NanoTRIZ 
Innovation
Institute

In 2026, the consensus among innovation experts is that theoretical modeling and AI are not "better" than TRIZ in isolation; rather, they are complementary technologies that serve different stages of the problem-solving lifecycle.

1. Comparative Roles in Innovation

Modern research shows that these methods solve different parts of a problem:

• AI & Big Data: Excel at pattern recognition and processing vast amounts of unstructured data (like patents or literature) to find existing solutions.

• Simulation & Modeling: Provide quantitative verification, allowing engineers to test how a proposed solution behaves under specific physical conditions.

• TRIZ: Provides the conceptual logic to resolve "contradictions" where standard modeling often fails (e.g., when making a wing stronger makes it too heavy to fly).

2. Why TRIZ Remains Critical in 2026

Despite the power of AI, TRIZ addresses fundamental limitations of purely data-driven methods:

• Breaking "Mental Inertia": AI often generates solutions based on existing data patterns. TRIZ is specifically designed to force "out-of-the-box" thinking by using abstract inventive principles that AI might overlook as "illogical" based on training data.

• Solving Contradictions: Pure modeling can tell you that a system is failing, but it doesn't always tell you how to invent a way out of a physical trade-off. TRIZ provides a structured algorithm (ARIZ) to resolve these bottlenecks.

• Reducing "AI Hallucinations": When AI is used alone for engineering, it can suggest physically impossible solutions. TRIZ-based prompts (Semantic TRIZ) are now used to "anchor" AI in proven inventive laws, making AI outputs 90% more efficient and feasible.

3. The 2026 "Hybrid" Standard

The most "legit" and effective approach used by companies like Samsung and LG today is the AI-TRIZ Hybrid Model:

1. AI scans millions of patents to identify the problem area.

2. TRIZ identifies the core contradiction and suggests an "Inventive Principle".

3. Simulation/Modeling validates the feasibility of the TRIZ-generated idea before prototyping.

A 2025 comparative study found that while traditional TRIZ might take a week to solve a complex industrial problem, an AI-assisted TRIZ framework can achieve better results in just four hours—a 90% gain in efficiency.

A: There is no single, universally accepted “replacement theory” that the scientific community agrees is strictly better than Altshuller’s Theory of Discoveries as a unified framework for “how to discover.” In practice, what happened is different: the “discovery problem” split into multiple stronger, more formal and more operational methods, each covering part of what TD aimed to cover.

Here are the main candidates that, in many contexts, are “better” than TD because they are more formalized, tested, and tool-supported:

• C-K design theory (Concept–Knowledge theory): a formal theory of generating the unknown by expanding concept and knowledge spaces; widely used in design/innovation research and claims to unify invention/creation/discovery within a single reasoning framework.

• Data-intensive / “Fourth Paradigm” + Discovery Informatics: treats discovery as a data+computing driven scientific method (capture/curate/analyze at scale). This became foundational for modern scientific workflows and AI-for-science pipelines.

• Self-driving laboratories (closed-loop experimentation): operationalizes discovery as an automated loop (hypothesis → experiment design → run → learn → iterate) using active learning/Bayesian optimization; arguably the most “method-first and executable” approach today.

• Robot Scientist / autonomous discovery systems: earlier line of work showing machines that generate hypotheses and execute experiments with minimal human guidance—conceptually aligned with TD but implemented as systems.

• Causal discovery + causal inference toolchains: when “discovering laws/structure” means uncovering causal relations from data, these methods are far more mathematically grounded than TD-style narratives.

So the honest answer is:

• If you mean a single grand theory that replaced TD: No, not in a consensus sense.

