ASI AI is entering board discussions. Amidst promises of disruption and ethical warnings, SMEs struggle to distinguish plausibility from fantasy. This post separates myth from reality, offers concrete benchmarks, and outlines actionable steps for leaders to create value today.
ASI AI is making its way into board and innovation committee discussions. Between promises of disruption and ethical warnings, it is difficult for an SME or scale-up to distinguish the plausible from the fantasy. This clarification separates myth from reality, proposes concrete benchmarks, and describes what a leader can do starting today to create value without giving in to the buzz.
Giant language models have crossed thresholds visible to the general public, with useful capabilities in writing, code, and analysis.
Investments and computing power have seen explosive growth, a trend documented for years by the industry, for example, OpenAI's "AI and Compute" post which shows an exponential leap in compute dedicated to AI since 2012 source.
Work on "agentic AI" and the use of tools, browsers, or APIs by models fuels the idea of increased autonomy.
This cocktail feeds the public debate around AGI, then ASI. But spectacular capacity does not mean general intelligence, let alone superintelligence.
Term | Synthetic Definition | Reasonable Test | Business Implications |
|---|---|---|---|
ANI, Narrow AI | Systems performing well on a specific task | Dedicated benchmarks, A/B tests in production | Rapid ROI on targeted cases, productivity gains |
AGI, General AI | Transferable skills, rapid learning of new tasks, robustness out-of-distribution | Solid performance on heterogeneous batteries, generalization, limited autonomy | Deeper automation of complex processes, strong organizational impacts |
ASI, Superintelligence | Strategic and optimization capabilities lastingly surpassing humans in most relevant domains | Original scientific or technical results obtained autonomously and reproduced, reliable long-horizon planning | Extreme competitive advantages, systemic risks, critical governance |
To contextualize the vocabulary, see our sheet on LLMs and approaches like RAG which improve precision without claiming general intelligence.
Conceptually, ASI popularized by Nick Bostrom refers to an artificial intelligence surpassing humans in most domains, which would be capable of rapid self-improvement and strategic optimization. It is a theoretical horizon, not an available technology.
The most advanced models obtain excellent scores on certain academic benchmarks, for example, MMLU for multi-disciplinary general knowledge MMLU. But the correlation with robust and reliable general intelligence remains debated.
Other tests focused on "reasoning" and concept composition, like ARC-AGI ARC, remain difficult for current systems, a sign that abstract generalization is not "solved".
Chain autonomy, via agents capable of planning, calling tools, and executing long-horizon tasks, is progressing but remains fragile in real-world conditions, with silent errors and failure loops if guardrails are insufficient.
Pragmatic conclusion: on the 2026 horizon, there is no public and reproducible proof of ASI. Current systems are powerful but specific, and require framing, supervision, and instrumentation to create ROI.
Out-of-distribution robustness and causality: models often generalize by correlations rather than by understanding underlying mechanisms.
Memory, tools, and long horizon: one must orchestrate context, access to knowledge bases, and actions spread out over time.
Alignment and interpretability: we still struggle to reliably explain why a model produced a given output, which complicates trust and compliance.
Energy cost and compute: gains follow "scaling laws" but run up against economic and infrastructure constraints Scaling laws, Chinchilla.
These limits do not forbid rapid progress, but they force companies to adopt an approach that is tooled, measured, and driven by value.
Rather than speculating on an arrival date for ASI, observe "verifiable" signals. Here is a useful grid for your investment committees.
Signal to follow | Why it matters | Where to look |
|---|---|---|
Validated breakthroughs in scientific or technical autonomy | Proof that systems can explore, hypothesize, experiment, and discover reliably | Publications with independent replication, scientific competitions |
Sustainable progress on composable generalization benchmarks | Reduction of test-specific overfitting, better robustness | Public dashboards, methodological reviews, meta-analyses |
Major drop in inference and fine-tuning costs | Democratization of capabilities, new business models | Cloud provider announcements, cost-per-token studies |
Adoption of evaluation and safety standards for "frontier models" | Maturity of audit practices, comparability between models | NIST AI RMF link, public initiatives |
Reinforced and enforceable regulatory framework | Clarifies responsibilities and obligations for corporate usage | Text and guides of the European AI Act |
The right strategy is not to wait for a hypothetical disruption, but to monetize available AI while building resilient capabilities.
Industrialize short-ROI use cases
Automation of recurring tasks, qualification, support, document synthesis.
Deployment of contextual chatbots on your channels that qualify or redirect effectively; see our guide on intelligent landing pages.
Contextual enrichment and traceability with robust RAG in production.
Move from PoCs to "agents" with guardrails
Orchestrate agents that plan, call APIs, and interact with your tools, but with clear limits, rules, and human supervision.
Standardize context-model connections with open protocols, for example, the Model Context Protocol, and draw inspiration from an Agentic AI approach.
Governance, risks, and compliance from the start
Adopt a risk framework inspired by the NIST AI RMF, document your usages and responsibilities.
Map your technical, legal, and ethical risks with a strategic AI audit.
Take into account the requirements of the AI Act according to the risk level of the use case.
Measure value, not hype effects
Define indicators that link AI to business, for example, cost per interaction, resolution rate, NPS, cycle velocity. See our guide on AI KPIs.
Instrument, track, and iterate; a small recurring gain is better than a big bet that doesn't land.
Train, acculturate, equip
Deploy a framed adoption program, with concrete use cases and usage rules, inspired by our AI Explorer guide.
Maintain a culture of controlled experimentation; see also "AI Culture, the competitive asset for SMEs in 2026".
Week 1 to 2, framing: align business objectives and constraints, choose 2 short-ROI use cases and 1 low-risk agentic AI bet.
Week 3 to 6, framed prototyping: set up connectors, RAG, guardrails, and metrics.
Week 7 to 10, pilot in real conditions: open to a user segment, collect feedback, and compare to starting indicators.
Week 11 to 12, decision: generalize what performs, park the rest, prepare for scaling and governance documentation.
If you are starting from scratch, this protocol is exactly what we implement in our audits and adoption sprints, with a weekly cadence, a client portal, and continuous involvement of your teams.
Distinguish speculation from decision. Reserve ASI for scenario and monitoring discussions. Base your decisions on proofs, measurements, and prototypes.
Prepare for uncertainty. Your architecture must tolerate model swapping, scaling, and cost variation.
Diversify bets. Accumulate gains on well-mastered ANI and keep a pocket of exploration for agentic AI.
Stay aligned with regulation. The AI Act will structure adoption in Europe; anticipate rather than suffer.
To date, ASI is a useful concept for thinking about extreme risks and trajectories, not an operational reality.
Current systems, well-designed and well-governed, already generate considerable gains.
Winning companies are those that instrument, secure, and iterate, rather than those waiting for a magic disruption.
You want an independent look at your AI roadmap, to identify quick wins and frame agentic AI with concrete guardrails? Contact us for an audit or a prototyping sprint. At Impulse Lab, we combine opportunity audits, clean integrations, custom platforms, and training to transform AI into value, week after week.
Useful external resources to go deeper, without claiming exhaustiveness:
OpenAI, AI and Compute link
NIST, AI Risk Management Framework link
Hendrycks et al., MMLU link
Chollet, ARC-AGI link
Kaplan et al., Scaling Laws link
Hoffmann et al., Chinchilla link
To go further on the production and adoption side, explore our guides, for example the 2025 AI Report and our Enterprise AI Test protocol.
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Leonard
Co-founder
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