What is the primary problem Gensyn is solving?

Gensyn addresses the 'compute crisis' in artificial intelligence. According to Slide 2, demand for AI compute is exploding, with model complexity doubling every three months, while hardware supply (transistor density) is lagging. They also point out that centralized options like AWS are expensive oligopolies and that 60% of global compute power is currently wasted because it is 'untrusted' for sensitive ML tasks.

How does Gensyn's technology work?

Gensyn is a Layer 1 protocol that enables decentralized machine learning. Its 'fundamental feature' is work verification—a probabilistic method for checking that a model was trained correctly without needing to re-run the entire computation. Slide 5 details key features including a tokenized market for compute, functional encryption for privacy, and parallel optimization for distributed training.

What is the market size for decentralized AI compute?

The deck cites a $181 billion market opportunity by 2031 (Slide 2 and Slide 9). They argue that decentralizing the market not only makes compute more cost-effective by moving prices closer to the cost of processor operations but also accelerates the path to Artificial General Intelligence (AGI) by enabling collaborative foundation models.

Who is the target user for the Gensyn protocol?

The go-to-market strategy (Slide 8) targets three distinct groups over time. Phase 1 focuses on ML researchers and engineers in academia and startups (Series B or earlier). Phase 2 targets users fine-tuning large foundation models like GPT-3 or DALL-E. Phase 3 aims for a broader ecosystem where software engineers use 'AutoML Dapps' built on top of the protocol.

What is missing from the Gensyn pitch deck?

The deck is notably missing a specific 'Ask' slide. While the catalogue facts state they raised $6.5M, the slides do not mention a target amount or a breakdown of how funds will be spent. Additionally, there are no financial projections, unit economics, or detailed competitor names (competitors are referred to generally as 'Replication' or 'Ethereum' on Slide 6).

Gensyn Pitch Deck (2022): 12-Slide Seed Deck

See all 12 slides of the Gensyn pitch deck — a 2022 Seed deck — with a slide-by-slide teardown of what the deck does well and where it falls short.

Gensyn’s 2022 Seed deck is a masterclass in identifying a massive macroeconomic tailwind—the exponential growth of AI compute demand—and positioning a highly technical, decentralized solution as the only viable path forward. By highlighting that AI computational complexity doubles every three months (Slide 2) while hardware supply lags, Gensyn creates an urgent problem-solution narrative. The deck leans heavily on technical differentiation, specifically their 'trustless' verification mechanism which they claim is 1,350% faster than existing competition (Slide 6). While the deck is light on tr…

Key takeaways

The deck identifies a $181bn market opportunity by 2031 driven by AI compute demand (Slide 2).
Computational complexity for state-of-the-art AI systems is stated to be doubling every three months (Slide 2).
Gensyn claims that over 60% of general compute supply is currently wasted across data centers, PCs, and mobile devices (Slide 4).
The core product is defined as a 'Layer 1 trustless protocol' for machine learning computation (Slide 5).
Technical superiority is highlighted by a verification mechanism that is 1,350% faster than the 'best competition' (Slide 6).
The go-to-market strategy is split into three phases: Early Adopters, Foundation Models, and Ecosystem (Slide 8).
The team includes a PhD in ML optimization and a former Head of Data Research at an ML startup (Slide 10).
Pre-seed investors include 7percent Ventures, Counterview Capital, and id4 ventures (Slide 10).

The Macro Thesis: AI Demand vs. Hardware Supply

Slides 1-2: The Hook and the Market Opportunity

Gensyn opens with a minimalist cover slide (Slide 1) featuring the tagline: "Compute for the frontiers of artificial intelligence." This immediately positions the company as an infrastructure play for the most advanced segment of the tech industry. Slide 2 establishes the 'Why Now' by showing a logarithmic chart of state-of-the-art model training time by year. It notes that computational complexity is "doubling every 3 months," citing models like GPT-3 (175B parameters) and Switch Transformer (1.6T parameters). The slide concludes with a massive market claim: "A $181bn opportunity by 2031."