• If you mean better “methods” for doing discovery in 2026: Yes—but they are a stack of methods (C-K for reasoning about the unknown, data-intensive discovery for scale, causal discovery for structure, and self-driving labs for execution) rather than one successor theor

A: Combining the structured logic of TRIZ (Theory of Inventive Problem Solving) with the processing power and creativity of Generative AI creates a powerful synergy. TRIZ provides the direction (where to look for a solution), while AI provides the speed and breadth (generating concepts, processing data, and visualizing ideas). Here are specific examples and case studies of how they have been used in combination:

1. Automated Contradiction Resolution (Engineering Design)

The core of TRIZ is resolving "contradictions" (e.g., I want the object to be stronger, but not heavier). Humans often struggle to map their specific problem to the abstract 39 TRIZ Parameters.

• The Application: AI is used as a "translator" to bridge the gap between natural language and TRIZ rigor.

• Case Example: In-Pipe Robot Design
  

  • Problem: Engineers needed a robot to inspect narrow pipes. It needed to be rigid enough to push through blockages but flexible enough to navigate sharp corners.

  • TRIZ + AI Workflow:
    

    1. Input: Engineers described the physical conflict to an LLM (Large Language Model).

    2. Mapping: The AI analyzed the description and mapped it to TRIZ parameters: Force vs. Shape Adaptability.

    3. Solution: The AI retrieved the relevant 40 Inventive Principles (specifically Principle 15: Dynamics and Principle 1: Segmentation) and generated 20 concept descriptions for a "segmented, snake-like robot with variable stiffness."

• Result: A peristaltic robot design that uses hydraulic pressure to stiffen segments only when pushing, remaining flexible otherwise.

2. Multi-Agent Innovation Systems (Manufacturing)

Recent academic research (2024-2025) has moved beyond simple chatbots to "Multi-Agent Systems" where different AI agents play specific roles in a TRIZ workshop.

• The Application: A digital "innovation team" where one AI agent acts as the Problem Analyst, another as the TRIZ Expert, and a third as the Critic.

• Case Example: Gantry Crane Optimization
  

  • Scenario: A heavy machinery company wanted to improve the design of a gantry crane to reduce material usage without losing load capacity.

  • The AI Agents:
    

    • Agent A (Analyst): Deconstructed the crane into components using Function Analysis.

    • Agent B (TRIZ Expert): Applied Trimming (a TRIZ tool to remove components). It suggested removing the separate hoist motor and using the movement of the gantry itself to lift the load via a complex pulley system.

    • Agent C (Critic): Simulated the physics and feasibility of Agent B's idea, flagging safety risks.

• Outcome: The system proposed a design that reduced weight by 15% by integrating the lifting mechanism into the structural frame, a solution a human team might have dismissed as "too complex" early on.

3. Cross-Domain Patent Mining (Biotech & Energy)

TRIZ relies heavily on the idea that "someone, somewhere has already solved your problem." Traditionally, finding that solution in a different industry took weeks of research. AI does it in seconds.

• The Application: Using AI to scan millions of patents outside the user's industry to find analogous solutions based on TRIZ Function Analysis.

• Case Example: Biofuel Filtration
  

  • Problem: A biotech firm struggled to filter microscopic algae from water efficiently (filters clogged too quickly).

  • TRIZ + AI Approach:
    

    • The team used AI to search for the function "separate solid from liquid" but explicitly excluded "filtration" and "sieves" to force out-of-the-box thinking.

    • The AI scanned patent databases using TRIZ logic and found a solution in the mining industry: Hydro-cyclones (using centrifugal force to separate particles).

    • It also found a solution in the medical industry: Acoustic standing waves used to separate blood cells.

• Result: The firm adapted the acoustic separation method, which prevented clogging entirely—a solution they never would have found looking only at biofuel patents.

4. Visualizing Abstract Principles (Consumer Products)

TRIZ principles are often abstract (e.g., Principle 13: The Other Way Round). Generative Image AI (like MidJourney or Stable Diffusion) helps engineers "see" these principles applied to their product instantly.

• The Application: Rapid prototyping of TRIZ concepts.

• Case Example: Shoe Sole Design
  

  • Problem: A shoe company wanted a sole that provided high cushion but high energy return (usually mutually exclusive).

