The FD intercept is the linear time trend coefficient in levels:

$$y_{i,t+1}-y_{i,t}=\alpha_i-\alpha_i + \beta\cdot (x_{i,t+1}-x_{i,t})+\gamma \cdot (t+1-t)+\varepsilon_{it,t+1}-\varepsilon_{i,t}$$

You can add time to your FE spec to check if this is the culprit.

Also some good ideas in this question on why $FD \ne FE$.

The FD intercept is the linear time trend coefficient in levels:

$$y_{i,t+1}-y_{i,t}=\alpha_i-\alpha_i + \beta\cdot (x_{i,t+1}-x_{i,t})+\gamma \cdot (t+1-t)+\varepsilon_{i,t+1}-\varepsilon_{i,t}$$

You can add time to your FE spec to check if this is the culprit.

Also some good ideas in this question on why $FD \ne FE$.

The intercept is the linear time trend in levels:

$$y_{i,t+1}-y_{i,t}=\alpha_i-\alpha_i + \beta\cdot (x_{i,t+1}-x_{i,t})+\gamma \cdot (t+1-t)+\varepsilon_{it,t+1}-\varepsilon_{i,t}$$

You can add time to your FE spec to check if this is the culprit.

Also some good ideas in this question on why $FD \ne FE$.

The FD intercept is the linear time trend coefficient in levels:

$$y_{i,t+1}-y_{i,t}=\alpha_i-\alpha_i + \beta\cdot (x_{i,t+1}-x_{i,t})+\gamma \cdot (t+1-t)+\varepsilon_{it,t+1}-\varepsilon_{i,t}$$

You can add time to your FE spec to check if this is the culprit.

Also some good ideas in this question on why $FD \ne FE$.