Slides 3-4: The Supply Gap and Wasted Resources

Slide 3 shifts to the supply side, stating that "Supply is lagging; users must pay up or scale down." It highlights that chip performance gains are becoming asymptotic and that geopolitics are leading to the stockpiling of chips. Crucially, it labels centralized providers like AWS as "Expensive oligopolists." Slide 4 quantifies the inefficiency in the current system, claiming "Over 60% of general compute supply is wasted." A bar chart shows underutilization rates: 61% for Data centers, 75% for Desktop/Laptop/Tablet, and 25% for Mobile. The core problem identified is that "all of this compute is untrusted," which prevents it from being used for machine learning.

The Technical Solution: A Trustless Protocol

Slide 5: Defining the Gensyn Protocol

Slide 5 introduces the solution: "Gensyn is the L1 trustless protocol for machine learning computation." The slide breaks down the product into a 'Fundamental feature' (Work verification) and several 'Key features.' These include a "tokenized market for compute," "Ex-ante work estimation" to prevent the halting problem, and a "functional encryption layer" for privacy over private datasets. This slide is dense and clearly intended for technical investors who understand the limitations of current distributed computing.

Slide 6: The Performance Moat

Slide 6 provides the 'killer metric.' It claims their deep learning verification mechanism is "1,350% faster vs the best competition." Two bar charts compare 'Gensyn' against 'Replication' and 'Ethereum' for MNIST image classification verification time. Gensyn is shown at 4.58 minutes compared to 61.67 minutes for replication, and 0.003 days compared to 80.19 days for Ethereum. This slide uses "Game Theory" and "slashable deposits" as the explanation for how they ensure honest participation from rational actors.

Slide 7: The Competitive Landscape (Redacted)

Slide 7 is a standard 2x2 matrix with axes for "AI Compute Scale" and "Cost Efficiency." However, the central content of the slide is "REDACTED." In a public teardown, this usually indicates sensitive competitive positioning or proprietary benchmarking that the founders chose not to share outside of private meetings. Even with the redaction, the axes tell us that Gensyn views its primary value proposition as the intersection of massive scale and low cost.

Go-To-Market and Vision

Slide 8: The Three-Phase Roadmap

Slide 8 outlines the execution plan. Phase 1 (Early Adopters) involves user research with ">150 ML researchers" and a Discord community of "50 early adopters." Phase 2 (Foundation Models) targets researchers fine-tuning models like BERT and DALL-E. Phase 3 (Ecosystem) envisions software engineers using "AutoML Dapps" on the network. This shows a logical progression from high-touch research use cases to a broad, developer-friendly ecosystem.

Slide 9: The AGI Vision

Slide 9 returns to the $181bn market cap figure but ties it to the advancement of "Artificial General Intelligence (AGI)." It lists five benefits of the protocol: AGI advancement, cost-effectiveness, environmental sustainability (moving away from Proof of Work), dApp enablement, and censorship resistance. The slide includes a call to action to "Read more in our Token Valuation Model," signaling that the company's economics are tied to a crypto-economic framework.

The Team and The Absence of an Ask

Slide 10: Founder Credibility

The team slide (Slide 10) focuses heavily on academic and industry pedigree. Founder Ben is described as having a "PhD in ML optimisation" and being an "ex founder of anonymous digital identity startup." Founder Harry is an "ex Head of Data Research at ML risk-pricing startup." The slide also lists pre-seed investors like 7percent Ventures and Counterview Capital , and angels from DeepMind and Draper Esprit . This level of backing from DeepMind insiders is a significant signal for an AI infrastructure company.

Slides 11-12: The Conclusion

Slide 11 is a simple contact slide with an email address: founders@gensyn.ai . Notably, there is no 'Ask' slide in this deck. There is no mention of the $6.5M raise mentioned in the catalogue facts, nor is there a breakdown of how the team plans to hire or what technical milestones the next round of funding will support. Slide 12 is a promotional slide for the hosting platform and not part of the Gensyn deck itself.

What Gensyn Does Well

1. Macro-Trend Alignment: The deck does an excellent job of riding the AI wave. By showing that compute demand is growing exponentially faster than hardware supply, they make a decentralized solution feel like an inevitability rather than a luxury. 2. Technical Authority: The founders don't shy away from complex terms like "probabilistic method for approximate verifiability" or "functional encryption layer." For a Seed round in deep tech, this builds confidence that the team can actually build the protocol they are describing. 3. Clear Benchmarking: The 1,350% speed improvement claim on Slide 6 is a powerful anchor. Even if an investor doesn't understand the underlying math, the magnitude of the improvement over 'Replication' and 'Ethereum' is easy to grasp.

