Kenneth Carter

Repetition builds the redlining habit: spot the issue, flag the risk, mark up language outside your tolerance, and learn over time what gets accepted and rejected.

• Negotiation is different: it means discovering what both parties want and looking for trades.
• A redline to a limitation-of-liability clause, for example, is not an accept-or-reject decision; it is a prompt to look for something to exchange elsewhere in the deal.
• Without deliberate training, Carter warns, teams negotiate by instinct, gut feeling or inertia, letting the other side set the agenda.

Carter gives three worked trades: the customer wants a higher liability cap, so the vendor asks for a longer contract term; the customer wants monthly billing, so usage limits and overages are made monthly too; the customer wants termination for convenience, so the price goes up.

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A deal point is anything either side can give or take — pricing, shelf space, promotions, discounts, payment terms, indemnification.

• Once identified, each becomes a tool for resolving a demand the other side calls unacceptable, instead of a binary fight over wording.
• Carter notes that this takes time: teams must decide which deal points matter and how to value them.
• Most organisations, they say, skip the work and let their teams wing it.

Carter frames a liability-cap trade not as a risk for legal to accept or reject, but as an opportunity to drive growth by exchanging that risk for a longer deal term.

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The real cost, in their account, is not legal fees but deal friction: delayed revenue, lost upsells, burned reps, dead deals and unpriced risk.

• Their alternative is to treat the negotiator as a driver of revenue growth and deal velocity.
• Carter describes trying to change the incentive structure directly, with a risk-adjusted award for sales reps that tied the paper a deal was signed on to the credit a rep received.

Carter describes an award for the sales rep who earned the most contract value adjusted to contract risk, with multipliers: 1.25 if the customer signed on the company's own paper, 0.8 if the contract was negotiated, and 0.2 if the customer's paper was used.

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Before word processing, even changing a document was costly, let alone renegotiating it, so the market settled on contracts of adhesion built for a world that could not negotiate at scale.

• That left two bad options: a standardised template that protects one side but fits nobody, or a bespoke negotiation that fits the deal but takes weeks and costs a fortune.
• Carter argues that the industry avoided nuance, over-lawyered the boilerplate and underpriced the risk.
• Drafting contracts for business rather than conflict, they say, is where legal can create value.

Carter pairs the diagnosis with the claim that software can now model preferences and run trade-off scenarios in real time, making tailored negotiation practical rather than a weeks-long bespoke exercise.

Later posts shift the same argument from drafting practice to tooling: what Carter first describes as how contracts should be written becomes an argument about what contract software should do.

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Their slogan for the distinction is that most AI tools predict the next word while Ozeki predicts the next move.

• The posts describe a system that can work from counterparty paper alone, with no historical deal data, by running simulations and recommending opening positions.
• To assess these product claims, ask for the underlying simulations and measured outcomes.

Carter describes a customer negotiating from counterparty paper with zero historical deal data. The system ran eight simulated negotiations and used them to recommend opening positions; Carter reports that the customer closed faster and with more confidence than expected in an unfamiliar negotiation.

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Their argument is commercial rather than philosophical: a business cannot scale on that kind of variability. Carter names “Artificial Discretion” as the next big thing in Agentics, suggesting that deal teams need judgement applied consistently, not more fluent generation.

Carter illustrates the problem with a simple example: run the same prompt repeatedly and each run returns a different output, which they say makes the result untenable as a basis for scaling a business.

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Reporting a takeaway from a legal tech panel at SF Tech Week, they say the best use of AI is to produce an outcome better than a human could reach alone, and that dealmakers who generate outsized results for clients combine data, technology and human intuition. The test for the technology is commercial: did the lawyer's involvement get the client a better price on what was actually being bought or sold, not just a cleaner set of legal terms?

Carter poses the question directly: have you ever had your lawyer negotiate an agreement for you and, through haggling over legal terms, get you a better price on what you were buying or selling?

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The post explains what they mean by looking for trades rather than handling clauses one by one.

Carter names concrete deal points—price, shelf space, payment terms and indemnification—and gives three examples: a higher liability cap in exchange for a longer term, monthly billing paired with monthly usage limits and overages, and termination for convenience paired with a price increase. They also note that this takes work: teams must decide which terms matter and how to value them.

For brands planning a campaign

A contract-management software company could build a webinar or byline around mapping deal points before responding to redlines. The examples give the audience material to discuss, while the emphasis on valuing terms offers a practical exercise.

Start here to understand how they frame legal and sales as teams with goals that need alignment.

Carter recommends looking for trades, writing contracts for business rather than conflict, and aligning sales incentives with contract value and risk.

• They describe an award formula that changes the multiplier based on whether a deal used the company’s paper, was negotiated, or used the customer’s paper.
• That specific example makes their argument about aligning incentives more tangible.

