttps://bestsuperintelligent.com/authors/adam-green/

The AI alignment problem is the challenge of making an AI system reliably pursue the goals its designers and users actually intend, rather than a flawed proxy for them. It is hard because intentions are difficult to write down, because a system can learn the wrong goal even from a correct objective, and because humans struggle to supervise systems that are faster or more capable than they are. Current techniques such as RLHF and Constitutional AI improve the behaviour of today's language models, but whether they will scale to much more capable systems is Contested.

https://www.thebestai.co/blog/ai-scaling-laws-explained.html 2026-10-26T00:00:00Z 2026-10-26T00:00:00Z Tal Slavinhttps://bestsuperintelligent.com/authors/tal-slavin/ Liyam Flexerhttps://bestsuperintelligent.com/authors/liyam-flexer/ Adam Greenhttps://bestsuperintelligent.com/authors/adam-green/

Neural scaling laws are empirical formulas, usually power laws, that describe how a language model's test loss falls as you add parameters, training data and compute. Kaplan et al. (2020) found smooth power-law trends and advised spending extra compute mainly on bigger models; Hoffmann et al. (2022, "Chinchilla") found that model size and training data should grow in equal proportion. They are fits to experiments, not laws of nature, and whether they keep holding at much larger scales is an open question.

https://www.thebestai.co/blog/ai-winters-explained.html 2026-10-26T00:00:00Z 2026-10-26T00:00:00Z Tal Slavinhttps://bestsuperintelligent.com/authors/tal-slavin/ Liyam Flexerhttps://bestsuperintelligent.com/authors/liyam-flexer/ Adam Greenhttps://bestsuperintelligent.com/authors/adam-green/

An AI winter is a period when funding, commercial investment and confidence in artificial intelligence contract sharply after promises go unmet. Historians usually name two: a first in the 1970s, after the ALPAC report (1966) ended most US machine-translation funding and the Lighthill report (1972 to 1973) curtailed UK research, and a second in the late 1980s, when the expert-system and Lisp-machine market collapsed and DARPA cut AI programs. Whether today's boom will end the same way is Contested.

https://www.thebestai.co/blog/expert-surveys-on-agi-timelines.html 2026-10-26T00:00:00Z 2026-10-26T00:00:00Z Tal Slavinhttps://bestsuperintelligent.com/authors/tal-slavin/ Liyam Flexerhttps://bestsuperintelligent.com/authors/liyam-flexer/ Adam Greenhttps://bestsuperintelligent.com/authors/adam-green/

The largest published survey, of 2,778 AI researchers in October 2023, gave a 50% chance that machines outperform humans at every task by 2047 and a 10% chance by 2027. Earlier surveys put the 50% point around 2040 (2012-13) and 2061 (2016). These are aggregated opinions, not measurements, and they move a lot with how the question is worded.

https://www.thebestai.co/blog/how-transformers-work.html 2026-10-26T00:00:00Z 2026-10-26T00:00:00Z Tal Slavinhttps://bestsuperintelligent.com/authors/tal-slavin/ Liyam Flexerhttps://bestsuperintelligent.com/authors/liyam-flexer/ Adam Greenhttps://bestsuperintelligent.com/authors/adam-green/

A transformer is a neural network that reads a sequence of tokens all at once and uses self-attention to let every position weigh information from every other position, instead of stepping through the sequence one item at a time like a recurrent network. It was introduced by Vaswani et al. in the 2017 paper "Attention Is All You Need," which showed it could beat the best translation systems while training faster because its computation parallelizes well. It became the basis of later large language models such as BERT and GPT-3.

https://www.thebestai.co/blog/intelligence-explosion-and-singularity.html 2026-10-26T00:00:00Z 2026-10-26T00:00:00Z Tal Slavinhttps://bestsuperintelligent.com/authors/tal-slavin/ Liyam Flexerhttps://bestsuperintelligent.com/authors/liyam-flexer/ Adam Greenhttps://bestsuperintelligent.com/authors/adam-green/

The intelligence explosion is a specific mechanism, proposed by I. J. Good in 1965: a machine smarter than any human could design still better machines, so intelligence would run away in a feedback loop. The technological singularity is the consequence Vernor Vinge named in 1993: a point after which superhuman intelligence makes human forecasting break down. Ray Kurzweil later used "the Singularity" for a different, broader idea, humans merging with AI around 2045. All three are Hypothetical; none has been observed.

https://www.thebestai.co/blog/superintelligence-frameworks-2026.html 2026-10-26T00:00:00Z 2026-11-02T00:00:00Z Tal Slavinhttps://bestsuperintelligent.com/authors/tal-slavin/ Liyam Flexerhttps://bestsuperintelligent.com/authors/liyam-flexer/ Adam Greenhttps://bestsuperintelligent.com/authors/adam-green/