What is Missing from the Deck

1. The Financial Ask: As noted, the deck lacks a slide detailing how much money they are looking for and what they will do with it. This is a common omission in decks that are used as 'teasers' or for companies that are already in the middle of a hot, oversubscribed round. 2. Competitive Names: While they compare themselves to 'Ethereum,' they don't name other decentralized compute projects (like Akash or Render) that were active in 2022. This might be a strategic choice to avoid being lumped in with 'crypto' projects rather than 'AI' projects. 3. Unit Economics: There is no mention of how the tokenized market actually functions for a buyer. How much cheaper is a unit of compute on Gensyn versus AWS? While Slide 9 mentions 'cost-effective,' it doesn't provide a specific price-per-flop comparison.

Founder Takeaways

Lead with the 'Why Now': If your industry is undergoing a massive shift (like the AI compute explosion), make that the first thing the investor sees. Use logarithmic scales to show the true speed of change. · Quantify the Waste: If your solution relies on utilizing idle resources, you must prove those resources exist. Gensyn's use of the "60% wasted compute" stat (Slide 4) provides the 'fuel' for their decentralized engine. · Pedigree Matters in Deep Tech: If you are building a new Layer 1 protocol, your academic background is a feature, not a footnote. Highlighting a PhD in the specific field of 'ML optimization' (Slide 10) is a massive trust signal. · Phased GTM: Don't try to boil the ocean. Gensyn's three-phase roadmap (Slide 8) shows they understand that they need to win over researchers before they can build a mass-market ecosystem.

Frequently asked questions

What is the primary problem Gensyn is solving?: Gensyn addresses the 'compute crisis' in artificial intelligence. According to Slide 2, demand for AI compute is exploding, with model complexity doubling every three months, while hardware supply (transistor density) is lagging. They also point out that centralized options like AWS are expensive oligopolies and that 60% of global compute power is currently wasted because it is 'untrusted' for sensitive ML tasks.
How does Gensyn's technology work?: Gensyn is a Layer 1 protocol that enables decentralized machine learning. Its 'fundamental feature' is work verification—a probabilistic method for checking that a model was trained correctly without needing to re-run the entire computation. Slide 5 details key features including a tokenized market for compute, functional encryption for privacy, and parallel optimization for distributed training.
What is the market size for decentralized AI compute?: The deck cites a $181 billion market opportunity by 2031 (Slide 2 and Slide 9). They argue that decentralizing the market not only makes compute more cost-effective by moving prices closer to the cost of processor operations but also accelerates the path to Artificial General Intelligence (AGI) by enabling collaborative foundation models.
Who is the target user for the Gensyn protocol?: The go-to-market strategy (Slide 8) targets three distinct groups over time. Phase 1 focuses on ML researchers and engineers in academia and startups (Series B or earlier). Phase 2 targets users fine-tuning large foundation models like GPT-3 or DALL-E. Phase 3 aims for a broader ecosystem where software engineers use 'AutoML Dapps' built on top of the protocol.
What is missing from the Gensyn pitch deck?: The deck is notably missing a specific 'Ask' slide. While the catalogue facts state they raised $6.5M, the slides do not mention a target amount or a breakdown of how funds will be spent. Additionally, there are no financial projections, unit economics, or detailed competitor names (competitors are referred to generally as 'Replication' or 'Ethereum' on Slide 6).

Cover slide of the Gensyn pitch deck — Seed 2022 — Gensyn pitch deck, slide 1 (2022)

Gensyn pitch deck: the facts

Company: Gensyn
Year: 2022
Stage: Seed
Slides: 12
Sector: Software

Gensyn pitch deck PDF

The full Gensyn deck is embedded on this page and can be read slide by slide in the browser — no download or account required. Each slide is covered in the breakdown above.

What the Gensyn pitch deck was used for

This is Gensyn’s 12‑slide seed‑stage pitch deck from 2022, used to raise a $6.5M seed round for a decentralized L1 protocol for machine learning computation. The deck positions Gensyn as a tokenized marketplace and verification layer for global compute, tapping idle data‑center, consumer and miner hardware for AI workloads. It focuses on how cryptoeconomic incentives and new verification mechanisms can make untrusted, distributed compute usable for training machine learning models. The raise funded the launch and development of Gensyn’s decentralized compute network for AI model training.