For brands planning a campaign

For a thought-leadership series, this supports a session on reducing sales–legal friction, with the incentive formula as a discussion case. A brand should present it as Carter’s example, not as a universal compensation model.

Carter contrasts standardized templates that protect one side with custom negotiations that can take weeks and cost more.

• They argue that the historical expense of changing documents encouraged contracts of adhesion, then present Ozeki as a way to model preferences and trade-offs.
• The post links a historical explanation to their product’s proposed role.

For brands planning a campaign

This could anchor a byline on the shift from static templates to negotiated agreements. Keep the distinction clear between Carter’s explanation of the past and their claims about what Ozeki can do.

Carter says a customer used Ozeki with only counterparty paper and no historical deal data.

• They report that the system ran eight simulated negotiations and recommended opening positions.
• Ask for the contract, simulation details, and outcome measurements to assess the product narrative and its performance claims.

For brands planning a campaign

A webinar could use this as a case-study prompt: what inputs informed the simulations, how were opening positions evaluated, and what does “closed faster” mean? Ask for those details to help buyers assess the claim.

They define negotiation as discovering the intersection of mutual wants: a redline to a clause such as limitation of liability is not simply an accept-or-reject decision, but a prompt to find a trade elsewhere in the deal. Concrete trades include exchanging a higher liability cap for a longer contract term, tying monthly billing to monthly usage limits and overages, and pairing termination for convenience with a price increase.

Carter contrasts redlining—accepting or rejecting clauses one by one—with seeking trades that start from mutual wants.

Carter notes that trades only make sense when something valuable is exchanged, and that identifying and valuing deal points is hard, time-consuming work.

For brands planning a campaign

A contract-management or deal-tech brand can build a thought leadership series around a trade-based negotiation framework, using Carter's worked examples to teach deal teams to move from clause-by-clause redlining to mutual-value trades.

Examples: Source 1 · Source 2 · Source 3

Once mapped, each point becomes a tool for resolving a demand the other side calls 'unacceptable,' rather than a binary redline. They say the work is so time-consuming that most organizations skip it and let teams negotiate by instinct, gut feeling or inertia.

Carter lists concrete deal points and shows how each can become a trade.

Carter acknowledges that valuing deal points is difficult and that most organizations do not do it.

For brands planning a campaign

A brand can create educational resources to help deal teams inventory and value deal points, or position a product as a trade-off modeling workspace rather than a clause-automation tool.

Examples: Source 1 · Source 2 · Source 3 · Source 4

The real cost, they say, is deal friction: delayed revenue, lost upsells, burned reps, dead deals and unpriced risk. Treating negotiation as legal overhead makes dealmakers see the general counsel as the 'dreaded department of no.' Their alternative is to make the GC a driver of revenue growth and deal velocity.

Carter lays out a list of friction costs, then reframes negotiation as a driver of revenue growth.

For brands planning a campaign

A contract management or sales-tech brand can frame negotiation as revenue enablement, using Carter's friction list to make the business case for faster, trade-based contracting.

Examples: Source 1 · Source 2 · Source 3

The multipliers were 1.25 when the customer signed on the company's own paper, 0.8 for a negotiated contract and 0.2 when the customer's paper was used. The aim was to align sales and legal around where trades could accelerate outcomes.

Carter gives a specific compensation formula with three multipliers.

This is presented as Carter's own past practice, not a universal recommendation.

For brands planning a campaign

A brand can use this incentive design as a concrete case example in webinars or bylines about cross-functional deal operations and faster contracting.

Examples: Source 1 · Source 2 · Source 3

The result, they say, was over-lawyered boilerplate and underpriced risk.

Carter offers a historical explanation, then diagnoses two extremes.

For brands planning a campaign

A contract lifecycle or legal-tech brand can develop content on modern contract design that absorbs acceptable risk and captures the value a business cares about, using Carter's historical framing as the hook.

Examples: Source 1 · Source 2 · Source 3

One post describes a customer using Ozeki on counterparty paper with zero historical deal data; the system ran eight simulated negotiations and recommended opening positions.

• They sum this up as 'Most AI tools predict the next word.
• Ozeki predicts the next move.'

Carter describes a customer scenario involving simulated negotiations and opening-position recommendations.

For brands planning a campaign

A brand assessing Carter can expect a point of view on what AI should do in deal work—simulation and trade-off modeling rather than redline generation—and build thought leadership around that distinction.

Examples: Source 1 · Source 2 · Source 3 · Source 4

They argue a business cannot scale on that variability and name 'Artificial Discretion' as the next big thing in Agentics.

Carter frames the distinction as Artificial Insanity versus Artificial Discretion.

For brands planning a campaign

A brand could use this provocation to open a discussion about reliability, discretion, and governance in AI-assisted contracting.

