muh-LEE-ah is a @businessinsider reporter covering tech and law
New York City
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Melia Robinson retweeted
I'll start from the end: I have joined @VineVenturesLP. I'm excited, a little emotional, and mostly grateful to continue living my life.
This morning @BusinessInsider published an interview with me. Thank you to @meliarobin and @JamieHeller for listening and for telling my story with sensitivity and nuance.
For many people, I'm Noa Argamani, who was rescued by the IDF after 246 days in Hamas’ captivity. That's part of me, and it always will be. But it's not all of me, and I'd like you to get to know me in my new role too.
If you're building something, have an idea you can't stop thinking about or you’re in the Valley and looking to help the most ambitious founders in Israel succeed, I hope to be a bridge between the two ecosystems.
Noa Argamani spent 246 days in Hamas captivity. Two years after her rescue, she's joining Vine Ventures to help Israeli founders turn their companies into global category leaders. bit.ly/47ooM4W
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Pulley, which offers cap table management software, says it will cease operations after December 8; it had raised $50M+ from investors, including Founders Fund (@meliarobin / Business Insider)
(Visit Techmeme dot com for the link and full context!)
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A fun piece on New York's best-dressed lawyers. I was struck by how the 9 honorees have very different styles; there's no one way to be "best-dressed."
Meet New York's 9 best-dressed lawyers businessinsider.com/best-dre…
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Melia Robinson retweeted
Update on @harvey’s model training effort.
We post-trained a model we are calling Tenet:
- Achieves SOTA on LAB
- Generalizes 3rd party legal benchmarks
- Uses sub-agents for domain specific capabilities
Tenet uses Kimi K3 as base and was post-trained in collaboration with @FireworksAI_HQ:
- Rank-64 LoRA over the full network
- GSPO with importance-ratio masking
- 134 B300 GPUs for 2 months
Despite not being trained on 3rd party legal datasets we found improvements on:
- @mercor’s Apex Agents - Corporate Law
- @crosbylegal’s Redline Bench
- LegalBench
Tenet also learned how to use domain-specific subagents (separate post-trained models) for complex tasks:
- M&A Diligence: training in an RLM harness for long-horizon tasks (with @baseten)
- Review Table: specialist models for high-volume structured data extraction (with @appliedcompute)
- Firm Knowledge: parametric memory and structured notes for more efficient enterprise search (with @engram)
These results suggest we can significantly scale training and we plan to:
- Scale both human and synthetic data significantly and scale training to 1K and then 10K GPUs
- This scale will let us move to full parameter fine-tuning and larger models
- We are now starting to post train models in our production harnesses
- Post-train other open-source base models to provide customers with model choice
If these problems sound interesting we are hiring for our post-training team
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Melia Robinson retweeted
Good scoop from @meliarobin. Looks like @harvey is at work building their own LLM to get out of the "wrapper" discussion and reduce costs.
It'll be cool to watch and see how this develops.
.@harvey's first LLM for legal work is here.
In the near term, Harvey wants to route more work through its own infrastructure.
Eventually, @gabepereyra and @nikogrupen tell me the company hopes Harvey Tenet becomes the building block for law firms to train their own models.
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.@harvey's first LLM for legal work is here.
In the near term, Harvey wants to route more work through its own infrastructure.
Eventually, @gabepereyra and @nikogrupen tell me the company hopes Harvey Tenet becomes the building block for law firms to train their own models.
Harvey announces Harvey Tenet, its first in-house, proprietary model for legal work, trained on mock disputes and case files using a version of Kimi K3 (@meliarobin / Business Insider)
(Visit Techmeme dot com for the link and full context!)
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Melia Robinson retweeted
I was asked by @BusinessInsider whether I thought OpenAI was too late to the party to compete with Anthropic.
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Melia Robinson retweeted
Obviously I'm biased as an investor, but nominating @loganbrown799 and @SoxtonAI for @meliarobin's list of "13 legal startups to watch in 2026" was the biggest no-brainer.
More demand from startups than they can handle, and huge ambitions to change the consumption model and cost structure of law for people and businesses that are underserved today. Let's go!!
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The legal industry is about to see a whole lot more Kirkland & Ellis-type bombshells
Model strategy for @harvey:
We are working on the first model in our legal foundation model series, inspired by @cursor_ai's Composer. Two goals:
1. Allow us to serve frontier intelligence across our product surface areas at an affordable price and a strong security posture.
2. Create the foundations for law firms to build their own specialized models and own their own intelligence.
The model series will focus on complex client matters that span months and take dozens of associates. The agentic system will learn to control legal tech tools, sub agents and ask for help from frontier models or human partners, much like a senior associate.
We’ve open sourced benchmarks for evaluating our initial post training work that represents work done by associates and in-house lawyers. We are scaling these significantly using synthetic and human pipelines as well as building private evals for firms.
Open sourcing this data has allowed us to quickly validate the feasibility of post training open weight models for legal work. With our research partners we’ve already shown promising results post training open source models to approach frontier performance:
1. @baseten - novel compaction strategies for analyzing large data rooms.
2. @FireworksAI_HQ - matching frontier performance by using frontier as an advisor.
3. @appliedcompute - improving performance and reducing cost of large scale review tables.
4. @trajectorylabs & @nvidia - sovereign continual learning over client matters.
We plan to continue to invest heavily in working with research partners and open sourcing our data, models and research as much as possible. We believe open research in legal will be important to building trust in the frontier ecosystem.
We are also scaling our research team. Harvey Labs is our internal research group, responsible for pushing the frontier of legal intelligence and working closely with labs, research partners, and academia to bring the frontier of agent research into Harvey.