Business model: Gensyn is building a decentralized layer-1 protocol and network that allows machine learning developers to access distributed compute resources (data centers, PCs, mobiles, miners) via a tokenized marketplace for AI training workloads.

Round: Seed.
Year: 2022.
Lead investor: Eden Block.
Investors: Eden Block (lead)., Galaxy Digital., Maven 11 Capital., CoinFund., Hypersphere., Zee Prime Capital., Founders from blockchain protocols (unspecified)., Pre‑seed investors mentioned around the seed: 7percent Ventures, Counterview Capital, Entrepreneur First, id4 Ventures.
Headquarters: London, United Kingdom.
Industry: Decentralized compute / Web3 infrastructure for AI / software.

Raised: $6.5M seed round (approximately £4.92M / €5.8M) closed in March 2022.

Total funding: Gensyn raised a $6.5M seed round in March 2022 and subsequently a $43M Series A in June 2023 led by a16z Crypto, giving total disclosed funding of at least $49.5M.

Use of funds as presented: Funding was allocated toward launching Gensyn’s decentralized computer network for training AI models and further development of the underlying protocol.

What happened after the Gensyn deck

Following the 2022 seed deck and seed round, Gensyn progressed to a much larger $43M Series A led by a16z Crypto in 2023, indicating investor confidence in its vision of a tokenized, verifiable global compute network for machine learning workloads.

Closed a $6.5M seed round in March 2022 to fund launch of its decentralized compute network for AI model training.
Subsequently raised a $43M Series A in June 2023 led by a16z Crypto, significantly expanding resources for protocol deve

What the Gensyn deck got right

Compelling articulation of a large, relevant compute inefficiency problem. The deck identifies a large, intuitive problem—wasted and untrusted global compute across data centers, PCs, mobile phones and miners—and directly ties it to AI’s growing demand for training compute, creating a strong problem‑solution fit for both crypto and AI investors.
Full‑stack technical architecture presented in a structured way. Gensyn’s core slide breaks down the protocol into work verification, market design, ex‑ante work estimation, privacy via functional encryption and parallel optimization, showing a full‑stack approach rather than a single‑feature or purely token‑driven idea.
Clear economic and developer‑centric value proposition. The deck frames the network as a tokenized market for compute that moves prices closer to the cost of processor operations while providing upfront cost estimation and foundation models for warm starts, which clearly speaks to cost‑sensitive AI developers.
Supplementary technical and token‑economic materials. Including references to a litepaper and a token valuation model signals that the team has deeper technical and economic documentation beyond the slides, reassuring sophisticated investors who want to scrutinize mechanism design and token economics.
Narrative links between infrastructure, environment and AGI. The market/opportunity slide connects decentralised compute to environmental benefits (greener use of GPU power away from PoW), dApp enablement and long‑term AGI progress, broadening the narrative from infrastructure to societal impact and future ecosystem.
Clever re‑use of miner hardware as a near‑term supply source. By highlighting the potential for EVM or other miners to search for yield on useful AI computation instead of PoW, the deck offers a concrete, timely source of supply and aligns web3 incentives with real‑world ML workloads.

What could have been stronger

Unsubstantiated scale of “wasted compute” problem. The problem slide highlights that over half of general compute supply is wasted and that data center hardware, personal computers, mobile phones and miners are underused, but it does not provide quantified evidence, specific datasets or references to support the magnitude of this claimed inefficiency, which may raise credibility questions for analytical investors.
Limited discussion of verification overhead and security trade‑offs. While the deck describes probabilistic and game‑theoretic work verification with challenge mechanisms, it does not, in the excerpted slides, explain failure modes, attack surfaces or how verification overhead impacts performance and latency for real‑world training jobs, leaving technical risk under‑explored from an investor perspective.
High‑level market and AGI claims without clear linkage to Gensyn economics. The market opportunity slide references a large market cap by 2031 and dramatic advances toward AGI, but without clearly tying these macro projections to Gensyn‑specific adoption scenarios, revenue models or token‑economic flows, which could make the valuation story feel aspirational rather than analytically grounded.
Architecture‑heavy narrative with minimal traction evidence. The excerpted slides emphasize distributed optimization, functional encryption and advanced protocol mechanics but do not mention any existing users, pilots, or benchmarks, suggesting the deck was heavily focused on vision and architecture rather than early traction or empirical performance data.
Go‑to‑market and ecosystem strategy only lightly sketched. The deck alludes to dApp enablement such as a decentralized DataRobot or similar higher‑layer services, yet the concrete go‑to‑market path—from protocol launch to developer adoption and integration into AI workflows—is not clearly laid out in the visible slides, which may leave execution strategy ambiguous.