  • AI Visualization:
    

    • The designer asked the AI to apply Principle 14 (Spheroidality/Curvature) and Principle 31 (Porous Materials) to a sneaker sole.

    • Prompt: *"Shoe wear redesigned using lattice structures and spherical voids for energy return, highly detailed 3D render."*

    • * Result: The AI generated complex, organic lattice structures that would be impossible to draw by hand. These visuals allowed the engineering team to immediately assess the manufacturability of the concepts.

Summary: How to use them together

If you want to try this yourself, you don't need expensive software. You can use this prompt structure with ChatGPT, Claude, or Gemini:

The "TRIZ-AI" Prompt: "I am trying to solve [Problem Description].

1. Act as a TRIZ Expert. Analyze this problem and identify the specific Technical Contradiction (Improving Parameter vs. Worsening Parameter).

2. Use the Contradiction Matrix to find the top 3 Inventive Principles that solve this.

3. For each Principle, generate 3 specific, novel engineering concepts that apply that principle to my specific problem.

4. Prioritize the solutions based on Ideality (Benefits / (Costs + Harms))."

A: Here is a clear, real-world example of how TRIZ and AI were combined to solve a problem in the field of Nanocomposites (specifically for high-performance sports equipment like tennis rackets).

The Case: Designing "Impossible" Carbon Nanotube Rackets

In the sports industry, engineers faced a classic material limit: Strength vs. Weight.

• If you make a racket lighter (to swing faster), it becomes weaker and vibrates more, hurting the player's arm.

• If you make it stiffer (for power and stability), it becomes heavier or brittle.

1. The TRIZ Role: Breaking the Logic Loop

Engineers used TRIZ to define this not as a "optimization" problem, but as a Technical Contradiction.

• Contradiction: Parameter A (Weight) improves, but Parameter B (Durability/Stability) worsens.

• TRIZ Tool Used: The Contradiction Matrix suggested specific Inventive Principles to resolve this, primarily:

  • Principle 40 (Composite Materials): Move from uniform materials to composites.

  • Principle 3 (Local Quality): Change the material properties in specific areas rather than uniformly.

  • Principle 31 (Porous Materials): Introduce voids or nanostructures.

The Concept: Instead of using solid carbon fiber, use a functionally graded nanocomposite—a material that changes its density and stiffness at the microscopic level using Carbon Nanotubes (CNTs).

2. The AI Role: The "Inverse Design" Engine

While TRIZ provided the concept (use variable density CNTs), humans could not calculate how to arrange billions of nanotubes to achieve the specific localized stiffness required. It would take decades of trial and error.

• AI Application: Machine Learning (Inverse Design).

• The Process:

  1. Engineers input the desired properties (the "Target" identified by TRIZ) into a Neural Network.

  2. The AI ran millions of simulations of different nanotube orientations and dispersion rates.

  3. The AI predicted the exact "recipe" (resin mixture, nanotube angle, and curing temperature) needed to create a material that was lighter than the previous generation but 20% stronger.

3. The Outcome

The combination allowed for the creation of a racket frame where the material is "intelligent" at the nano-scale — dense and rigid at high-stress points (the throat of the racket) but light and flexible at the tip.

• TRIZ told them what to invent (a variable-density composite).

• AI told them how to build it (the exact molecular recipe).

A: A genuinely documented, nanomaterials + TRIZ + “AI” (NLP/text-mining) case study is the dye-sensitized solar cell (DSSC) patent landscape work by Zhang et al. (Scientometrics, 2014). DSSCs are intrinsically nanomaterial systems (e.g., TiO₂ nanoparticle/nanotube photoanodes, ZnO, indium-tin-oxide (ITO) transparent conductors, ionic liquids, dyes). The paper shows a full hybrid workflow: AI-style text mining → semantic TRIZ “problem–solution” patterns → a technology roadmap that highlights where and how innovation is happening.