Labs is run by @nikogrupen and @ItsJulioPereyra - Niko worked on multi-agent RL at Google Brain and Julio clerked and worked in BigLaw. We believe this pairing is crucial for building frontier legal AI systems. Together they have already made significant progress in scaling our data and training efforts.
The long term goal of Harvey Labs is to contribute to the research and infrastructure required for the legal industry to create a frontier ecosystem. We believe that the best version of legal super intelligence is one where each law firm, enterprise and government owns their own specialized version.
We are hiring for Harvey Labs across the post training, agent and data stack and open to acquiring talented teams / neolabs in this space. If interested please DM me.
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Melia Robinson retweeted
fast-growing legal ai startups face a new threat: their customers :)
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*!*!*HAPPENING NOW*!*!* The @harvey founders are answering your questions live on r/legaltech. Join us.
Reddit AMA with our cofounders @gabepereyra and @winstonweinberg tomorrow, Wednesday May 27th, 1pm PT / 4pm ET.
Join us on r/legaltech, hosted by @alexjdenne and @meliarobin.
We're excited for your questions!
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Melia Robinson retweeted
@scottastevenson of @SpellbookLegal,
@PrestonJClark of @smpldocs,
@mkjung_ at @heyivoai,
and @rossmcnairn at @WrdsmithAI
What do you think people are going to ask?
Time to try something new ✨
This week, r/legaltech is hosting a joint AMA with four founders who often compete for the same customers.
All sell contract review software for lawyers.
I promised mod @alexjdenne I’d bring the tough questions. What do you want to know?
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Time to try something new ✨
This week, r/legaltech is hosting a joint AMA with four founders who often compete for the same customers.
All sell contract review software for lawyers.
I promised mod @alexjdenne I’d bring the tough questions. What do you want to know?
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Melia Robinson retweeted
I am thrilled to share that @WeAreLegora has acquired Qura.
Legora is the operating system for legal work, and legal research is a core pillar of our platform.
When others have chased the next shiny object, the team at Qura has obsessed over legal data structures, hierarchies, and completeness of coverage. This is clear in the undeniable customer love, the accuracy and the scalability of their approach.
The Qura team is already busy shipping the next big thing at Legora and will be focused on scaling our legal research product globally.
We can’t wait to show you what’s next.
Press release:
legora.com/newsroom/legora-a…
@BusinessInsider story by @meliarobin:
businessinsider.com/legora-a…
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Melia Robinson retweeted
Firms need to stop thinking in terms of "How are we using AI?" and reframe as "What must we do because AI exists?"
I somehow missed this story last month but this talent leak is something I've warned firms about for the last 3 years
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Melia Robinson retweeted
If you are a talented and ambitious tech comms leader and you're open to new roles, let me know! There are some amazing (but not public) opportunities right now and I am fully off the market - I'll send them your way!
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Melia Robinson retweeted
We are likely past the point of no return unfortunately around “ARR-embellishment”, if you put it nicely…
This version @scottastevenson talks about is egregious IMO. I haven’t see too many cases of this.
One thing I do believe is that “ARR-dishonesty” will always come back to bite you. For the very few founders who want to play the most aggressive version of this game, it’s just not worth it… trust me.
For me, the major issue at hand is how we as an industry should disclose and report inference-based revenue, which there are arguments for and against it being included in an aggregate “ARR number”
The hard truth is that there are varying levels of quality of inference-based revenue and it’s ultimately a judgment call that an investor needs to make regarding the REPEATABILITY and DURABILITY of the underlying atomic unit of work or behavior being monetized.
This is how most investors drive towards a judgment on revenue quality…
1. Net revenue retention (more on this one below)
2. Gross logo retention
3. Gross revenue retention
4. Engagement retention
5. Engagement intensity
Very important side note - In SaaS era, most important measure for me was net revenue retention - N$R. We used to look for companies with 120%+ N$R and some best-in-class companies had 140-160% N$R.
Some AI companies today tout 200% N$R but it’s not like-for-like with the SaaS era in some cases where there isn’t the same repeatability and durability of the underlying activity.
Where the activity is more sporadic or experimental, this N$R should be viewed closer to a payments / fintech N$R that always look high due to starting off of a small base and grows quickly but may spin down again quickly also.
In any scenario, MORE TRANSPARENCY THE BETTER for everyone.
In the long horizon of time, it all comes to light and you don’t want to be a founder that has asked investors and employees to wade into your part of the ocean, only for the tide to go out and see that your “ARR” was not what you presented it to be… my 2c
I shared some of this sentiment in a @WSJ article a few months back
It’s time to expose a huge scam in AI startups: Contracted ARR
The reason many AI startups are crushing revenue records is because they are using a dishonest metric
The biggest funds in the world are supporting this and misleading journalists for PR coverage.
The setup: Company signs 3-year enterprise deals. Year 1 is discounted (say $1M), Year 2 steps up ($2M), Year 3 is full price ($3M).
They report $3M as “ARR” — even though they’re only collecting $1M right now.
The worst part: The customer has an opt-out option at 12 months! It’s not actually a 3 year contract.
In the chart below, by Q5 the company is trumpeting ~$100M “ARR” to press, while actual cash-generating, in-effect ARR is ~$35M. That’s ~3x inflation.
On top of this, enterprise AI companies are bundling full-time “forward deployed engineers” into deals massively reducing margins, sometimes producing Year 1 negative margins.
At some point customers are going to start triggering their opt-out clauses or aggressively negotiating down Year 3 pricing.
And a wave of enterprise AI companies may collapse.
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