How an investor would read this deck

What draws attention

Strength and novelty of Gensyn’s work‑verification scheme for ML computation, including probabilistic verification and game‑theoretic challenge mechanisms, because that underpins the security of using untrusted global compute for AI workloads.
Economic design and incentives in the tokenized compute marketplace—pricing, yield for miners and other providers, and how compute buyers are protected from fraud or low‑quality hardware.
Regulatory and reputational considerations of tapping hardware from data centers, consumers and miners, including any compliance posture for handling private datasets via the functional encryption layer.
Evidence of developer demand for such a network, including early testnets, pilots or partnerships with AI teams, given the deck’s heavy focus on technical architecture.
Comparison with centralized cloud providers and existing distributed compute solutions to understand where Gensyn has compelling cost, performance or trust advantages.

Risks that stand out

Technical risk that the verification and distributed optimization layers may not scale efficiently enough for large‑scale model training, making the network less competitive than centralized cloud options.
Adoption risk if AI teams are reluctant to move critical training workloads to a new, tokenized, untrusted compute network despite potential cost or scale benefits.
Regulatory and compliance risk around using globally distributed, potentially consumer‑grade or miner hardware for sensitive datasets, even with functional encryption, especially in heavily regulated sectors.
Token‑economic and market‑structure risk if incentives do not sustainably attract high‑quality compute providers or if speculative token dynamics distort pricing away from the underlying cost of processor operations.
Competitive risk from both hyperscale clouds adding lower‑cost spot and distributed compute offerings and other web3 compute networks pursuing similar AI workloads.

Questions this deck invites

How exactly does the probabilistic and game‑theoretic work‑verification mechanism function in production, and what are the quantified failure rates and performance overhead for typical ML training jobs?
What is the detailed token‑economic model—how are compute providers compensated, how are prices discovered, and how are buyers protected from dishonest or low‑quality providers?
Which categories of hardware (GPU, CPU, mobile, miner) does Gensyn realistically support today, and what benchmarks exist comparing cost, latency and throughput to major cloud providers?
How is privacy guaranteed in practice when running training over private datasets—what functional encryption schemes are used, and what are their limitations for large‑scale deep learning?
What concrete milestones (testnets, mainnet, paying users, ecosystem dApps) have been achieved since the 2022 seed round, and how do they map against the roadmap implied in the seed deck?

What founders can take from the Gensyn deck

A clearly articulated technical problem—here, the large amount of wasted and untrusted global compute—can anchor a complex protocol story if framed with specific asset classes (data centers, PCs, mobiles, miners) rather than abstract concepts.
Detailing core protocol components (work verification, market design, ex‑ante work estimation, privacy, distributed optimization) in a single, structured slide helps investors see that the team has thought through the full stack rather than just the token or the marketplace layer.
Connecting the protocol to major macro themes such as AI compute scarcity, environmental concerns and AGI progress can broaden the appeal of a deep‑tech infrastructure play beyond purely crypto‑native investors.
Emphasizing how unused hardware (including miners seeking yield post‑PoW) can be repurposed for AI workloads illustrates a concrete, narrative‑friendly demand and supply story in a seed‑stage deck.
Including references to a litepaper and token valuation model in the deck signals intellectual rigor and gives technically sophisticated investors and token specialists resources to go deeper outside the constraints of a short presentation.
Focusing on tokenized cost‑effective access to the “global compute stack” shows how a seed‑stage protocol can credibly pitch a large market without needing immediate revenue numbers, provided the mechanism design is explained sufficiently.

Gensyn pitch deck: common questions

What does Gensyn do, in simple terms?

Gensyn is a London‑based company building a decentralized layer‑1 protocol and marketplace where machine learning developers can run training jobs on globally distributed compute (data centers, PCs, mobiles, miners) with cryptoeconomic work verification.