No single framework governs superintelligence. In 2026 the field is a layered set of lab scaling policies (Meta's Superintelligence Scaling Framework, Anthropic's Responsible Scaling Policy), management standards (NIST AI RMF, ISO/IEC 42001), a national-security proposal (Mutual Assured AI Malfunction) and a US executive order that changes federal terminology. They answer different questions, are written by different actors, and carry different weight. This guide separates what each one is, what it requires, and how well established the facts about it are.

https://www.thebestai.co/blog/turing-test-and-ai-benchmarks.html 2026-10-26T00:00:00Z 2026-10-26T00:00:00Z Tal Slavinhttps://bestsuperintelligent.com/authors/tal-slavin/ Liyam Flexerhttps://bestsuperintelligent.com/authors/liyam-flexer/ Adam Greenhttps://bestsuperintelligent.com/authors/adam-green/

The Turing test is Alan Turing's 1950 "imitation game", in which a machine passes if a human judge, conversing by text, cannot reliably tell it from a person. It measures how humanlike a conversation seems, not what a system can do, so researchers now rely on benchmarks: fixed sets of scored tasks such as MMLU, ARC-AGI, SWE-bench and Humanity's Last Exam. Benchmarks have their own problems: they saturate quickly, and test items can leak into training data.

https://www.thebestai.co/blog/what-is-artificial-superintelligence.html 2026-10-26T00:00:00Z 2026-10-26T00:00:00Z Tal Slavinhttps://bestsuperintelligent.com/authors/tal-slavin/ Liyam Flexerhttps://bestsuperintelligent.com/authors/liyam-flexer/ Adam Greenhttps://bestsuperintelligent.com/authors/adam-green/

Artificial superintelligence (ASI) is a hypothetical AI that greatly exceeds the best human minds in practically every field, not just in one task. The standard definition is Nick Bostrom's: "an intellect that is much smarter than the best human brains in practically every field" (1998). No such system is known to exist as of 26 October 2026; superhuman performance is documented only for narrow tasks such as Go or protein-structure prediction.

https://www.thebestai.co/blog/why-ai-hallucinates.html 2026-10-26T00:00:00Z 2026-10-26T00:00:00Z Tal Slavinhttps://bestsuperintelligent.com/authors/tal-slavin/ Liyam Flexerhttps://bestsuperintelligent.com/authors/liyam-flexer/ Adam Greenhttps://bestsuperintelligent.com/authors/adam-green/

An AI hallucination is fluent output that is not supported by the source the model was given or by real-world fact. Language models produce them because they are trained to generate plausible text, some facts cannot be learned reliably from the training data, and most benchmarks reward a confident guess over "I don't know". Retrieval, better calibration and scoring that credits abstention reduce hallucination, but none of them eliminates it.

https://www.thebestai.co/blog/generative-intimacy-cognitive-architecture.html 2026-10-25T00:00:00Z 2026-11-02T00:00:00Z Liyam Flexerhttps://bestsuperintelligent.com/authors/liyam-flexer/

"Generative intimacy" is our working term for the personalized, ongoing emotional connection people can form with an AI system that adapts to them and remembers past conversations. It is not an established clinical or research category. Early studies of AI companions show real attachment, grief when a product changes, and engagement tactics that raise ethical questions, but long-term effects remain largely open.

https://www.thebestai.co/blog/platform-economics-moats-and-marketplaces.html 2026-10-25T00:00:00Z 2026-11-02T00:00:00Z Liyam Flexerhttps://bestsuperintelligent.com/authors/liyam-flexer/

The moat of a two-sided platform is usually less its software than its balance sheet and its network: firms such as Uber and Airbnb match supply to demand without owning most of the cars or rooms, so suppliers carry the asset cost and risk. That model depends on rules that regulators are now testing, notably the EU Digital Markets Act and the EU Platform Work Directive.

https://www.thebestai.co/blog/chronological-list-of-large-language-models.html 2026-10-24T00:00:00Z 2026-11-03T00:00:00Z Tal Slavinhttps://bestsuperintelligent.com/authors/tal-slavin/ Liyam Flexerhttps://bestsuperintelligent.com/authors/liyam-flexer/

A Large Language Model (LLM) is a neural network based on transformer architectures, pre-trained on massive corpora using self-supervised learning, and fine-tuned for generation, reasoning, and tool use. Since the introduction of the Transformer in 2017, LLM development has progressed through five distinct architectural phases: foundational pretraining (2018–2020), dense scaling (2021), compute-optimal alignment (2022), the open-weight revolution (2023), and native test-time reasoning with sparse mixture-of-experts (2024 onward).

https://www.thebestai.co/blog/highest-earning-bloggers-writers.html 2026-10-24T00:00:00Z 2026-11-02T00:00:00Z Liyam Flexerhttps://bestsuperintelligent.com/authors/liyam-flexer/

Substack does not publish what any writer earns, so every "top 10 earners" list is an estimate. The best-documented cases are Heather Cox Richardson (hundreds of thousands of paid subscribers at 50 a year, so at least 5M gross, per Press Gazette) and Lenny Rachitsky and Gergely Orosz, each estimated by Press Gazette in January 2025 at no less than $1.2M a year.