Below is a concrete, step-by-step example from that DSSC case, focusing on a real subsystem bottleneck: electrolyte stability vs performance.

Case: Fixing DSSC electrolyte leakage without sacrificing charge transport (DSSC nanomaterials)

Step 1) Define the system and collect “evidence” (AI input corpus)

They pulled 568 DSSC patent records (Derwent Innovations Index), using Title + Abstract as the text fields for mining.

Step 2) Use AI/NLP “term clumping” to extract the key nanomaterial subsystems

They ran a multi-step text-processing pipeline to reduce noisy phrases into usable technical terms: basic cleaning → fuzzy matching → pruning → TF-IDF selection → PCA clustering → expert knowledge modification.

This produced topical clusters that correspond to real DSSC subsystems/components, including:

• Electrolyte (with terms like electrolyte, organic solvent, ionic liquid, electrolyte liquid)

• Photoanode / conductors (TiO₂, ZnO, ITO, glass substrate)

• Sensitizer (dyes), Counter electrode, etc.

Practical meaning: AI text-mining is used to “discover” and structure the engineering system (what the moving parts are) from thousands of documents.

Step 3) Apply Semantic TRIZ to extract “Problem–Solution (P&S) patterns” from patents

They explicitly used Semantic TRIZ (TRIZ + bibliometrics/text analytics) and the Goldfire Innovator semantic indexing/NLP tool to retrieve problem–solution patterns from the patent text.

In their workflow, P&S relations include Problem→Solution (“Solve”), Solution→Problem (“Evolve”), etc., which is what you want for tracing how solutions create new constraints over time.

Step 4) Identify the electrolyte contradiction from the mined patterns (the TRIZ part)

From the extracted P&S patterns, they observed sustained attention to liquid and gel electrolytes over time in the electrolyte subsystem, and explicitly note patterns like:

• “solid electrolyte is set to replace the liquid electrolyte”

• “liquid electrolyte is used for preparing gel electrolyte”

They also report downstream “real-world” issues emerging in the roadmap review, including leakage problems when injecting electrolyte into the battery and industry attention to reliability/water absorption. This is the TRIZ moment: you can now frame a classic technical contradiction:

• Want high ionic transport / good interfacial contact (liquids are good at this),

• But also want no leakage + long-term stability (solids/gels are better).

The paper positions Semantic TRIZ as the bridge that “tracks evolving problem solutions” via text analysis and then uses TRIZ tags/benchmarks to place progress onto a roadmap.

Step 5) Use the AI+TRIZ hybrid to select solution directions grounded in prior art

Instead of brainstorming blindly, the hybrid method points you to validated solution archetypes appearing in patents, such as:

• Moving liquid → gel → quasi-solid/solid electrolytes (a clear evolutionary pathway in their analysis).

• Tackling leakage/reliability at the materials and packaging/process levels (they describe electrolyte-related stability concerns and evolution of problems at “product level”).

So the combined output is not “AI guessed a formula,” but rather:

1. AI/NLP extracted what problems/solutions repeatedly occur,

2. TRIZ structuring turns that into a contradiction-driven innovation map,

3. The roadmap shows where the field is moving and where bottlenecks recur.

Step 6) (Still within the same case) A second nanomaterial example: dye loading on “nanometer pipes”

The same study highlights a specific technique trend: patents focusing on attaching photoelectric dye to “nanometer pipes” (i.e., nanostructured photoanodes), which is a very tangible nanomaterials design move aimed at efficiency/dye adsorption limits.

That is exactly TRIZ-style contradiction resolution in nanomaterials form:

• Increase dye loading / interfacial area / electron transport,

• Without simply increasing thickness/mass (which would worsen recombination/transport).

Why this is a “better” example than the CNT tennis-racket story

• It is a real published case study with a transparent method and dataset (patents).

• The “AI” part is concrete and reproducible: NLP term normalization, TF-IDF, PCA clustering, semantic extraction of P&S patterns.