Which fundraise was this pitch deck used for?

The deck was used for Gensyn’s $6.5M seed round closed in March 2022. The funding went toward launching its decentralized computer network for training AI models and further protocol development.

Who invested in Gensyn’s 2022 seed round?

The $6.5M seed round was led by Eden Block, with participation from Galaxy Digital, Maven 11 Capital, CoinFund, Hypersphere, Zee Prime Capital and founders from blockchain protocols; earlier pre‑seed investors included 7percent Ventures and Counterview Capital with Entrepreneur First and id4 Ventures.

How did Gensyn position itself in this seed pitch?

According to coverage of the round, the deck and associated materials framed Gensyn as applying tokens to distributed computing for AI developers by providing a verifiable, tokenized market for compute rather than just generic web3 infrastructure.

What happened after this seed deck and round?

Later disclosures show that after the seed round Gensyn went on to raise a $43M Series A in June 2023 led by a16z Crypto, indicating that the project progressed beyond the initial tokenized compute concept pitched in 2022. Those later developments are not part of the seed deck itself but matter for understanding outcomes.

Sources

Funding and outcome facts on this page were researched on 2026-08-22 from the pages below.

Gensyn pitch deck slides

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What each slide of the Gensyn pitch deck says

Slide 2

: 109,000 sillexploding ist re ia (1.61) Dera arse) : Ry Lie Os (am) The computational complexity of state of Ed aia the art Al systems is doubling every 3 & a SE enh = Derr-2 @.58) i [0 seat Lasge (340m) BERT Bass (120M) A $181bn opportunity by 2031 A 1 N 108 1.00 16,009 109.000 Ogensyn

Slide 3

. ° Supply is |KTe[spigle; users must pay up 5 or scale down 10 [7 quatcom x . [7] 2eppatin Seapdragon =» Over 60% of general compute is wasted B wi | resra s On [7] henle = 5 B Xavier soc Jacinto a1s => Asymptotic chip performance gains a TOA => Ballooning demand from pew sectors 2 => Geopolitics leading to stockpiling of chips Se => Centralised compute options are either: € Expensive oligopolists (e.g. AWS); or So 4 Limited in supply due to incentives Ogensyn

Slide 4

over @} of general compute supply is wasted . . Problem: all of this compute is untrusted Data center hardware is underused Personal computers sit idle Mobile phones have huge potential for overnight utilisation EIH1 miners will soon be searching for yield Ogensyn

Slide 5

Gensyn is the L1 protocol for machine learning computation Read more in our Litepaper mental ™ - Work verification: probabilistic method for approximate verifiability and deterministic, game-theoretic, challenge mechanisms for computational work verification = Market: tokenized market for compute with up-front cost estimation, foundation models for warm starts, and on-demand execution - Exante work estimation: neural network graph unravelling using intermediate representation with specified loop parameters to prevent the halting problem = Privacy: functional encryption layer allows computation over private datasets = Parallel optimisation: implementation of state-of-the-art distributed opti…

Slide 6

first of its =» Probabilistic method for approximate verifiability: using the A f irst of its metadata from gradient based optimisation methods to check that a model has been trained as requested sl scalable kind =» Deterministic challenge mechanisms: a graph based pinpoint de ep iE e arn in g protocol for contract arbitration between trainers and verifiers =» Game Theory: a Truebit-style incentive game that produces 7 1 1 honest participation from financially rational actors through Vv € r 1 £ 1 c at 1 on slashable deposits and reward bounties mechanism 7s 100 1,350% faste { . on yu = 5 Z 5 45 ]] Z 60 | vs the best C ig : ° % El | . . b 5 competition EE pag ed joes] Ogensyn

Slide 9

Decentralising the market for compute power generates a MR market cap by 2031 Read more in our Token Valuation Model Antificial General Intelligence (AGI) advancement: Dramatically advances ascent to AG! by fuelling the design and training of collaborative foundation models Cost effective: both high in scale/supply given access to the global compute stack and cost effective as the price of compute is moved closer to cost-price of processor operations Environmental: greener return on processor work by attracting GPU (or custom processor) power away from Pow scheme; as well as reaching consensus via PoS dApp enablement: facilitates higher layer functionality (e a decentralised DataRobot or Be…

Slide text above is read directly from the Gensyn deck PDF embedded on this page.