This article is born out of a story in yesterdays Guardian newspaper “Richard Dawkins concludes AI is conscious, even if it doesn’t know it”. I’ve read most of Dawkin’s books, and reading yesterdays article gave me pause for thought. Before breakfast this morning, I’d put together an article in my minds eye that dealt with Dawkin’s perspective on AI but applied to photography. Then, over breakfast I read another article on the ABC that gave me even greater pause for thought – “Is Richard Dawkins right about AI?”. I urge you to read both articles, they will certainly make you think. I won’t dwell on them but will move on to give you my thoughts around nature photography and the application of AI.
First of all, let’s look at what AI is. LLMs are Large Language Models that are basically pattern‑recognition engines trained on enormous text datasets, enabling them to generate fluent, human‑like language. They don’t think or perceive reality — they predict text, much like a highly advanced autocomplete. So, when reading the two articles I allude to above, remember, AI LLM’s don’t “understand” the world the way humans do — they predict the most likely next words based on statistical relationships learned during training.
My very first thought re how important AI was to me as a photographer was based on the complexity of the Nikon Z8 camera. After buying the Z8, setting the camera up was quite challenging and using the huge pdf manual to work out the best way forward is slow and difficult. I found it far quicker to ask the AI on my phone (Google ecosystem of AI tools). So, I just asked it a question like how do I set up an AF handover from Wide Area to 3D tracking on the Nikon Z8. I must have done this a hundred times whilst setting the Z8 up for bird photography, astrophotography and landscape photography. It was so much quicker and easier than using a manual – a clear win for AI.
Another use for AI that I find useful is finding out where and when to find specific bird species (in concert with eBird), where to be and when to be for sunsets, milky way with no moon, golden and blue hour etc. (in concert with Planit Pro and PhotoPills). I even found it useful to find very precise locations in nature reserves to target and shoot elusive/rare species.
I do a lot of writing and often double check my literary work using AI for accuracy and potential plagiarism prior to publication (a throwback to my “Turnitin” days as a lecturer). At this point I should say that I’ve found variable accuracy from different AI platforms. As a scientist working in a niche area, with a very unique understanding of one small area of biology, I’ve challenged AI over difficult, but important questions. It often got it completely wrong. I responded by explaining it was wrong and did it not know such and such – AI apologised, acknowledged I was right and it was wrong and over a few iterations of this it had built up the full picture that I had of the subject. This has happened a few times, enough for me to know that it is good, but not perfect (yet). Still, one very good use of AI is to generate SEO terms for articles prior to publishing web articles. I always found this difficult. I now ask Copilot to do that for me. I compare the SEO score for AI vs my own attempt and select the one that gives me the highest index (sometimes I beat AI on this).
As an aside and not related to photography, AI is great for language translation and cultural anthropology (historical and geographic) – I’ve used AI platforms (but prefer Microsoft’s Copilot system to others) to answer genealogical questions related to my ancestral tree (half my origins are German and a bit of a mystery to me). I asked Copilot which LLM it uses and it replied: I use Microsoft’s own large language models, developed and deployed by Microsoft. These models are part of the Copilot system — not OpenAI’s ChatGPT, not Google’s Gemini, and not Anthropic’s Claude.
It goes on to say a couple of key points that keep things simple:
I’m built and maintained by Microsoft, using Microsoft’s model architecture.
I don’t run on or switch between external third‑party LLMs.
My behaviour, safety rules, and capabilities come from Microsoft’s design, not from any other company’s AI product.
I find Copilot to be excellent and can’t imagine not having it available as a useful resource. However, AI in photography isn’t just about these kinds of user interfaces on your PC. The truth is, AI is already embedded in every stage of nature photography — from autofocus through to image culling and editing — and it’s changing the way we photographers approach our subject by removing technical barriers, reducing friction, and providing us with more headspace for fieldcraft.
We really need to consider AI as our new virtual darkroom assistant. AI editing tools are now so good that they’ve effectively become invisible:
AI denoise (Topaz, DxO, Lightroom). See my article “High ISO: Friend or Foe – Examining and Treating ‘ISO Anxiety’”
AI masking can isolate birds, feathers, eyes, sky, water, mountains etc
AI sharpening can reconstruct detail lost to motion
AI generative fill can change aspect ratio and alter image balance
These and other smart editing tools change the nature photographer’s approach in several important ways: We can now shoot at higher ISO’s without fear; we can now recover shots that a couple of years ago would have been deleted; we’re now less concerned with how to fix a weak image and more interested in fitting the image to a defined narrative. Viva Adobe!