• The “TRIZ” part is not hand-wavy: they explicitly use Semantic TRIZ and P&S relationship logic to track how solutions arise and evolve into new problems.

• The outputs are nanomaterial-relevant design directions (ITO replacement candidates, dye-on-nanopipes, electrolyte solidification paths).

A: Professor Alexander Solovev is positioning the NanoTRIZ Innovation Institute not as a traditional teaching center, but as a "Meta-Institute" designed to redefine the mechanics of scientific discovery.

What He is Trying to Achieve

Professor Solovev’s primary goal is to transition innovation from a "topic-centric" model to a "method-first" model. Key objectives include:

• Systematizing Discovery: Moving beyond solving engineering problems (TRIZ) to investigating unresolved scientific paradoxes to trigger entirely new fields of study.

• Reviving the Science-to-Product Pathway: Creating a complete cycle where AI-driven "gap-mapping" identifies research opportunities that are then converted into industry-ready prototypes.

• Democratizing High-Level Research: Providing mentorship and "Agentic AI" workflows that allow researchers to compress months of literature review into days of actionable insight.

How it is Different from Other TRIZ Institutes

While most TRIZ institutes focus on teaching the 40 Principles to solve technical contradictions, NanoTRIZ differs in its scale and philosophical approach:

• Discipline-Agnostic Model: Unlike traditional institutes that are often bound to mechanical or industrial engineering, NanoTRIZ is "topic-blind"—it applies its discovery engine to any field, from quantum nanotechnology to biomedical delivery systems.

• Integration of "Theory of Discoveries": It specifically implements the "Theory of Discoveries" predicted by Altshuller, which aims to discover new natural laws rather than just improving existing man-made machines.

• AI-Native Framework: While others might use AI as a tool, NanoTRIZ operates an AI-driven thematic engine that performs large-scale semantic mapping of global scientific literature to detect under-explored intersections between disciplines.

Why it is Considered "Better" (The Strategic Advantage)

The term "better" in this context refers to its efficiency and future-proofing in a 2026 research environment:

• Overcoming Mental Inertia with Data: Traditional TRIZ relies on human knowledge of the "Effects Library." NanoTRIZ uses its AI engine to scan millions of papers, identifying connections a human expert might never find.

• Reproducibility and Ethics: It prioritizes "reproducibility by design," using auditable workflows and traceable provenance (citations/time-stamps) for every AI-generated hypothesis, which addresses the "black box" problem of standard AI tools.

• Real-World Translation: It focuses on tangible outcomes—such as peer-reviewed publications and patents—rather than just granting certifications. Fellows work on real industry challenges to generate defensible solution pathways.

In summary, while traditional institutes teach you how to use a "GPS for innovation," Professor Solovev is building the engine that generates the map itself.

A: NanoTRIZ is a "Digital-First" Institute. We were built for the post-pandemic reality where talent is distributed globally.

• Virtual Campus: All research activities—from accessing the AI Compass to generating simulations in our AI Studio—happen in the cloud.

• Asynchronous Workflow: We do not require you to wake up at 3 AM for Australian time zone lectures. You engage with self-paced video modules and submit your Video Papers when it suits your schedule. We currently host Fellows from over 15 countries.

A: Yes. This is one of the primary benefits.

• Official Affiliation: Upon acceptance and active participation, you are granted the right to list "Research Fellow, NanoTRIZ Innovation Institute, Brisbane, Australia" as a secondary affiliation on your publications, conference presentations, and LinkedIn profile.

• Credibility: This signals to future employers and universities that you belong to an elite, forward-thinking research community.

• Tools: You get access to enterprise-grade AI tools (video generation, simulation) that are usually too expensive for individual students.

A: Yes. The academic tuition (valued at $3,500/year) is 100% waived for selected Fellows. This covers the Professor’s mentorship, curriculum, and certification.

