I’ve been doing research lately on which publishing by lawyers carries the greatest authority with large language models such as ChatGPT, Gemini, and Claude.
I’ve found where a lawyer publishes matters as much as what they publish.
In doing my research, I came across an article by Dave Maney that makes an important point: in
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Big changes are coming to Ontario’s hiring landscape in hopes of making the job-hunting process more accessible and less
As the automotive industry heads into the final stretch of 2025, At the Wheel: An Automotive Roundup brings you the latest from Dykema’s Automotive, Mobility & Transportation group. In this Fall Edition, we catch up on trending topics, industry insights, and key takeaways from recent events, while giving a glimpse of what’s ahead.
What is law firm consolidation?
Law firm consolidation refers to the process of combining two or more law firms or legal service providers into a single entity. Typically, this process will consist of either a merger, in which two firms combine their operations into one, or an acquisition, in which one firm acquires and absorbs
I’ve been thinking about a story that I believe deserves more attention than it’s getting.
Robin AI, once positioned as a rising star in legal AI, has missed its funding round, cut a third of its staff, and landed on a distressed sale marketplace. The question isn’t whether this is unfortunate. It’s whether this is a harbinger. (Non-Billable)
Is Robin AI’s collapse a one-off execution failure, or the first visible crack in a legal tech AI bubble?
What happened at Robin AI
Robin AI launched in 2019 with a compelling premise: a “lawyer-in-the-loop” contract review system that combined large language models with proprietary contract data. The founding team brought credibility: lawyer Richard Robinson and machine-learning researcher James Clough building something at the intersection of both worlds. In early 2024, they raised $26 million in Series B funding. The marketing was aggressive: major enterprise clients, ambitious platform expansion across drafting and negotiation, claims of transformative efficiency gains.
By late 2025, the picture had changed dramatically. Internal reports suggested the company failed to secure another major funding round (targeting roughly $50 million), laid off about a third of its workforce, and quietly listed itself for sale on a distressed marketplace.
That trajectory, from high-profile funding to forced sale in under two years, warrants closer examination.
The red flags were there
Robin AI never publicly disclosed its Series B valuation. In a market where lofty valuations typically accompany large deals, that absence now looks less like discretion and more like avoidance. Without a clear number, it’s impossible to assess whether investor expectations matched operational reality or whether growth projections were ever grounded in achievable metrics.
More telling were the employee accounts. Reviews on Glassdoor described a culture of overwork, inadequate support, and marketing claims that outpaced product capability. One reviewer noted the company positioned itself as AI-driven while “in practice most of the work is handled manually by staff.” Another called it their “worst professional experience to date,” citing a “rule by fear” environment where junior team members shouldered contract reviews with minimal support.
These aren’t just grievances about workplace culture. They’re signals about the gap between what was being sold and what was being delivered.
What looks like a fluke
AI-related news dominates the business pages these days. Many companies increasingly are adapting their business processes to incorporate AI-related operations, and an growing number of companies are adjusting their business strategies to accommodate AI. While these changes present a host of opportunities, they also involve risks. A securities lawsuit recently filed against the integrated circuit (IC) design software company Synopsys shows how these kinds of AI-related risks can translate into securities litigation. In the complaint, the company is alleged to have understated the additional customization requirements that its customers’ AI-adapted operations would entail. A copy of the October 31, 2025, complaint can be found here.
Why is deepening client relationships more effective than chasing new leads?
Steve Fretzin explains that strengthening relationships with existing clients is far more effective than constantly pursuing new leads. Research suggests it can take six times more effort, money, and time to acquire new clients than to retain current ones. By focusing on existing clients,
I was recently at a charity benefit reception and dinner. An acquaintance (and I stress acquaintance) came up to me, took one look and out of the blue asked, “you’re retired, aren’t you”? For some reason that question was both galling and irritating. It took me a bit to figure why even though I’m sure
By Dr. Ken Broda-Bahm: The courtroom had all the usual elements: oak paneling, parties, a judge, a jury box, a flag. Only in the jury box, instead of live human jurors, there were three large digital monitors on which appeared the “jurors” for this case: ChatGPT (OpenAI), Grok (xAI), and Claude (Anthropic). This event, held
This article provides an excellent overview of the challenges insurers face in drafting policy language that keeps pace with the fast-evolving use of artificial intelligence. The author discusses how multiple insurers faced the challenge of defining what is “artificial intelligence” or “generative AI” by consulting with various industry sources. I asked ChatGPT how it would