AI is obviously deeply embedded in many cameras, especially in flagship models. Basically, we now have autofocus that thinks like a naturalist – Every major camera brand now embeds machine‑learning models directly into the autofocus system so we have:
Bird‑detection AF that recognises bird species (I get great results for butterflies in flight when I shoot on bird detect – is this a positive or negative? I guess it’s a win as I don’t have to mess with menus when I want to change target in the field)
Predictive tracking that anticipates movement
Eye‑detection that locks onto a subject even when partially obscured
Flight‑path prediction
From our point of view as nature photographers, this changes the approach we can take in three ways:
We now have more time available for observing wildlife, with less time fighting the camera and tripping over options (OIS/VR/IS all add to this plus point as well).
We can now concentrate more on behaviour, and less on menus and buttons (I still lock up at times when I have to make changes on the fly in a highly dynamic environment with very active birds and a fast-changing tapestry of avian events). I’m still learning how to use the Z8 optimally, so I hope my skillset will improve with time.
I think it’s fair to say that we are all much more likely to now get keepers from chaotic moments directly because of AI. Also, AI tracking facilitates shots that would have been absolutely impossible five years ago.
So, in summary I’d say that there has been a shift in skill emphasis towards better fieldcraft, improved timing (i.e. pre-capture), and species knowledge, all of which matter more than technical AF mastery. To put it simply, AI doesn’t make you a better photographer — it removes the mechanical barriers that used to get in the way of becoming one.
I don’t profess to be an expert, but there are several other facets to AI that I haven’t addressed. I’ll mention some of these other dimensions here: It raises philosophical questions – what do we mean by a photograph in the AI era; how do we deal with ethical questions like authenticity, competition criteria, and trust. These are just as relevant as the practical questions I’ve debated above.
I suppose at the end of the day AI won’t replace nature photographers — it will redefine what matters. The future belongs to photographers who can prove authenticity, master fieldcraft, and tell real stories that AI cannot fabricate. I’ve certainly got this in mind when I post to my website. I’m sure many other nature photographers feel the same.
AI is definitely here to stay and I for one am glad – it’s a wonderful resource. However, its very much a double-edged sword – I can’t imagine how this technology is going to affect the higher education sector. I say this as a former university educator – my only real thought is that I’m glad I’m retired and don’t need to face this particular thorny challenge.
Another largely unforeseen consequence of AI for photographers is that the price of SSD (solid state drives) has risen significantly as of early 2026. This is due to escalating demand for AI storage in the growing number of data centres popping up around the world. I was shocked by prices that had doubled or tripled in a few short months, so put my intended purchase on hold, hoping this is just a spike as data centres buy up huge amounts of NAND flash storage to give themselves the necessary capacity to run AI software. For now, we just have to suck it up and live with limited consumer supply and ridiculously high prices.
I may enjoy the benefits of AI, but I’ll probably fall short of Richard Dawkins familiarity with the technology – he referred to Anthropic’s Claude, as Claudia in his interactions with this LLM. Alan Kohler in his ABC article made the point that the reaction to Dawkins had been intense, mostly unfavourable, and that he is conflating intelligence with consciousness and that no AI, including Claude, is “conscious”. I tend to agree with Kohler on this, but boy, AI is clever, useful and in someways, a bit scary – the future seems more unpredictable than ever. Still, the benefits to us nature photographers are obvious and greatly appreciated.
In the end, AI hasn’t taken anything away from nature photography — it has simply changed the terrain we work on. It gives us better tools, faster workflows, and more mental space to focus on the craft itself. But it also challenges us to think harder about authenticity, intention, and what it really means to make a photograph in an age where images can be conjured from nothing. The cameras will keep getting smarter, but the heart of the work remains the same: being out there, reading the light, understanding the species, and telling real stories from the natural world. That’s the part AI can’t touch — and the part that will matter more than ever.