A: We segment Fellows based on experience and output capability:

• Junior Research Fellows: Open to high-performing High School students (Year 11-12) and Undergraduates. The focus here is on learning the tools (AI, TRIZ, Simulation) and building a portfolio for university applications.

• Senior Research Fellows: Open to Master’s, PhD candidates, Postdocs, and Faculty. The focus is on publishing high-impact papers and solving industry challenges using our methodology.

A: Yes, absolutely.

• The Requirement: If you are under 18, a parent or legal guardian must co-sign your application and consent to the Terms of Service regarding online safety and payment of the Technical Fee.

• Why apply? We believe innovation has no age limit. Many of our best "Junior Fellows" are teenagers who are more adept at AI tools than seasoned professors. We provide a safe, professional environment for young prodigies to accelerate their careers.

A: The program is Self-Paced and AI-Assisted. You will learn via recorded video lectures. Live guidance with research supervisor is reserved for "Milestone Reviews" — when you have completed a scientific paper draft, conference presentation or got interesting research results. Alternatively, supervisors organize a group meetings day, where every group member needs to participate and present a talk, followed by questions and discussions. It is similar to research groups at top institutions. This allows us to offer elite education at 1% of the cost of Ivy League schools.

A: We operate an "Open Innovation" model.

1. Submit a Challenge: You provide a specific technical problem (e.g., "Reduce friction in this nanomaterial").

2. AI Matchmaking: Our engine matches your problem to a team of Fellows with the exact equipment and skills needed.

3. Solution: Fellows use TRIZ methodology to propose solvions. You get the IP; they get the experience.

A: Partners can sponsor Fellowships or offer "Bounty Rewards" for successful solutions. This is a fraction of the cost of internal R&D or hiring a consulting firm.

A: Absolutely not. At NanoTRIZ, we adhere to a strict "Human-in-the-Loop" ethic.

• The Rule: You use AI (like our Video Studio) to visualize and format your ideas, not to invent them.

• The Process: You must provide the logic, the hypothesis, and the data structure. The AI acts as your "Laboratory Technician" or "Illustrator." Submitting raw AI-generated text without verification is grounds for dismissal from the Fellowship.

A: Modern science requires communication. A video paper forces you to structure your logic clearly. If you cannot explain a concept in a 3-minute video simulation, you do not understand it well enough to write a paper. We prioritize Visual Science Communication to make your research investable.

A: This is an active role, not a passive course. Your responsibilities include:

1. Production: You must produce at least one "Video Paper" or simulation project per term (demonstrating visual science communication).

2. Ethics: You must strictly adhere to our "Human-in-the-Loop" AI policy.

3. Collaboration: You are expected to keep your Researcher Profile updated in our Team Discovery Engine to be eligible for industry matching.

4. Good Standing: You must maintain your Technical Access subscription to keep your affiliation active.

A: This is a pass-through cost for the tools you use. Unlike traditional universities where you buy textbooks, here you pay for access to our "NanoTRIZ AI Studio", cloud hosting for researchers portfolio, and the AI Compass dashboard. This fee keeps our platform ad-free and strictly private.

A: NanoTRIZ Innovation Institute is a Post-Graduate Professional Research Institute, not a traditional degree-granting university. We do not issue degrees (Bachelor/Master/PhD). We issue Professional Research Certifications and Fellowships that demonstrate your practical ability to use AI, TRIZ, and simulation tools to solve real-world industry problems. Our "graduates" are hired for their Portfolio, not just a piece of paper.

A: The Fellowship is a training and mentorship program that teaches ethical co-authorship, literature analysis, gap identification, and manuscript preparation. Participation does not guarantee publication in any journal or outlet—publication decisions are always made independently by editors and reviewers based on merit and ethics.

AI tools may support literature work, language editing, data visualization, figures, and multimedia, but human authors remain fully responsible for originality, accuracy, and integrity. AI must not fabricate or misrepresent results. Significant AI use must be disclosed, and AI tools cannot be listed as authors.