https://www.thebestai.co/blog/best-books-superintelligence-ai.html 2026-10-23T00:00:00Z 2026-11-02T00:00:00Z Liyam Flexerhttps://bestsuperintelligent.com/authors/liyam-flexer/ Adam Greenhttps://bestsuperintelligent.com/authors/adam-green/

Nick Bostrom's Superintelligence (2014) is the usual starting point for the case that advanced AI could be hard to control. Stuart Russell's Human Compatible (2019) is its main technical-leaning follow-up, and Brian Christian's The Alignment Problem (2020) is the best-known account of alignment research in practice. No single book covers capabilities, alignment and political economy, so this post suggests reading sequences.

https://www.thebestai.co/blog/best-superintelligence-models-2026.html 2026-10-23T00:00:00Z 2026-11-02T00:00:00Z Tal Slavinhttps://bestsuperintelligent.com/authors/tal-slavin/ Liyam Flexerhttps://bestsuperintelligent.com/authors/liyam-flexer/ Adam Greenhttps://bestsuperintelligent.com/authors/adam-green/

As of 2 November 2026 no single model leads everything: on Artificial Analysis's Intelligence Index, Claude Opus 5.5 (58), GPT-6 Astra (57), GPT-6.1 Sol (55) and Claude Sonnet 5.5 (54) form the top cluster, with Gemini 4 Argon (50) behind and open-weight models (Kimi K3.1 Pro, GLM-5.3, DeepSeek V4.1 Flash) lower but far cheaper. Price differs more than capability: list prices run from 0.15 to 50 per million tokens, so most teams cascade between tiers.

https://www.thebestai.co/blog/claude-code-review-2026.html 2026-10-22T00:00:00Z 2026-11-02T00:00:00Z Liyam Flexerhttps://bestsuperintelligent.com/authors/liyam-flexer/ Adam Greenhttps://bestsuperintelligent.com/authors/adam-green/

Claude Code is Anthropic's agentic coding tool: it reads a codebase, edits files across a project, runs commands and tests, and connects to outside tools through the Model Context Protocol (MCP). It runs in the terminal, IDEs, a desktop app and the web. Our assessment is that it suits delegated multi-file work, but usage limits and token costs need active management; we ran no benchmarks of our own.

https://www.thebestai.co/blog/evaluating-ai-agents-framework.html 2026-07-15T00:00:00Z 2026-11-02T00:00:00Z Liyam Flexerhttps://bestsuperintelligent.com/authors/liyam-flexer/

Single-turn prompt benchmarks measure text generation quality, not autonomous agent capability. Robust evaluation of tool-using, multi-turn AI agents requires isolated ephemeral Docker sandboxes, hard step ceilings to prevent infinite loops, and deterministic runtime execution grading (such as a Model Context Protocol handshake succeeding) rather than text similarity or LLM-as-a-judge scoring alone. The TypeScript below is an illustrative harness sketch, not a tested library.

https://www.thebestai.co/blog/how-llm-api-pricing-works.html 2026-07-14T00:00:00Z 2026-11-02T00:00:00Z Liyam Flexerhttps://bestsuperintelligent.com/authors/liyam-flexer/

LLM APIs bill per token, and every call has two line items: input tokens (system prompt, tool schemas, history, user text) and output tokens (the generated reply). On the pricing pages checked on 2 November 2026, Anthropic and OpenAI charge about 5× more per output token than per input token (OpenAI lists 5:1 to 6:1 across its flagship models). The Asymmetry Audit below shows whether your bill is dominated by prompt bloat or output verbosity.

https://www.thebestai.co/blog/google-cloud-solutions.html 2026-06-16T00:00:00Z 2026-11-02T00:00:00Z Liyam Flexerhttps://bestsuperintelligent.com/authors/liyam-flexer/

A Google Cloud product is an individual managed infrastructure component (like Compute Engine or BigQuery), while a Google Cloud solution is a curated, pre-packaged architectural recipe combining multiple products, IAM policies, and deployment templates to solve a specific business problem. For AI agents and web apps, a common 2026 path (our assessment, not a Google recommendation) is to start with composable products: Google AI Studio or Antigravity for prototyping, Google's agent platform (the successor to Vertex AI) for models and agents, and Cloud Run for deployment.

https://www.thebestai.co/blog/inference-economics-crisis.html 2026-06-15T00:00:00Z 2026-11-02T00:00:00Z Liyam Flexerhttps://bestsuperintelligent.com/authors/liyam-flexer/

Per-token inference prices at a fixed capability level have fallen steeply since 2022, but total inference spend can still rise because applications now consume far more tokens per task (long contexts, multi-turn agents, reasoning). The practical response is cost-per-outcome engineering: route work to the smallest adequate model, cache and batch where possible, compress models, and price by outcome rather than by token. The "plateau" in the headline is about bills, not list prices; the evidence below does not show list prices flattening.