# Networking in the Dutch AI sector | llmnet.nl

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# Networking in the Dutch AI sector

By Ivo Donker — compiled with AI support (Claude & Gemini) · Last updated: 6 August 2026

The market for artificial intelligence and large language models in the Netherlands is characterized by a relatively compact ecosystem. Where anonymity dominates in larger international markets, the Dutch AI community is strongly interconnected. Professionals, researchers, and engineers regularly run into each other through various channels. This makes networking within this sector a particularly effective way to enrich your substantive knowledge and discover job openings before they are formally announced.

Many technical organizations and research groups choose to first consult their direct network when filling specialized positions. Building a lasting presence in the Dutch AI sector therefore provides access to opportunities that don't appear on regular job boards. Anyone who understands how the dynamics work and how valuable contacts are formed can work purposefully on their professional profile. This article analyzes the dynamics of networking in the Dutch AI sector, the types of gatherings, and the underlying mechanisms.

## The dynamics of the Dutch AI market

Networking yields more direct results within the field of artificial intelligence than in many more traditional sectors. One of the main reasons for this is the pace at which the state of the art changes. Because the skills required for roles evolve quickly, traditional recruitment profiles often fall short. Employers look for evidence of practical competence and affinity with the domain. Conversations with peers give both parties faster insight into each other's level and interests than a formal document.

In addition, the geographic and organizational scale in the Netherlands is manageable. The core of AI development is concentrated around a limited number of knowledge centers, university hubs, and technology clusters. A professional who actively participates in subject-matter gatherings builds up a recognizable profile with key figures in the field within a few months. Knowledge of the broader landscape helps put job openings into context; see also the analysis of [market developments in the Dutch AI labor market](https://vacatures.llmnet.nl/en/ai-arbeidsmarkt-nl).

## Types of gatherings and what to expect from them

Not every gathering serves the same purpose. It's useful to distinguish beforehand between different types of occasions in order to use your own time and energy efficiently.

### 1. Local meetups and specific user groups

Local meetups revolve around practical experience, specific software frameworks, or niche topics such as model optimization and data processing. The atmosphere is generally informal and the barrier to approaching speakers or organizers is low. The content ranges from short presentations to interactive demonstrations. Here you mainly find engineers and developers who work with the technology on a daily basis.

### 2. Trade conferences and symposia

Trade conferences offer a broader overview of the latest developments, academic insights, and applications in the industry. Attendees come not only to listen, but also to test trends and exchange experiences. Making contacts here happens during breaks, at booths, or during organized networking sessions. The audience is more diverse, ranging from researchers to managers and product owners.

### 3. Hackathons and open-source work sessions

At hackathons and collaboration days, delivering a working prototype or a contribution to a project is central. These are excellent environments to test practical collaboration. You learn how peers solve problems, how they communicate under pressure, and what quality level they maintain. Employers and teams often specifically look for talent here because the actual skills are directly visible.

### 4. University lectures and colloquia

Academic gatherings at universities offer in-depth theoretical insights and updates on ongoing research. The interaction is substantive and focused on research results, methodological justification, and technological constraints. This type of gathering is particularly valuable for anyone who wants to focus on roles at the intersection of R&D and science.

### 5. Closed working groups and expert panels

As professionals build up more experience, part of their network shifts to closed sounding-board groups and periodic meetings. Access to these groups usually happens through invitation from existing members. The discussions cover strategic issues, architecture decisions, and confidential insights.

Recognize the type of event: Commercial conferences often focus on selling licenses or services, with presentations sometimes taking on a sales character. Subject-matter gatherings are characterized by the presence of source code, detailed architecture diagrams, and critical questions about the results presented.

## Active contribution versus passive attendance

There is an essential difference between being physically present at an event and actively contributing to the community. Passively attending ten gatherings in the back row rarely produces lasting relationships. A single active contribution, on the other hand, can produce immediate recognition.

You can contribute actively at various levels:

- Fixing a bug or clarifying documentation: By submitting a pull request to an open-source project, you show that you understand the codebase and think along with the users. For anyone interested in recent developments in open source, the overview on [open-source LLM trends](https://nieuws.llmnet.nl/en/open-source-llm-trends) offers valuable context.

- Sharing an experience during a lightning talk: Many meetups offer room for short five- to ten-minute presentations. Discussing an obstacle you've run into and how you solved it makes a great conversation starter afterward.

- Making examples available: Sharing structured setups or worked-out templates helps others in the sector. Consulting [a public prompt library](https://community.llmnet.nl/en/prompt-bibliotheek) can, for example, offer insights into effectively structuring instructions for language models.

## Online interaction without overload

In addition to in-person gatherings, a significant part of the discussion takes place online. However, it's tempting to spend too much time on this without it adding substantive value. The goal of online networking is not to continuously generate posts, but to make your subject-matter work visible and participate in relevant discussions.

An efficient online strategy rests on three pillars:

- Documenting your own learning moments: Write short, clear articles or technical notes about specific problems you've found a solution for.

- Participating in issue trackers and discussion forums: Respond to substantive questions in open-source repositories when you know the exact cause of a problem.