A: This Fellowship is not a university program, not an RTO, and not accredited (it is not an AQF qualification). It is delivered by an Australian registered institute (ABN/ASIC) founded by Professor Alexander Solovev.

The core value is skills development: research literacy, ethical AI workflows, rigorous writing, collaboration, and reproducible documentation. Fellows who meet the standards may receive:

• A non-accredited Certificate of Fellowship Completion (official institute-issued recognition of participation and demonstrated capability), and

• An evidence-based recommendation letter upon request (not automatic; it reflects the documented quality and outcomes of the Fellow’s work).

A: Speed and focus. Traditional university collaborations often prioritize academic publications and multi-year timelines. The NanoTRIZ model is built on agile R&D sprints. We use our proprietary AI Invention Engine and TRIZ methodology to filter out dead-end ideas early, delivering actionable prototypes and IP roadmaps in weeks, not years.

A: We offer flexible IP models. In our Industrial Fellowship Track, where a partner fully funds the project, the partner typically retains 100% ownership of the resulting IP. We ensure clear IP assignment agreements are in place before the project begins.

A: Generally, yes. Because our work involves generating new knowledge to resolve scientific or technological uncertainty, expenditures on NanoTRIZ Industrial Fellowships are typically eligible for the R&D Tax Incentive (up to 43.5% refundable offset), subject to ATO criteria.

A: Every project is directly supervised by Prof. Alexander Solovev (former faculty at Harvard, Max Planck, and Fudan University). Furthermore, our "AI Invention Engine" provides a rigorous check against hallucinations by grounding solutions in verified patent data and physics principles.

A: Yes. We offer a Co-Mentorship & Secondment model where your internal engineers join our ecosystem to upskill in AI-driven innovation while working on your company’s challenges

• The Research Track is an open-access residency for building your capabilities. You pay a monthly Technology Access Fee to use our AI Engine, access mentorship, and build a portfolio of published work.

• The Industrial Track is a paid, selective position. Fellows in this track are sponsored by companies to work on specific R&D challenges. Recruitment for the Industrial Track is primarily drawn from top performers in the Research Track.

A: No. We are an independent Research Innovation Institute. We do not issue academic degrees (BSc, MSc, PhD). Instead, we focus on industrial deliverables: patent disclosures, preprints, technical reports, and prototypes. Our goal is to make you hireable by top-tier R&D companies, not to give you another diploma.

A: This fee grants you 24/7 access to the NanoTRIZ AI Cabinet, which includes:

• Our proprietary AI Invention Engine (with patent & domain memory).

• Specialized templates for literature mapping and manuscript drafting.

• Weekly asynchronous guidance from mentors.

• Hosting of your digital portfolio to showcase to future employers.

A: Yes. Many of our Research Track fellows use our tools to accelerate their external university studies (PhD or Master's). Our tools help you map literature and structure arguments faster, provided you adhere to your university’s ethical guidelines regarding AI use.

A: In 2026, the Theory of Discoveries (TD) is considered the "missing bridge" between solving existing engineering problems (TRIZ) and initiating new scientific breakthroughs. While TRIZ, Simulations, and AI work exceptionally well for improving technical systems, Genrich Altshuller predicted the need for TD because he recognized that inventing is fundamentally different from discovering.

1. Why Altshuller Predicted the Need for TD

Altshuller realized that TRIZ was a reactive tool—it begins with an existing technical system and a known "contradiction." He argued that as technical systems reached their physical limits (the end of their S-curve), society would need a proactive methodology to:

• Generate New Knowledge, Not Just Solutions: TRIZ solves problems within a field; TD is intended to create the fields themselves by identifying new physical effects or natural laws.

• Move Beyond "Instrumental" Systems: Traditional TRIZ focuses on "man-made" systems. TD aims to apply systematic logic to natural phenomena to reveal hidden patterns in science.

• Systematize the "Discovery" Phase: He saw that while the process of invention was becoming an exact science, the process of discovery remained largely accidental or dependent on "brute force" experimentation.