Examples: Source 1

Dealmakers who fuse data, technology, and human intuition, in their view, will generate outsized results for their clients.

Carter reflects on a panel and offers a synthesis rather than a step-by-step method.

For brands planning a campaign

A brand can position Carter as a speaker or byline author on human-plus-AI negotiation, especially for audiences concerned about replacement narratives.

Examples: Source 1

The posts promote Ozeki’s approach to finding trade-offs between contract terms and invite readers to request a demo.

Original disclosure

I built Ozeki Technologies—not to mark up redlines, but to accelerate outcomes.

View example

Both writers argue against treating negotiation as a clause-by-clause drafting exercise and push readers to focus on what actually changes a deal outcome.

Schnell's focus is post-signature reality: how vendors operate after execution, how disputes are resolved through business conversations rather than courtrooms, and how to build future leverage into enterprise AI terms.

• Carter's focus is the trade itself: identifying deal points before a contract is signed and exchanging them to close.
• Schnell does not write about a negotiation product, and Carter does not write about dispute dynamics.

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Both explain legal technology to practitioners in plain, checklist form rather than as product marketing, and treat adoption — whether lawyers will actually use a tool — as what determines whether legal tech is worth anything.

Grubb's legal AI post is an evaluation guide for firms and in-house teams choosing and trialling tools, covering live demos, post-sales support, lawyer staffing and self-service.

• Carter's posts cover negotiation method and what a deal tool should model, not how to run a procurement process.
• Grubb also posts career and leadership reflections, a register they do not use.

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Both take a vendor-side view of the market and make their case through a single vivid example or contrast — a company's data sold at auction, or a prompt that gives a different answer every time.

Seader's subject is trust and control: data privacy, AI sovereignty, who owns the model, and what happens to client material.

• Carter's subject is the negotiation itself: modelling what each side wants and finding trades.
• Seader writes from inside a large legal publisher; Carter writes as the founder of a deal-negotiation product.

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They make the case that fewer than 0.1% of contract disputes are ever formally litigated, that vendors often keep operating according to default practices regardless of what the signed contract says, and that the practical goal is the contractual ability to be disruptive when something goes wrong.

• They also address AI clauses directly, walking through new AI terms appearing in enterprise agreements and how legal, contract, and procurement teams should navigate them.
• That gives a contract software vendor whose buyers draft or receive AI vendor terms a workable session subject.
• The material also shows a live session format already in use: an announced AI legal session on AI-related enterprise contract terms.

Schnell's framework teaches something different from Carter's.

• Carter asks what each party can trade; Schnell asks what leverage the contract gives a party if the relationship goes wrong after signing.
• A series built on Schnell would teach buyers to position for disputes, not reach mutual value, and would leave out the redline-to-trade reframe that defines Carter's approach and the three worked trades a sponsor can turn into a live exercise.
• For a worksheet-driven session, ask for a repeatable method to inventory deal points on both sides; the posts do not show one.

Examples: Source 1 · Source 2

Her post draws on hundreds of evaluation calls with law firms and in-house teams and reads as a buyer's checklist: insist on a live, real-time demo; understand post-sales support and turnaround before buying; check whether lawyers are on the product team; test how much lawyers can self-serve without vendor involvement; and look for tools that fit existing legal workflows. For a contract management vendor selling into in-house legal, that closely matches the purchase decision a thought leadership series usually aims to influence, and gives a byline a structure readers can apply in their next trial.

A series built on this angle teaches how to select software, not negotiate a contract.

• It would leave out Carter's core reframe of redlining versus trading and the worked trades that make a session executable; buyers would leave with evaluation criteria rather than a negotiation method.
• The posts contain no negotiation framework or contract-deal examples.
• If negotiation is the goal, ask for a speaker who can provide both; otherwise, the series subject would change rather than simply the speaker.

Examples: Source 1 · Source 2

The posts offer two concrete frames: the Spirit Airlines bankruptcy example, in which internal emails, messages, and operational records were sold and a buyer paid $10 million to train models on them; and AI sovereignty, which shifts the buying question from what a tool does today to who controls the engine and what happens if that changes.

• That fits a contract management vendor seeking to argue for a particular data-handling position.
• The sovereignty framing also gives a webinar a natural discussion question for buyers evaluating legal AI vendors.

Seader's material covers platform architecture, data policy, and model control, not negotiation.

• A series built on it answers a procurement and risk question rather than the negotiation-strategy question a sponsor needs answered.
• It also shifts the focus to vendor comparisons on data handling, making the sponsor's own practices part of the discussion.
• For trade-based teaching, ask for a negotiation framework, a deal-point method, and worked contract examples; the posts show none.

Examples: Source 1 · Source 2