- Targeted interaction: Respond substantively to posts from peers with additional facts, a different perspective, or a clarifying question.

For those at the start of their career who want to build up their knowledge step by step, [a structured learning path for beginners](https://leren.llmnet.nl/en/leerpad-beginners) can help to properly master the necessary basic concepts.

## The difference between asking questions and asking for favors

One of the most common mistakes when networking is confusing a substantive question with asking for a favor. When you approach an experienced specialist and ask them to put in a good word for you with an employer, you're asking for a favor. The recipient has to attach their own reputation to someone they barely know, if at all. The chance of a positive response is small in that case.

Asking a concrete, well-considered question about the field, on the other hand, works almost every time. Experts like talking about their field and share their insights when the question shows evidence of your own groundwork. Compare the approaches below:

Approach | 
Example of approach | 
Expected effect | 

Asking for a favor (Inefficient) | 
"Can you forward my resume to the recruiter at your company?" | 
Low response rate, causes friction because the recipient can't vouch for your qualities. | 

Asking a question (Effective) | 
"I saw your presentation on model quantization. How did you handle the latency losses when converting to 4-bit?" | 
High response rate, invites a substantive conversation based on shared interest. | 

## Strategies for candidates without senior experience

New to the job market, or switching from a different field? The feeling of lacking relevant work experience can create barriers. Still, this is less of an issue within the AI sector than in more traditional fields. The sector places a lot of value on the demonstrable ability to quickly master new technologies.

As a beginner, you bring the following to a networking conversation:

- A concrete project: Make sure you can explain your own project in detail. You should be able to explain why you made certain choices, which problems came up, and what the outcomes were. You can find insight into how to approach this in the guide on [building an AI portfolio](https://vacatures.llmnet.nl/en/ai-portfolio-bouwen).

- Targeted and prepared questions: Show that you follow recent developments and understand what the challenges are within a particular subdomain.

- Genuine interest: Actively listen to the other person's experiences. Ask further about how teams make choices between different model architectures or infrastructure.

## Networking for introverted professionals

Many engineers and analysts are not naturally inclined to seek out busy networking drinks. Networking, however, does not require an extroverted personality; it requires a structured approach that fits your own way of working.

For introverted professionals, the following tactics work best:

- Opt for smaller gatherings: Work sessions, lectures, and smaller roundtables offer a calmer environment than large-scale trade shows.

- Take on a functional role: Offer to help the meetup organization with registration, audio equipment, or managing the Q&A. A fixed role gives you a clear task and a natural reason to approach people.

- Prepare conversation topics: Think of two or three technical topics or recent paper publications beforehand that you have a well-founded opinion on. This prevents you from having to search for topics on the spot.

- Focus on one-on-one conversations: Don't seek out the largest group; instead, join someone who is also standing alone, or strike up a conversation at the demo tables.

## Effective follow-up after a gathering

The actual networking contact rarely happens during the gathering itself; the gathering is only the first introduction. The lasting relationship is built in the days and weeks that follow. A good follow-up is specific, brief, and picks up on what was discussed during the meeting.

Follow these steps when following up:

- Send a message within 48 hours: Refer to the specific conversation you had. Mention, for example, the specific topic or the paper that was cited.

- Add value: Consider including a link to a repository or article that relates to the conversation.

- Don't make direct demands: Don't immediately ask for a meeting or job interview. Let the contact rest until there's a natural reason to communicate again.

When a suitable position eventually becomes available at the contact's organization, making a connection is much easier. Extensive information about the further process can be found in the guide on [applying for an AI role](https://vacatures.llmnet.nl/en/solliciteren-ai-functie).

## Reciprocity: maintaining the network

A network that's only consulted when you're looking for a new employer or an assignment quickly loses its value. Lasting networks run on reciprocity: the principle that you give as much (or more) as you take.

Maintaining a network requires ongoing attention:

- Share insights you've gained: When you find a solution to a stubborn problem, document it and share the outcome with the people who struggle with similar issues.

- Refer people onward: If someone in your network is looking for specific expertise that you don't have, connect them with a peer who can offer the solution.

- Give feedback on the work of others: Take the time to give pull requests, articles, or demonstrations from peers constructive, technical comments.

By structurally contributing to knowledge sharing within the Dutch AI landscape, you become a recognizable and valued part of the community. This forms the most solid foundation for professional growth and a long-lasting career in the sector.

## Further reading

- [Overview of the Dutch AI labor market](https://vacatures.llmnet.nl/en/ai-arbeidsmarkt-nl)

- [Building an AI portfolio](https://vacatures.llmnet.nl/en/ai-portfolio-bouwen)

- [Applying for an AI role](https://vacatures.llmnet.nl/en/solliciteren-ai-functie)

- [Trends in open-source LLMs](https://nieuws.llmnet.nl/en/open-source-llm-trends)

- [Templates and prompt library](https://community.llmnet.nl/en/prompt-bibliotheek)

- [Learning path for beginners in AI](https://leren.llmnet.nl/en/leerpad-beginners)

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