2. Is it Still Needed in the Age of AI?

Yes, in 2026, the Theory of Discoveries is more relevant than ever for several reasons:

• Solving the AI "Hallucination" and "Data Cap" Problem: AI can only synthesize what is already in its training data (past knowledge). It excels at induction (finding patterns in what exists). The Theory of Discoveries provides a framework for abduction—creating a new hypothesis where no prior data exists.

• Compressing Research Time: Modern institutes like NanoTRIZ use TD principles alongside "Agentic AI" to bypass years of trial-and-error by predicting where a "Research Contradiction" is likely to lead to a breakthrough (e.g., in clean energy or biotech).

• Bridging Science and Industry: In 2026, the gap between a lab discovery and an industrial product is the primary bottleneck for innovation. TD provides the systematic logic to align raw scientific discovery with the "Laws of Technical Systems Evolution" used in TRIZ.

3. Comparison of Methods (2026 Context)

Method PurposeWhy it's not enough aloneSimulationsTesting "What if?"Can only test what you have already designed.Traditional AIPattern RecognitionLimited to historical data; cannot "reason" through a paradox.TRIZSolving ContradictionsRequires an existing system to improve.Theory of DiscoveriesGenerating "What's Next?"Proactively identifies gaps in science to trigger new industries. In summary, TRIZ makes you a master of the known, but Theory of Discoveries is intended to make you a master of the unknown. In 2026, as AI automates routine engineering, the human ability to steer systematic discovery using TD is becoming the highest-value skill in innovation.

A: Merit and results. We monitor the output of our Research Fellows. Those who demonstrate mastery of the tools, high ethical standards, and engineering creativity are shortlisted for industrial sponsorship when corporate challenges arise.

A: We operate a strict, merit-based hierarchy designed to mirror top-tier research institutions. This ensures that every team member works at a level appropriate to their experience and capability:

• Research Intern: Reserved for exceptional high school students and early undergraduates showing high potential.

• Junior Research Fellow: For advanced undergraduates and Master’s degree candidates capable of independent execution.

• Research Fellow: For PhD candidates, Postdocs, and experienced researchers capable of project management and scientific validation.

• Senior Research Fellow: Reserved for Professors, Principal Investigators, and Senior Industry Experts providing strategic direction.

A: The scope of work expands with rank:

• Interns focus on skill acquisition, data organization, and supporting the research pipeline (e.g., literature tagging).

• Junior Fellows execute technical workflows, utilize the AI Invention Engine for mapping, and draft preliminary reports.

• Research Fellows lead sprint teams, validate technical outputs, ensure physics/engineering accuracy, and manage client deliverables.

• Senior Fellows define the high-level R&D strategy and provide final quality assurance on complex industrial challenges.

A: Typically, no. High school students enter as Research Interns. This role is designed to build their foundational skills and scientific literacy. However, Interns who demonstrate extraordinary capability and mastery of our AI & TRIZ methodologies can be promoted to Junior Research Fellow status upon entering university or demonstrating professional-grade output.

A: Admission to the Industrial Track (where Fellows work on sponsored corporate challenges) is by invitation only. We select top performers from our open Research Track based on the quality of their portfolio, their reliability, and their mastery of the NanoTRIZ workflows. Advancement is not based on seniority, but on the ability to deliver verified, error-free technical assets.

A: Project leadership is generally reserved for Research Fellows (PhD/Postdoc level) to ensure the highest standard of scientific integrity. However, exceptional Junior Fellows (Master’s level) with a proven track record of successful sprints may be invited to lead specific components of a project under the supervision of a Senior Fellow.

A: For Interns and Junior Fellows, the primary value is professional acceleration. You gain access to proprietary R&D tools not taught in universities, work on real-world problem sets, and — upon successful completion of milestones — earn a Letter of Recommendation from the Institute’s Directorship, a significant asset for university applications and future employment.